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Visiting Artist: Ronan Peterson
Altered States: Unique Forms and Intense Surfaces
Saturday, March 4, 10am – 4 pm and Sunday, March 5, 10 am – 4 pm
Tuition: $300.00, members $150.00
This 2 day workshop will look at using thrown and altered parts to construct unique vessels and approaches to help enliven and personalize ceramic surfaces. Initial demonstrations will include throwing and altering parts for forms such as vases, boxes, and teapots. We will look at assembling these parts and adding further handbuilt elements to complete our ceramic forms. The remainder of the workshop will be devoted to slipping, carving, and resist techniques to develop lively and intense surfaces intended for, but not exclusive to, firing in electric kilns. If time allows, glaze approaches will be demonstrated, or at least discussed. Participants will receive a packet of slip, glaze, and terra sigalatta recipes to reference after the workshop.
BIO: Ronan Kyle Peterson grew up in Poplar, NC, a small community deep in the mountains of western North Carolina. He attended the University of North Carolina at Chapel Hill and received a Bachelor of the Arts degree in Anthropology, with a minor in Folklore. His interest in Folklore led him to John C. Campbell Folk School in Brasstown, NC, where he began taking classes in ceramics and other media. After working for two years with two potters in the area of Asheville, NC, he attended Penland School of Crafts. Initially, he intended to stay for a two month Concentration in Wood and Soda Fired Pottery with MacKenzie Smith, but two months turned into four years.
Currently, Ronan maintains Nine Toes Pottery, a ceramics studio in Chapel Hill, NC, which produces highly decorative and functional earthenware vessels. His work is drawn from processes of growth and decay in the natural world and translated into a ceramic comic book interpretation of both real and imagined phenomena. His ceramic vessels have been shown in local and national exhibitions and has been featured in both Ceramics Monthly and Clay Times, and the books 500 Bowls and 500 Plates and Chargers, which includes an image of his plates on the back cover.
Also an educator, Ronan taught for 10 years at local craft centers and ceramics studios in the Chapel Hill/Raleigh/Durham area of NC, and has recently taught workshops throughout the Southeastern United States, including at Penland School of Crafts and Arrowmont School of Arts and Crafts.
See Ronan's blog at: http://ninetoespottery.blogspot.com/


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- © 2014 American Society of Plant Biologists. All Rights Reserved.
Increased adoption of the systems approach to biological research has focused attention on the use of quantitative models of biological objects. This includes a need for realistic three-dimensional (3D) representations of plant shoots for quantification and modeling. Previous limitations in single-view or multiple-view stereo algorithms have led to a reliance on volumetric methods or expensive hardware to record plant structure. We present a fully automatic approach to image-based 3D plant reconstruction that can be achieved using a single low-cost camera. The reconstructed plants are represented as a series of small planar sections that together model the more complex architecture of the leaf surfaces. The boundary of each leaf patch is refined using the level-set method, optimizing the model based on image information, curvature constraints, and the position of neighboring surfaces. The reconstruction process makes few assumptions about the nature of the plant material being reconstructed and, as such, is applicable to a wide variety of plant species and topologies and can be extended to canopy-scale imaging. We demonstrate the effectiveness of our approach on data sets of wheat (Triticum aestivum) and rice (Oryza sativa) plants as well as a unique virtual data set that allows us to compute quantitative measures of reconstruction accuracy. The output is a 3D mesh structure that is suitable for modeling applications in a format that can be imported in the majority of 3D graphics and software packages.
In recent years, there has been a surge of interest in the construction of geometrically accurate models of plants. Increased adoption of the systems approach to biological research has focused attention on the use of quantitative models of biological objects and processes to both make and test hypotheses. Applications of the systems approach are diverse and include the study of canopy photosynthesis (Watanabe et al., 2005; Song et al., 2013). The work reported here was motivated by a need for realistic three-dimensional (3D) representations of plant shoots for photosynthesis modeling. Canopy structure or architecture is an important codeterminant of the maximum productivity of crops and theoretically can influence canopy photosynthesis efficiency (Zhu et al., 2004; Long et al., 2006; Reynolds et al., 2012). For example, many high-yielding cereal varieties tend to have upright leaves, which raises the optimal leaf area index and reduces the proportion of leaves in a light-saturated condition (Murchie and Reynolds, 2012). However, there is still considerable variation in plant canopy architecture, and it is currently unclear whether many existing architectures (which have been influenced by breeding for specific traits) offer optimal arrangements for photosynthesis (Long et al., 2006; Murchie et al., 2009). Therefore, there is a need for rapid, automated, user-accessible techniques for accurately measuring the 3D architecture of complex crop canopies.
Existing plant modeling approaches can be broadly classified as either rule based or image based (Quan et al., 2006). Rule-based approaches generate model plants based on rules or grammars with specified structure. These rules, and hence the form and parameters of the models produced, are often derived from measurements of real plants (Watanabe et al., 2005; Alarcon and Sassenrath, 2011). The resulting virtual plants can model different phenotypes, plant response to various growing conditions and stresses, and when based on real-world data will be reasonably accurate. However, the data acquisition process is often extremely time consuming and is usually tailored to a particular species. In many cases, only a small set of varieties can be described, due to the manual measurements required to parameterize the model. It is also difficult to incorporate additional data into a rule-based model after it has been created. For example, modeling the effect of environmental factors on plant development will require rules to be modified and additional measures to be taken, again a time-consuming process. Critically, while models created in this way may capture the important characteristics of a given species, they do not necessarily describe any specific, individual plant. This is important when considering the variation in plant structure that can occur in response to environment and features that are associated with communities of plants, including crop canopies. This can be seen in the relationship between planting density and tillering rate, for example (Zhong et al., 2002).
Image-based approaches attempt to directly model a given object by extracting the necessary information from one or more images of that object. The approach relies on techniques developed in the wider field of computer vision and is computationally challenging. The continued increase in available computing power, however, means that this can be done efficiently, and in some cases fully automatically. Image-based approaches to plant modeling are particularly attractive, as, in addition to supporting systems biology, they provide a route to plant phenotyping (Houle et al., 2010; White et al., 2012). 3D geometrical models contain the information needed to compute summary plant traits, such as total leaf area, mean leaf angle, etc. These underpin both plant breeding programs and attempts to understand the relationship between genotype, phenotype, and environment, regardless of the scientific approach taken.
Plants, however, provide a particularly challenging image-based modeling target, including large amounts of self-occlusion (leaves obscuring one another) and many smaller surfaces that appear similar. Depending on the plant species, leaves can lack the texture necessary to perform robust feature matching, either to separate leaves from one another or to locate specific leaves across multiple views. To overcome this challenge, where image-based modeling approaches are successful, they have often involved user interaction to guide the process (Quan et al., 2006).
It is possible to further categorize image-based approaches into those that are top down, beginning with an existing, generic plant model that is fitted to the received image data, and those that are bottom up, creating a description of the plant by examining the contents of the images. Top-down approaches attempt to simplify the model construction problem by instead solving a model refinement problem. An existing model is adjusted to fit the image data, so that the new plant representation is consistent with what is observed. Quan et al. (2006) take this approach, first obtaining an ideal leaf model from a single leaf and then fitting it to all other (user-segmented) leaves in the scene. By adapting an existing model, topological inconsistency (such as the self-intersection of leaf surfaces) is avoided, but this comes at the expense of generality. This approach can be extended in principle to other plant species, but where the geometry of a plant or leaf differs greatly from the expected model, the suitability of this approach is unclear. This is also significant where a plant model has been generated with highly specific real-world data, such as the work of Watanabe et al. (2005).
Bottom-up methods begin with one or more images and reconstruct a plant model based only on the observed pixel data. Two broad approaches exist, both requiring a set of images captured from different, but known, viewpoints. Silhouette-based methods (Clark et al., 2011; Kumar et al., 2012) segment each image to identify the boundary of the object of interest. Each silhouette is effectively projected into the viewed environment, identifying a region of 3D space that could be occupied by the plant. These regions are combined to determine the maximum possible object size that is consistent with the images presented to the algorithm. In many cases, where the number of input images is high, the resulting model will be a good approximation of the true plant structure. However, as the scene becomes increasingly complex, for example with the addition of more leaves in an older plant, the discrepancy between the true object and the model will increase. This problem becomes more pronounced when extending these techniques to very complex scenes, such as plant canopies, where its effectiveness is limited.
Correspondence-based methods identify features of interest (i.e. recognizable patches of pixels in the image), independently in each of a set of images, and then match those features between views. If the image features associated with a particular plant feature (e.g. the tip of a leaf) can be identified in multiple images taken from different viewpoints, knowledge of the cameras' positions and orientations allow its 3D location to be computed. The result is a point cloud representation, a set of (x, y, z) coordinates approximating the plant's surface (Quan et al., 2006; Omasa et al., 2007; Alenya et al., 2011). Point cloud data can be obtained directly from special-purpose hardware devices such as light detection and ranging laser (LIDAR; Omasa et al., 2007) and time-of-flight laser (Alenya et al., 2011) scanners. These systems use a large number of distance samples to measure the depth of the scene relative to the capture location. The equipment involved, however, can be expensive and often places restrictions on the environments in which it can operate.
Image-based modeling algorithms are widely applicable and require only easily accessible and affordable cameras. Their generality, however, can become a hindrance, as the challenging nature of plant topology may require additional assumptions to be made as the reconstruction proceeds. The representations they produce may also be unsuitable for direct use in some situations. The volumetric data structures produced by silhouette-based methods, for example, are static: the size and positions of the voxels are defined early in the process and are difficult to change. While measurements of height and volume are easily made from volumetric descriptions, estimating motion (e.g. of leaves moving in the breeze) is extremely difficult. Volume-based descriptions contain an implicit surface around that volume that can be measured; however, these surfaces are less suitable for ray tracing and modeling, as each leaf is represented as numerous surfaces (inside, outside, and sides of the leaf volume). Similarly, point clouds can be used to calculate the density and distribution of plant material but cannot immediately be used to host models of photosynthetic activity; for this, a surface-based representation is required.
This article describes a fully automatic, bottom-up approach to image-based 3D plant reconstruction that is applicable to a wide variety of plant species and topologies. The method is accurate, providing a true representation of the original plant, and produces data in a form that can support both trait measurements and modeling techniques such as forward ray tracing (Song et al., 2013).
The proposed approach requires a point cloud and a set of color images. We obtain these using existing correspondence-based techniques (Furukawa and Ponce, 2010; Wu, 2011), with a focus on inexpensive equipment; however, hardware-based approaches that generate point clouds in three dimensions, such as LIDAR scanners, also could be used. The point cloud is first described by fitting a set of planar patches (flat surfaces), each representing a small section of plant material, usually a segment of leaf. Where the quality of the input point cloud is high, the initial surface estimate will provide a good model of the plant. Image noise and the complexity of the plant, however, will typically lead to missing areas of leaf material and poorly defined leaf boundaries. Therefore, we extend existing approaches by refining the initial surface estimate into a more accurate plant model. Initial surface patches are resized and reshaped based on image information and information obtained from neighboring surfaces. The resulting surfaces are then subdivided into triangular sections suitable for output to produce a smooth and geometrically accurate model of the plant.
The reconstruction process makes few assumptions about the nature of the plant material being reconstructed; by representing each leaf as a series of small, flat sections (like pieces in a jigsaw puzzle), the complete leaf surface itself can take any reasonable shape. The output is a 3D triangular mesh structure that is suitable for ray tracing or other modeling applications in a format that can be imported into many 3D graphics and modeling packages. The generality of our technique allows it to be scaled to scenes involving multiple plants and even plant canopies: the software makes no assumptions about the number of plants in the image. However, the focus of this article is on the accurate reconstruction of single plants of varying species. In the discussion below, we will outline our intentions for extending this method to field-scale phenotyping.
A software tool utilizing the techniques described in this article has been released as an open-source project under Berkeley Software Distribution license.
In this section, we present results obtained when applying our reconstruction approach to multiple views of single plants. Verification of our approach is achieved using a unique artificial data set in which an in silico model rice plant is rendered from multiple viewpoints to generate artificial color images that are then treated in the same way as a real-world image set. This approach allows the reconstructed plant to be directly compared with the artificial target object, a difficult problem if no such ground truth were to exist.
We have tested our reconstruction methods on data sets obtained from rice (Oryza sativa) and wheat (Triticum aestivum) plants. The number and nature of the images were left to the user to decide given the subject in question, although we recommend more than 30 images surrounding the subject for a single plant, including a variety of angles from side on to above. No special consideration was given to the environment in which the plants were imaged: the rice data set was captured in an indoor environment and the wheat data set in a greenhouse. These environments provide complex backgrounds, which raise additional challenges, but the plants can still be reconstructed using our methods. It is likely that a permanent installation with a more strict protocol for image capture would result in more consistent point cloud reconstruction between data sets; readers are encouraged to explore this option if using our methods over extended periods. An overview of each data set is given in Table I.
The computational performance of our approach and the image capture that proceeds it varies depending on the number of images and the parameters used. For a recommended data set of 30 or more images using default parameters, both the image capture and reconstruction can be expected to take on the order of a few minutes.
Reconstruction of Example Rice and Wheat Data Sets
Figure 1 shows the result of applying our reconstruction approach to the rice and wheat data sets. The reconstructions are colored based on the normal orientation of each surface; the models have not been textured to avoid concealing imperfections in the output mesh. Quantitative evaluation of the effectiveness of any 3D shoot reconstruction is challenging due to a lack of ground truth models for comparison. Here, we offer a qualitative evaluation of the benefits and shortcomings of our approach followed by a quantitative evaluation using the virtual rice data set.
The initial surface estimate, obtained by calculating an α-shape over each patch, will naturally reproduce any flaws present in the patch-based multiple-view stereo (PMVS) point cloud (Fig. 2, A and C). Most notable is the lack of point information in areas of poor texture and noise perpendicular to the leaf surface, where depth has not been adequately resolved. These issues can be caused by the heavy self-occlusion observed in larger plants but are often caused in even simple data sets by a lack of image features in the center of leaves.
Depth noise is significantly reduced by the use of best-fit planes in small patches, as all points are projected onto a single flat surface. However, the boundary of each surface is a function of the parameters used to create the α-shape and the quality of the underlying data. As such, we can expect the α-shape boundaries to be a poor representation of the true leaf shape. With this in mind, we would characterize a successful reconstruction as one that improves upon the initial surface estimate through the optimization of the surface boundaries.
Consider Figure 2, A and C, depicting initial surface estimates from the rice and wheat plants in Figure 1. In Figure 2A, the initial surface contains a great deal of overlapping regions, something that, if not rectified, would produce errors when quantified or used in modeling. Figure 2B shows our level-set approach to have removed this overlap and smoothed each surface boundary such that the original leaf surface is recovered. This result is representative of the success of our approach across all the leaves in this data set.
The wheat data set generally contains wider leaves, and their lack of surface patterning often results in only sparse point data in the center of each leaf. This is shown in Figure 2C, where large missing sections of leaf must be recovered. Figure 2D shows our results on this section, in which the cluster boundaries have been optimized and again form a more continuous surface. There is still some overlap in the clusters on the left of the image, but this is caused by the angle from which the image was rendered and relates to the relative orientations of neighboring clusters.
The clusters toward the left of Figure 2D are orientated at slightly disjointed angles, due to noise in the original point cloud reconstruction. This makes optimization of the intercluster boundaries challenging, as the intersection of these boundaries depends not only on their orientation but also on the position from which they are being viewed (the reference view IR from "Materials and Methods"). This is an important characteristic of our reconstruction algorithm in its current form: the boundaries of neighboring patches will be reshaped relative to the reference camera view IR for each cluster. It is possible that gaps may be observable between surfaces when viewed at angles very dissimilar to the reference view. In reality, for clusters with very similar orientations, these gaps will be negligible. However, as the main focus of our reconstruction work to date has been optimizing the boundary speed functions of level sets, we have yet to address this problem. We anticipate that further work on smoothing the normal orientations of neighboring clusters or merging neighboring clusters into a single curved leaf model will solve this issue; this will be a focus of upcoming research.
Quantification of Accuracy Using in Silico Image Capture
To verify the accuracy of our reconstruction approach, an additional data set was created based on the plant used in the rice data set. The rice plant was first manually modeled using the point cloud created by PMVS and 3D graphics software (Topogun [SC Pixelmachine SRL, version 2.0; www.topogun.com] and Blender [version 2.69; Blender Foundation; www.blender.org]). This is a time-consuming and subjective process and should not be viewed as a suitable alternative to automatic reconstruction. However, it is possible to produce an easily quantifiable ground truth model that can be used as a target for automated reconstruction. We textured and colored the virtual plant in order to emulate the original plant leaves. Finally, we rendered 40 distinct camera views of the same model (Fig. 3A), simulating an image-capture system moving around a static plant. The resulting data set can then be reconstructed in the same manner as real-world data, but importantly, we retain the ability to compare the reconstruction with the original virtual plant model, in particular keeping the same coordinate system. The original model, and our reconstruction, can be seen in Figure 3, B and C.
As our model contains no notion of leaf or plant structure at this stage (plants are described as the combination of many distinct surfaces), we have chosen to focus our evaluation on macro-level geometric traits such as surface area instead of lower-level traits such as leaf angle or curvature. We anticipate that these additional traits will be incorporated as our methods are extended to consider topological plant structure.
A variety of geometric measures were calculated to quantify the differences between the original ground truth model, the initial surface estimate, and the model output by our reconstruction. In each case, any differences between the expected and observed results were used to produce percentage error values, which can be seen in Figure 4A. An increase or decrease in error between the initial surface estimate and our optimized reconstruction is an indication of the performance of our level-set approach.
The summary measurements of width, depth, height, and convex hull volume for the ground truth model and our reconstruction are in good agreement. The convex hull represents the smallest 3D volume that still encompasses the entire model. The results show that our approach does not significantly alter the overall size and shape of the original model. The slight decreases in depth and convex hull volume were caused by the very tip of the one leaf missing in the output model; this, in turn, could be caused by missing point data in the original cloud or the lack of a suitable reference image of that surface.
Area measurements were also calculated from both two-dimensional (2D) projected viewpoints and an overall surface area measure for each model. An orthographic projection acts in a similar way to the flattening of points onto their best-fit planes, as described above. The plant is rendered with all surfaces projected parallel directly onto the image. This removes all considerations of camera focal length and radial distortion. In all cases, the area of the reconstructed model is slightly higher than that of the original ground truth. In the worst case, the corner-projected (three-quarter) view shows a 5.5% increase in size in the reconstruction over the original model, and the total surface area was increased 3.9% from the original model. However, all area measurements show an improvement over the original surface estimate, from an average error of −10% to +4%. The initial surface estimate may also contain additional flaws, such as those seen in Figure 2, that are not reflected in these geometric measurements.
We believe that the 3.9% increase in total surface area observed between the original model and our reconstruction is caused primarily by the nature of our patch-based model. As we model the curved surfaces of leaves using a series of small, flat patches, this approximation will often differ slightly from the underlying plant (Supplemental Fig. S1). Results can be improved, to a point, by altering the size of the surface patches (Supplemental Fig. S2); however, good results usually can be obtained with the default values provided by our software. It is also possible to observe an increase in accuracy with an increase in image resolution (Supplemental Fig. S3). This benefit is marginal, however, and will come at the expense of increased cost of hardware and higher computational requirements for processing. Importantly, these results show that our approach is consistent for a large range of image resolutions and patch sizes, meaning that any user should be able to obtain good results without the need to optimize numerous parameters experimentally.
While approximating a curved surface with planar sections will inevitably reduce accuracy by a small amount, small sections are easily managed, and the algorithmic complexity of the fitting problem is reduced. Our approach, therefore, is able to reconstruct more complex surfaces than many existing algorithms applied to this type of data and, crucially, is generally applicable to plants of any size or shape. Future work will look to combine this general reconstruction approach with a more specific plant model for certain species, allowing us to obtain more accurate models of larger surfaces without completely sacrificing generality.
Software Availability
The software associated with this article is open source, distributed under a Berkeley Software Distribution license. It will be distributed on SourceForge (http://sourceforge.net). A link to the SourceForge distribution page is available at www.cpib.ac.uk. The software is written in C# using the .NET framework, so it is currently available only for the Windows operating system.
The recovery of accurate 3D models of plants from color images is challenging. A single plant constitutes a crowded scene in the sense described by Furukawa and Ponce (2010), and the construction of accurate 3D models of objects of this level of complexity is an active research topic. Images of plants exhibit high degrees of occlusion, with the occlusion relations between leaves varying from image to image. To complicate matters further, individual leaves are difficult to identify: most of the leaves on a given plant have similar color and texture properties. Rather than address these issues in a single process that transforms a set of images into a 3D model via feature correspondence or silhouette analysis, the approach presented here develops each leaf segment individually, automatically selecting an image likely to contain the necessary information. The proposed method reduces the effect of occlusion by choosing an image with a clear view of the target surface and addresses the similarity problem by performing detailed analysis of the colors present in that image. The approach is suited to image capture with either a hand-held camera or a fixed imaging setup and will work effectively on images taken using low-cost consumer digital cameras.
The mesh representation produced provides a detailed model of the surface of the viewed plant that can be used both in modeling tasks and as a route to shoot phenotyping. It should be stressed, though, that the surface description output by the proposed technique comprises a large set of distinct planar patches rather than larger, curved surfaces describing whole leaves. The level-set method resizes and reshapes each patch to maximize its consistency with neighboring patches and the selected image, and as such the reconstructed patches provide an accurate approximation of the leaf surfaces.
Looking to the future, both field phenotyping and canopy-scale modeling will require 3D models of plant communities and canopies. The major additional challenge as the number of plants is increased is the greater incidence of occlusion between leaf surfaces. Our reconstruction algorithm operates on a best-view reference image, chosen separately for each patch. Therefore, it is robust to occlusion, as heavily obscured viewpoints are discarded. However, it is still the case that leaves that cannot be viewed clearly from at least one camera are likely to be poorly reconstructed. This makes the image acquisition process particularly important.
Further development of this technique itself will focus on the generation of more easily usable 3D models for plant phenotyping. Plant trait measurements can be obtained from a point cloud or patch-based model in a straightforward manner using existing modeling packages such as Meshlab (Cignoni et al., 2008); however, this is still a manual process in which a user can select points of interest and obtain distance, or other, measurements. Grouping neighboring patches into leaves before fitting plant-specific leaf models would remove boundary discrepancies between nearby patches, providing automatic trait measurement while remaining applicable to ray tracing. As yet, our technique does not distinguish between plant leaves and stems. We anticipate that domain knowledge can drive the segmentation process here as well, where plant components are identified based on knowledge of expected plant structure. Finally, while the performance of our approach and that of the initial point cloud estimation appears robust to a varied number of input images and views, the determination of the optimal set of image views and mechanisms for their capture is something that should be explored.
Growth of Plants
Cultivation of wheat (Triticum aestivum) plants took place in greenhouses on the Sutton Bonington campus, University of Nottingham, during the summer of 2013. Seeds of the variety Paragon were germinated on Levington's seed and modular compost (Everris). Following vernalization, they were transferred to 2-L pots with J. Arthur Bower's John Innes #2 soil-based compost. Plants were watered daily and illuminated with natural light as supplemented with 400-W high-pressure sodium (sonTAgro) bulbs (Philips). Rice (Oryza sativa) plants of variety IR64 were cultivated hydroponically in a controlled-environment chamber as described by Hubbart et al. (2012). Conditions were 30°C (continuous), photoperiod of 12 h, with light provided by 600-W metal halide bulbs (Philips) supplemented by domestic incandescent bulbs. Irradiance at plant height was 700 µmol m−2 s−1. Sampling took place at the leaf 11 stage.
Obtaining an Initial Point Cloud
The reconstruction algorithm described in this article uses an initial point cloud estimate as a basis for the growth of plant surfaces in three dimensions. Numerous software- and hardware-based techniques exist to obtain point clouds of objects. While we have chosen to make use of an image-based technique, PMVS (Furukawa and Ponce, 2010), in principle, other approaches, such as laser scanners or LIDAR systems (Omasa et al., 2007), also could work effectively in its place.
PMVS reconstructs a 3D point cloud model of a plant and scene based on multiple color input images. A requirement of this algorithm is that the intrinsic (focal length, etc.) and extrinsic (3D position and orientation) camera parameters be known. In the case of static-camera capture systems, such as that used in RootReader3D (Clark et al., 2011), calibration (the process of obtaining camera parameters for all images) can be performed once and the estimated parameters used until the system is reconfigured. Tools exist that can perform automatic calibration of moving camera systems, and our intention is that this software be usable with a single digital camera (we use a Canon 650D with a 35-mm lens), with images captured manually by a user. We use the VisualSFM (Wu, 2011) system to perform automatic camera calibration. This software provides a step-by-step process for camera calibration and provides an interface to the PMVS software. VisualSFM uses scale-invariant feature transform (Lowe, 1999) features to find corresponding points in pairs of images, which in turn are used to calculate the 3D position of each camera position relative to all others and relative to the model being reconstructed. There are two outputs to this process: a series of camera models, each representing the 3D position of the camera when each input image was taken, and the point cloud representation of the entire scene reconstructed by PMVS.
VisualSFM and PMVS were created to address the general image-based modeling problem and represent the current state of the art in this area. PMVS has been shown to provide high-quality descriptions of simpler, convex objects, and although plant complexity poses a challenge, it provides a sound basis for the development of plant modeling techniques.
An Initial Surface Estimate
Although each point can reasonably be expected to lie on some surface, the point cloud representation produced by PMVS contains no explicit description of those surfaces. If the final plant model is to be used with a ray-tracing system (Zhu et al., 2004; Song et al., 2013) or if details of individual leaves are required, a surface-based description must be constructed. Methods for the reconstruction of a surface mesh from a point cloud exist (Carr et al., 2001; Kazhdan et al., 2006). Most, however, construct a single surface describing the entire point cloud. While leaves of the same plant can be considered connected in the biological sense, attempting to fit a single surface representation between multiple leaves is challenging. In fact, we wish to identify each leaf separately, to aid the reconstruction and later modeling processes. This surface identity problem is increased when extending a reconstruction approach to plant canopies. Common algorithms such as Poisson surface reconstruction (Kazhdan et al., 2006) cannot be used over complex plants or plant canopies; where they attempt to fit a single surface over the scene, they will inevitably oversimplify the complex structure.
In this work, we address this problem by representing plants as a series of small jigsaw-like surface sections, with each leaf surface being composed of multiple separate surface patches. We begin by producing an initial surface estimate based on the input point cloud before refining this estimate until the plant model is complete.
The initial surface consists of a number of small, flat surface patches (the pieces of the jigsaw puzzle) that represent small areas of plant material. Previous work in plant modeling has used planar sections to represent leaves, such as the artificially generated plant models used by Song et al. (2013). Over larger plants, or within a plant canopy, large planes represent an oversimplification of the underlying leaf shape. Smaller surface patches increase accuracy and allow neighboring sections with different orientations to characterize the curved nature of the leaves, just as smaller jigsaw pieces would allow us to better approximate the curved surface of a 3D ball puzzle.
In order to establish the required size of the fitted planes and the location and orientation of each patch, the point cloud is first segmented into small groups of points (Fig. 5A). Points are clustered with their nearest neighbors using the strategy described by Klasing et al. (2008). We extend this method to limit the potential size of each cluster, such that no surface patch becomes too large, oversimplifying the model. Points are grouped as defined by a preset distance, above which points are considered too far apart to form a cluster. This distance is dependent on the size and resolution of the model being captured. However, as the PMVS algorithm and laser scanning devices usually output points with a consistent density, the distance parameter can be set once and then remain unchanged between experiments. Here, clusters were limited to a maximum size of 150 points; reducing this number will increase the number of planar sections fitted to the data, increasing accuracy at the cost of decreased algorithmic efficiency. In our experiments, a limit of 120 points represents a significant partitioning of the point cloud, typically producing sections of leaf approximately 2 to 5 cm2. Crucially, the limited size of the clusters ensures that points on neighboring leaves, between which a surface should not be formed, are rarely clustered together.
In order to approximate the surfaces throughout the model, a flat surface section is fitted to each group of points using least-squares regression. This best-fit plane minimizes the orthogonal distance to each point and is described by a center point and two vectors representing the surface orientation and rotation (Fig. 5B). It is this plane that represents the 3D position of this section of leaf surface, with the shape of the plane (which we will optimize shortly) describing the edges of that leaf surface. In performing the optimization steps on these planes, rather than the 3D point cloud, we simplify the challenging 3D surface-fitting problem into a series of less complex 2D surface-fitting problems.
Any 3D reconstruction process requires a coordinate system or frame of reference in which to describe the world. It is helpful when describing the process to visualize the different coordinate systems in use. We identify the system used by our plant model as world coordinates, representing the 3D geometry of the scene. It is in this coordinate system that the eventual completed model will be output. Each point in the input cloud is associated with the cluster of points to which it has been assigned and can be projected (flattened) onto the best-fit plane for that cluster. Once projected, we say that the point resides in 2D planar coordinates (Fig. 5C), where instead of a 3D world position, we refer to its location on the patch on which it sits. The orthographic projection also has the effect of flattening the points in each cluster to lie on their best-fit plane, reducing noise in individual points and, as mentioned above, reducing the search for an optimal patch boundary to two dimensions. It is important, however, to consider the planar and world coordinate systems as essentially different views of the same information. As such, point and mesh surfaces generated on a plane will have an associated world position that can be output as a final 3D model, and if the boundaries of our 2D patches are improved, so too is the boundary of the 3D plant model.
An initial surface description is constructed by calculating the α-shape (Edelsbrunner et al., 1983) of each set of 2D points, expressed in planar coordinates. An α-shape is a subset of the commonly used Delaunay triangulation (which generates a triangular mesh over a set of points) but contains restrictions on which edges and faces are preserved. In general, points that are closer together will be connected by triangular faces, whereas boundary points that are farther apart will not. In practice, the value, α, can be increased in size to increase the level of detail in the boundary of the triangulation by removing larger edges. The α-value can be tuned for a given data set to preserve the shape of the boundary of each reconstructed point set. Figure 5, D and E, shows the Delaunay triangulation for the example cluster and an associated α-shape. The Delaunay triangulation oversimplifies the boundary of the shape, whereas the α-shape does not.
Shape Optimization Using Level Sets
The set of α-shapes computed over all clusters forms an initial estimate of the location and shape of the leaf surfaces. The limitations of the initial stereo reconstruction on plant data sets means that, in many instances, this estimate will be inaccurate or incomplete and will require further optimization to adequately reflect the true shape of the plant. Missing areas of leaf should be reconstructed, and overlapping shapes should be adjusted to meet at a single boundary. Many methods, such as active contours (Kass et al., 1988), parameterize the boundary of a shape before attempting this optimization: fitting a curve that is then manipulated to best describe the leaf. Such approaches are ill suited to the complex boundary conditions that might be produced by α-shapes. Consider Figure 6A, in which three example clusters have been segmented in close proximity and described by their α-shape boundaries. The red cluster contains a hole, but this hole should be preserved in order to prevent the yellow cluster from being obstructed. The red cluster also contains two distinct segments; this occurs when all the links between the two sections are longer than α and is difficult to model with a traditional contour method. This is particularly true where we can expect these two regions to merge as the optimization process continues, as is likely here.
These problems can be solved using the level-set method (Osher and Sethian, 1988; Sethian, 1999). The distinct regions and hole in the red cluster are preserved, and each region can reshape independently of the others. This is achieved by defining the boundary as the intersection of a 3D surface and a plane
The boundary of the α-shape computed for a given cluster is used to initialize a level-set function. The level-set method defines a 3D function, ϕ, that intersects the cluster plane (Fig. 6, B–D). We represent the level set as a signed distance function, where the value (the height) at any point on the function is equal to its distance from the nearest boundary. The function contains negative values within our α-shape boundary and positive values outside. Thus, the boundary itself is defined as the set of all points in ϕ that intersect the cluster plane. Consider the function in Figure 6B. It is initially shaped such that the intersection with the 2D plane matches the red region in Figure 6A. If the values of the level-set function are decreased, the 3D function will move downward and the boundary will change shape, joining up the two separate regions and eventually filling the hole in the surface. Alternatively, if the function were to move upward, the boundaries would shrink, making the surfaces continually smaller until they eventually disappear, where the 3D function no longer intersects the plane at all.
Our goal is to optimize the height of the level-set function such that the boundary represents an accurate approximation of the shape of the leaf on which the cluster is located. To achieve this, during each step of the level-set process, the height of the distance function at any point (x, y) is altered based on a speed function v. The speed function can be altered based on both global and local parameters; thus, different regions of ϕ can move upward and others downward. The result is a boundary that grows or shrinks as necessary to fit the underlying data. We have defined our speed function to consider three components: the curvature of the boundary, the underlying image data, and the interaction between neighboring surfaces:
Boundary Curvature
vcurve is a measure of the local curvature of the level set, calculated as described (Sethian, 1999). We calculate this as:where κ is the curvature at a given point on the level set, which represents the smoothness at that point, and ω is a small scaling factor used to ensure that curvature does not dictate the movement of the patch boundary compared with vimage and vinter. The curvature term encourages the level-set function to remain smooth, meaning that the boundary of the surfaces will also remain smooth. With any triangulation such as α-shapes, it is possible for the initial patch boundaries to be jagged (Fig. 2), while it is likely that the optimum leaf reconstruction will be smoother. Strongly convex or concave regions will be given negative or positive curvature terms, respectively, and will generally become flatter over time. An example showing the effect of this component of the speed function is shown in Supplemental Figure S4.
Image Information
The image term, vimage, references color information in the input images to ascertain whether the reconstructed surface lies over regions depicting plant material. We view each small patch of leaf surface from the point of view of one camera position and compare this projection with the captured image. Where an image does not show plant material at the expected location, it is likely that the surface should be shrunk at this point. Conversely, if pixels near the current surface boundary show a high likelihood of being plant material, it may be beneficial to grow the surface into this position.
While it is mathematically possible to view a given surface in any input image, it may not be useful to do so in all cases. Some images will present an obscured view of a specific leaf surface, and others may be viewed from a near-perpendicular angle, resulting in a view of a region that is less useful than other camera views. In the worst case, some cameras will not see a given surface at all, where the leaf falls outside of that camera's field of view. To avoid these difficulties, rather than combining information from multiple views, we choose instead to pick one so-called reference view in which to obtain color information. We choose a reference image that represents a calculated best view of an individual flat surface, and this is performed for each surface patch in the reconstructed model.
Reference image selection begins by projecting each cluster into each camera view (viewing each cluster surface with respect to each camera). For each initial surface, we obtain the pixel locations in which that surface appears in a specified image. Attached to each projected position is a z-depth value that records the distance that the projected point lies from the camera's image plane. These distances can be used to sort clusters that appear at the same location in an image, which may be caused by either true occlusion between leaves or overlapping surfaces caused by errors in the input point cloud. In both cases, a heavily overlapped view of a surface is a hindrance, and this image should be considered less suitable for selection as the reference view.
It is desirable to select camera views that contain as little interference between surface patches as possible. For each image and each patch, we calculate vclear, a measure of the number of pixels into which a surface projects, voccluded, representing the percentage of occluded pixels belonging to that cluster, and voccluding, representing the percentage of pixels within a certain cluster that occlude those from other clusters. voccluded and voccluding fall between 0 and 1, and in both cases values close to 1 are preferable. vclear is normalized between 0, for an image in which a surface does not appear, and 1, where that surface appears at its largest. The clear pixel count also provides a measure of the angle of incidence between a cluster and the camera plane. If a cluster is seen at an angle from a given camera, this camera will likely contain fewer projected pixels than one that sees a front-on view of that leaf surface.
For each patch, the combination of normalized clear pixel count, occlusion, and occluding percentages can be used to sort images in terms of view quality:
A reference image, IR, is chosen for each patch that maximizes this view-quality measure. In other words, an image is chosen such that the patch appears as large as possible in the image and with as little occlusion as possible.
When referencing pixel values in the image IR, we use a normalized green value to measure the likelihood of leaf material existing at that location. Normalized green is robust to changes in illumination, which are frequent within a canopy where shadows are cast from other leaves. It does this by disregarding luminance and considering only hue.
Computed from the red, green, and blue color channel information, the normalized green value of a pixel is calculated as:
As long as care is taken to choose a suitable background during image capture, we can assume that normalized green values will be higher in pixels containing leaf material and lower in pixels containing background. Where lighting conditions remain consistent over an image set, we can also assume that the distribution of normalized green values is the same over each image. The expected normalized green values of leaf material for an image set should be ascertained at the beginning of the reconstruction process, before being used to contribute to the vimage term.
In a data set where the only strong normalized green response originates from plant material and the background intensity is fairly consistent, we can assume that the histogram of normalized green values for all pixels over all images will contain two dominant peaks, shown by the dashed line in Figure 7. If we restrict sampling to only those pixels that are initially occupied by surface patches, we would expect the frequency of background pixels to be reduced dramatically, as shown by the solid line in Figure 7. The unimodal thresholding approach of Rosin (2001) is ideally suited to analyzing such a histogram and is capable of finding an appropriate threshold level below the foreground peak. Using this point, marked in Figure 7 with an arrow, the vimage term can be scaled such that surfaces that appear significantly green in their reference image will grow and those that do not will shrink. The vimage term, therefore, is calculated as:where N is the normalized green value of that surface location in its reference image, t is the threshold calculated using the Rosin (2001) method, and σ is the sd of the normalized green peak. This term will produce a value between −1, where a lower normalized green value reaches the expected color of the nonplant pixels, and +1, where a higher value reaches the expected color of plant material. This will cause the level-set boundary to grow over areas of plant material and shrink over areas of background.
Surface Interaction
The final component of the speed function, vinter, works to reshape each surface based on the location and shape of nearby patches. As each surface patch is located on an individually oriented plane, neighboring patches might have different orientations. This makes the calculations of their 3D intersections challenging and makes decisions on how to reshape these surfaces to best avoid one another difficult. For example, there may be two neighboring patches that are separate (that do not intersect) in world coordinates but from specific camera views may obscure one another. In other camera views from different angles, they may not overlap at all. We want to find the optimum boundary shape for these patches such that they are sufficiently close in world coordinates in the final model. We use the reference image IR for each patch and examine the interactions in this 2D view. The function vinter is then calculated such that any given point is penalized if it is occluded by another surface when seen from the point of view of the reference image. We define the function as:where p is a small negative value such that the level-set boundary shrinks at this location. Where this occurs, we also set the value of vimage at this position to 0, regardless of the normalized green value. This causes the image component to be ignored where a surface is occluded. The motivation for this is that where a surface is obstructed, we cannot reliably use image information at that position. In simple terms, the vinter component of the speed function will have no effect where a surface is growing separately from all others in the model. In cases where a surface is obscured by one or more others, the boundary will shrink away from these locations.
The Complete Speed Function
The individual components of the speed function are responsible for optimizing each level set, and therefore each surface boundary, in a different way. The curvature term ensures that extremely sharp boundaries are avoided: these boundary conditions are unlikely on a real leaf, and a smoother boundary is also beneficial when outputting the final 3D model. The image term references an image chosen that provides a detailed view of that surface and will grow the surface where leaf material is observed in the image. The interaction component will examine neighboring surfaces in the same reference image and will shrink patches at locations where they are obscured. The complete speed function, therefore, works to grow each surface where leaf material is observed while preserving the smoothness of its boundary and ensuring that overlapping surfaces are avoided.
The complete speed function is used to update each position on the level-set function, and this process is performed for each surface patch. The process must be repeated until each cluster boundary has reshaped to adequately fit the underlying image data. The speed function will slow significantly as we approach this optimal shape. Where a level-set boundary no longer moves with respect to the reference image (does not alter the number of pixels it appears in), we mark this cluster as complete and discontinue level-set iterations. Any level sets that do not slow significantly will continue until a maximum number of cycles has elapsed, a parameter that can be set by the user. We typically use between 200 and 500 iterations as a compromise between computational efficiency and offering each level set adequate time to reshape. In many cases, clusters will halt naturally before this maximum has been reached.
Remeshing the Level-Set Functions
Once the level-set operation has terminated, patch shapes may bear little resemblance to their original α-shape description. Each surface, therefore, must be retriangulated in order to provide the mesh information required for a complete plant model. The α-shape method used above is less suitable for this task, as the parameter α may have a noticeable effect on the resulting boundary shape: the level-set function now has a known boundary that was not available during the original surface estimation. This can be used to drive a more accurate meshing approach that will preserve the boundary contour.
We use constrained Delaunay triangulation for this task, based on the algorithms outlined by Shewchuk (2002). A constrained triangulation will account for a complex boundary shape when producing a mesh from a series of points; however, it will not oversimplify the boundary by fitting surfaces across concave sections and can consider holes in the surface if required. For each cluster, we sample from the boundary of the level set in order to obtain a series of points that travel clockwise around the shape. A constrained triangulation is computed from these points, a process that will automatically generate additional points, where required, within the shape itself. An example mesh generated from a single level-set function is shown in Figure 8.
As each point in the new triangulation exists in planar coordinates, they can be easily back projected into world coordinates to be output in mesh format. Our software outputs the completed mesh in the standard PLY format, which is readable in all commonly used software packages and can be imported into modeling tools.
Supplemental Data
The following materials are available in the online version of this article.
Supplemental Figure S1. Possible errors observed when using a patch-based reconstruction approach.
Supplemental Figure S2. The effect of patch count and size on reconstruction accuracy.
Supplemental Figure S3. The effect of input image resolution on reconstruction accuracy.
Supplemental Figure S4. The operation of individual components of the level set speed function.
The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Tony P. Pridmore (tony.pridmore{at}nottingham.ac.uk).
↵1 This work was supported by the Biotechnology and Biological Sciences Research Council (funding to the Centre for Plant Integrative Biology to E.H.M. and T.P.P.).
↵[C] Some figures in this article are displayed in color online but in black and white in the print edition.
↵[OPEN] Articles can be viewed online without a subscription.
↵[W] The online version of this article contains Web-only data.
- three-dimensional
- light detection and ranging laser
- patch-based multiple-view stereo
- two-dimensional
- Received August 19, 2014.
- Accepted October 7, 2014.
- Published October 20, 2014.


text:
REPORTS: Star Due On Merseyside For Medical
News emerging tonight is that Newcastle have agreed to let Liverpool sign their Spanish full-back, José Enrique in a deal costing between £5 and £10 million.Depending on which paper you believe, The Mirror suggests £5 million while the Daily Mail s…
News has emerged that Newcastle have agreed to let Liverpool sign their Spanish full-back, José Enrique in a deal costing between £5 and £10 million.
Depending on which paper you believe, The Mirror suggests £5 million while the Daily Mail says the deal is worth £10 million. Either way, if we get to sign the experienced defender, it will shore up a long-standing problem in our team.
I believe we will get our man for around £6 million. He is due at Liverpool within the next 48 hours to have a medical and agree personal terms. A bit of solidity at left-back and another Spaniard to keep Reina company.
Submitted by Friends of Liverpool
12 Comments
Well for understanding between them in a defence it makes sense notwithstanding the fact that he is a good defender with experience!
Believe in this news/rumour until it happens !!!
we need a complete new back four if were playing with four but i think with the players we have we should jus play 3 with lucas and adam just in front of them, but think we should use flanagan and kelly we need to start trusting our youth squad to flourish in the first team, and all the ones that did well last season shelvey, spearing so on seem to of all been replaced so whats the point of the accademy just to use the players wen needs must and ship them of even if they do well, they say were buying for the future we why not play the next couple of seasons for the future and give the younger ones their chance, if we carry on like we are we might even win the league but at the cost of becoming another joke club like man city and chelsea, i hate the mancs(utd) but all there last ten years of sucess has been down to the ones coming through at an early age from their accademy, giggs, scholes, beckham, nevile so on the whole spine of there team.
The back four were perfectly fine the backend of last season…
The only reason we are leaking pre-season is simply because we don't have Lucas protecting them once he is back we shall be tight again!
good news if true YNWA
shawcross and Scott Dann then thats it for liverpool
José Enrique is good at going forward and putting balls into the box, but i dont rate him as defender ,very suspect to me
Gaz wood he looks very suspect? I take it you dont watch a lot of football. You have bought a good player in carroll and paid over the odds, you have bought Henderson which is comical, you have spent 13 million too much on downing. Believe me as a toon fan and football watcher this is a very good buy.
Kenny pls just try and sign enrique,cahill nd showcross/upson.if u can pay 16 millio pounds 4 henderson den y can't u pay 18 million 4 cahill who is gud..is lyk kenny is b'comin anoda hodgson…we need more dan 2 quality defenders.tym 4 defenders plsssss.send dis 2 kenny or henry
Not good is is crap insua is better left back then him
This is a shrewd bit of business from KK. Not only do we have an attacking left back to assist Downing, we have a pal to keep Reina happy in the gloomy North. Also Suarez will have another person to speak to in espanol. Previous posts he is dodgy with defending, on the contrary, did he have a midfield like ours to keep the opposition at bay? Just 1 more big name KK, and we ready for at least 2nd or 3rd position. And please sell Cole, Poulsen, Insua, Aurelio, ElZhar, Ngog and Degen.
Or yes, Kenny if you are reading these articles, please follow Man United, Tottenham and other premier league teams and travel to South Africa next summer for a few friendlies.
There are loads of Pool die hard fans here….and we would fill Soccer City and Moses Mabhida stadiums. YNWA
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Why I Don't Feel Guilty Eating Foie Gras, And You Shouldn't Either
Published on Friday, 30 September 2016 17:11
Written by Cheryl Teo Kai Lin
Foie Gras with Truffle Cream from PocoLoco
I have gotten a lot of shit for my passion of foie gras. Everytime I do, I die a little inside, not out of guilt for the duck/goose that was slaughtered for its liver, but for humanity's intellect which is at a rapid decline for wholeheartedly believing practically everything they read on the internet or hear from word of mouth without even doing a modicum of research into the truth. Whatever happened to intellectual curiosity?
I was five years old when I lost my foie gras virginity. It was during a brunch buffet at Equinox in Swissotel where my family was celebrating my birthday.
Classic Foie Gras from Garçons
My dad had set a plate with an indiscernibly-shaped, wobbly piece of meat infront of me and informed me that it is goose liver and it tastes phenomenal, encouraging me to try some. I gingerly cut off a small slice and put it into my mouth, and in that exact instance, the cosmos aligned for me, everything in the world suddenly felt pristine and perfect, my eyes opened to the hidden paradise that only food as divine as this piece of liver can take you away to.
It was love at first bite. It was decadently rich and creamy with a delicate fruity sweetness and a subtle funkiness. Pan-seared foie gras is caramelized on its two flat sides, so it is nicely charred on the outside with a crisp texture, but extemely tender, succulent and buttery on the inside. Foie gras is 90% fat and it has this amazing key feature of melting at just below body temperature. Foie gras goes into your mouth in a highly viscous, semi-solid state and slowly melts away like butter, enveloping your mouth with the most heavenly taste and texture.
Black Miso Foie Gras from Garçons
Needless to say, I promptly went back to the foie gras station for seconds, and thirds, and fourths and etc. Though there was a limit on foie gras for each diner, the chef secretly kept piling on the foie gras for me because he thought that I was a cute kid who knew how to appreciate the finer things in life at the tender age of five. In actual fact, I'm just a glutton. But I am a glutton who has done a lot of research into my favorite dishes because I am THAT passionate about eating.
Ducks Explode From Being Force Fed?!?!
Foie gras has long had a lot of controversy surrounding it, with the main subject of concern being the inhumane method of farming ducks and geese for their livers. People who argue against eating foie gras would mention how foie gras farmers shove metal tubes down the birds' throats that pierce their esophagus and pump in so much food that sometimes the birds explode. Is this true? Let us trace this claim back to the original source that brought this to attention.
AHA! Here is the
from PETA (People for the Ethical Treatment of Animals). Basically the gist of the PETA article is that the birds are subjected to abuse by being force-fed, die from ruptured organs from over-feeding, housed in small metal cages with barely any room to move around, and female chicks (which can't be farmed for foie gras) are tossed into grinders as soon as they hatch.
Meat is Murder PETA campaign
Do note that this organization has an inherent penchant of organizing crazy, over-the-top publicity stunts that 90 per cent of the time involves nudity, like the
Meat is Murder
campaign where volunteers lie on huge styrofoam trays in the nude drenched in fake blood to mimic meat being sold in supermarkets, or filming a sarcastic video in the fictitious
Sunny Acres Farm
with naked volunteers contained in cramped metal cages looking petrified and mentally disturbed, and the most recent one where
volunteers are actually branded with a red hot branding iron
to show onlookers the pain that animals farmed for meat go through.
The list of insane antics conducted by PETA fanatics goes on and on, but these few examples should tell you how severely melodramatic the organization is. So maybe you should read their articles with a pinch of salt. If you still want to take their word for it, then maybe the next part, which is one of the saddest things I have ever discovered about an animal organization, will sway your belief.
The PETA Exposé: The Animal Shelter That Kills Puppies and Kittens
It has been revealed by countless of websites with photographic evidence that PETA's agenda gravitates towards holding shocking "artistic" demonstrations than actually offering shelter to abandoned and unwanted animals.
I have written a long article titled
Animal Shelter of Horrors
in graphic detail with numerous photos of dead dogs and cats that were frivolously thrown out like trash after they have been euthanized by PETA.
Now I am not living in the illusion of a perfect whimsical world. I do understand that many animal shelters are faced with the difficult decision of putting down certain old and unadoptable animals when they run out of space. But what PETA is running is basically a slaughter-house. Instead of using their time and resources getting these poor animals adopted, they would rather kill them so that they can focus their energy on their crazy publicity stunts.
In 2012 alone, PETA euthanized 89.4% of the animals they received. These poor animals are sent to PETA with the intention of giving them a second shot in life but they are put down without a second thought and their bodies temporarily stored in a US$9,370 freezer installed solely for this purpose. The frozen bodies are then regularly picked up by Pet Cremation Services of Tidewater and incinerated. This entire inhumane process is paid for by the donations of animal-lovers who have no idea that their well-intended contributions would go to the mass murder of thousands of innocent lives.
In fact, over the past 11 years, PETA has massacred a staggering amount of 29,426 perfectly healthy animals. It is probably very difficult for animal-lovers to believe that their kindly donations are going towards ending the lives of these animals but there is concrete evidence showing that PETA is a hypocritical, unfeeling organization whose interest seem to primarily lie in organizing attention-grabbing publicity stunts rather than improving the lives of animals.
The PETA headquarters is based in Norfolk, Virginia. Even though PETA states that they believe "that animals have rights and deserve to have their best interests taken into consideration, regardless of whether they are useful to humans", it does not seem to be the case at all. What they ACTUALLY believe in that they do not mention, is that people have a right to kill animals as long as it is done "humanely", which in PETA terms mean poisoning them with an overdose of barbiturates, even if the animals are not suffering from any illnesses or wounds.
PETA defends themselves by blatantly LYING that all of the animals they kill are "unadoptable". This is a complete, outright lie as numerous rescue groups, individuals, and veterinarians have come forward testifying that the animals they have passed over to PETA were healthy and adoptable. Former employees and interns have even admitted to PETA's monstrous acts, with one intern reporting that he quit in disgust after personally witnessing perfectly healthy, adorable and chubby puppies and kittens put down in the killing room.
"It is a lie because PETA refuses to provide its criteria for making the determination as to whether or not an animal is "unadoptable." It is a lie because according to a state inspector, the PETA facility where the animals are impounded was designed to house animals for no more than 24 hours. It is a lie because Ingrid Newkirk herself admitted as much during a television interview: when asked whether or not PETA kills healthy animals, she responded, "Absolutely." It is a lie because PETA staff have described the animals they have killed as "healthy," "adorable" and "perfect." It is a lie because PETA itself admits it does not believe in "right to life for animals." And it is a lie because when asked what sort of effort PETA routinely makes to find adoptive homes for animals in its care, PETA had no comment."
First Foie Gras, Next, World Domination
So what has the inhumane foie gras industry got to do with PETA killing puppies and kittens? Simple. It is a recipe for world domination. Or rather, a small step in their grand plan of turning everyone vegans. All their publicity stunts, and everything they release about the meat industry is aimed at getting people to stop consuming meat. All these radical projects and disturbing videos filmed in decrepit farms are funded with the money that is supposed to go towards finding animals their forever homes.
PETA had secretly filmed a
of a foie gras farm's facilities, with Kate Winslet narrating the sorrowful plights of the birds that are bred in this dilapidated farm. The video is truly disturbing – it shows ducks being forcefully handled while being fed, ducks hyperventilating in small cages, and ducks covered in their own feces. But what Kate Winslet failed to mention is the location of this particular farm that they filmed.
For all we know, it could have been a farm in China where animal rights are practically non-existential. They show you the worst of the worst, trying to portray that this is how every foie gras farm operates, when it is the farthest thing from the truth.
The above in-depth documentary on foie gras is the final episode of a three part series. In this video, a woman visits a foie gras farm in France where the ducks have free reign of the farm for a couple of hours and they are housed in spacious, sanitary pens. They are fed a special concoction of corn and duck fat (this is for the feed to slide down easier even though ducks don't have a gag reflex) through a tube and their necks massaged to ensure a smooth flow. A fixed amount of feed is fed once every morning and evening, and as shown in the video, it is really not a lot. It even shows how the ducks are prepped for the removal of their livers.
The documentary is a stark contrast to PETA's video. With that video, PETA has successfully managed to propagate their fanatism through foie gras as it a lesser known food. By buying into the whole "ducks explode from foie gras production" and preaching it to others, you are not only spreading false information but also a form of propaganda.
Forget PETA, and Focus on Yourself
PETA is a hypocritical organization, but you might just be a hypocrite too. Some foie gras production might not be as humane as you would like it to be, but do you know that the scrambled eggs you eat for breakfast, that chicken rice you consume for lunch, and the mee pok you have for dinner does not come from a land of sunshine and rainbows?
Male chicks being ground up alive
For example, something as innocuous as unfertilized eggs has a great deal of cruelty behind its production too. There needs to be a constant supply of new hens to replace old ones to keep up with the egg demand. So the farm sorts through cute, chirping, fluffy chicks once they hatch. They identify the female chicks and place them aside where as the males are thrown into a metal grinder alive (footage
Hens in the eggs industry
Hens die due to the terrible living conditions
Hens are also cramped into tiny, squalid cages once they reach maturity so that the eggs they lay simply roll down a tube and makes for simple gathering. To prevent deaths due to frustration from over-crowding, the female chicks' beaks are cut off with a hot blade so that they can't peck their cellmates.
Beaks severed off
Foie gras, poultry, red meat, pork and seafood – there is always some sort of cruelty behind the production of every single type of meat. A quick search on the internet will bring up the atrocities involved in the mass production of meat. But unless you are willing to give up meat and eggs entirely, and become a full-time vegan, shut the hell up about how consuming foie gras is cruel and eat your goddamn eggs that are as equally cruel in producing. And even then as a vegan, studies have shown that
plants can tell when they are being eaten alive
, and emit this frequency akin to screaming. Yup, there is no escaping the circle of life.
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This is Kailash hereJai Shree Krishna
friends, This is Kailash here. new in Punespider.
you can be in touch with me everytime. I like this site. before I was at apnacircle but I think this is also useful to all, so I request to all my friend please add me in your friends/buddy list & enjoy punespider
Jai Shree Krishna
Kailash Chandra Ladha (Mastermind)


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Ouch, what a bad week for the dog. After rolling to two straight wins, Hartley stumbled last week and absolutely collapsed this week, going a year-worse 1-4. True, I didn't do much better, but at least I beat the dog and helped the Humans tie up the overall score. To recap the week…
Furman vs. LSU (-47)
After a shaky first half, the spread of this game was never in doubt. A sure-thing LSU victory, though, was in question, but the Tigers got their shit together in the third and fourth quarters to win 48-16. Still, a 32-point win is nothing near the 47 points LSU needed to cover.
Furman was my "Bet the Farm" pick, and again, I continue to do well with these picks. Katie also picked Furman and was correct, while Hartley went with LSU and lost.
Tennessee vs. Alabama (-28)
Tennessee had been good at beating the spread this year, even if the Vols fail to win most of those games. Sadly for me, Tennessee didn't even show up against Alabama and lost 45-10. Katie and Hartley took Bama while I lost with my Tennessee pick.
UCLA vs. Oregon (-22.5)
I was sweating this one as Oregon only led UCLA 21-14 after three quarters. Thankfully, Heisman hopeful Marcus Mariota decided to put the game away in the fourth quarter and the Ducks won 42-14 and covered the spread. I won, while Katie and Harley lost with UCLA picks.
Penn State vs. Ohio State (-14.5)
I could not have been more wrong with this game. I thought Penn State had a legitimate shot at knocking off Ohio State after the Nittany Lions' upset of Michigan. Oops.
The Buckeyes took a 42-7 lead at halftime and handed Penn State its worse loss since 1899 with a 63-14 final. Katie was the only one to pick Ohio State.
Texas Tech vs. Oklahoma (-7)
In the best game of the weekend, Oklahoma escaped a back-and-forth game with Texas Tech to win 38-30 and cover the spread. Again, Katie was the only one to pick this game correct.
As previously stated, Hartley finished 1-4 (20%) for the week. I went 2-3 and Katie went 4-1 to give the Humans a 6-4 (60%) record. The Humans dominate this week and tie the overall record 4 ½ to 4 ½.
Week 10 will feature some ho-hum games, but hopefully betting against the spread will make it more interesting. We'll see.


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A Torrance resident whose father was killed while working as a sheriff's deputy was named Tuesday as the recipient of the National Law Enforcement & Firefighters Children's Foundation scholarship.
Brianna Powell was one of 23 students from across the country to receive the award, which is based on financial need and academic merit. Recipients are children of law enforcement officers killed or permanently disabled while in the line of duty.
Powell's father, Deputy David Allen Powell, was killed Nov. 30, 2002, when he responded to a call of shots fired in Artesia. A gunman fired four shots, striking Powell in the upper body.
Brianna Powell is a graduate of North High School, where she was National Honor Society scholar and an actress and dancer who performed in plays and on dance teams. She was a Girl Scout, volunteered at local schools and senior citizen centers and worked to combat bullying.
She is a sophomore at Cal State Fullerton.
— Larry Altman


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wikiHow is a "wiki," similar to Wikipedia, which means that many of our articles are co-written by multiple authors. To create this article, 15 people, some anonymous, worked to edit and improve it over time.
This article has been viewed 25,183 times.
Learn more...
There's a very old joke that goes, "How do you get to Carnegie Hall? Practice!" You may have also heard the expression "Practice makes perfect." Or, how about this one? "A little practice goes a long way." You may notice a theme here: Practice is the main road to becoming really good at something. Here are some tips for how to practice anything-from a musical instrument to a speech, a sport, or even just doing something as simple as homework.
1Set the stage. Practicing something badly may be as ineffective as not practicing it at all. So before you even begin, make it easy for yourself to practice well by choosing a quiet, uncluttered environment. This means turning of the television, clearing off a space on the desk or table, moving to an empty room, or otherwise making your surroundings conducive to concentration and productive work.
2Check your attitude. As important as your practice environment is your attitude: Go into your practice session with enthusiasm and optimism. Having a positive outlook before you start can help you deal with the inevitable bumps along the way.
3Set small goals. Start out with goals that you can reach. If your practicing an instrument, trying to learn an entire piece in one practice session is a goal that may be too big to reach, which can leave you frustrated and even less likely to want to practice. The key to successful practice is to aim small. You will not only learn better what you're practicing, but also enjoy it more.
4Bring snacks. Nobody can concentrate when they are hungry, thirsty, or tired so make sure you have some snacks on hand for when your attention is flagging.
5Relax. Before you practice take several deep, slow breaths in through your nose, filling your lungs with air. Then slowly blow the air out through your mouth. Do this several times to bring yourself into a relaxed state of mind.
6Start with the hard stuff first. Whatever you are practicing-whether it's a piece of music or a swimming stroke you're trying to master-take a look at what you have to do and figure out which parts are hard and which parts are easy. Then, work on the hard parts first. This will accomplish a few things: First, it will get the stuff you dread out of the way, and free you up to spend time on the things that come easier to you. Second, it will make all the hard parts seem easier. Starting out with the hard stuff with a fresh mind will be easier than trying to tackle the tough stuff when you're tired from practicing everything else.
7Break it down. Even if you have already started your practice with small goals, it is always a smart technique to break it down even further. That means working in tiny chunks. If you're working on music, that may mean working on one measure at a time; if you're memorizing a speech that may mean practicing one sentence at a time. Practice those small bits until you are able to do them five times in a row. Once you have one piece done, you will be able to move on to the next one.
8Put it back together. Once you've practiced in small bits, then your job is to put them back together. One you can do that, keep enlarging those bite-size chunks you've been tearing off until, eventually, you put it all back together.
9Work in slow motion. For things such as homework, this means slowing down and not rushing through, reading carefully, and not skimming. For things such as a dance recital, this means working is literal slow motion, like a slow-motion instant replay in sports. Play or dance slowly and you may be surprised at how much attention you give to small parts. Once you're able to practice in slow motion, speed it up. You will improve greatly without a doubt.Advertisement
Community Q&A
QuestionWhat do you mean by practice makes perfect?Community AnswerThe more you repeat something, the better you will do it and the more it will seem natural to you.
- Set your mind on what you want to practice before practicing it.Thanks!


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Hiitanjan on Sept. 20, 2009
Okay, I've decided this thing to be a kind of "testing comic". Uhhh… Does that make sense? I'm planning on trying new shizz on coloring and I'm gonna use this comic as a test subject. So each page will be oddly colored. hrrrm. yeah. Storyline is kinda fuzzy, I haven't really thought it thoroughly with this one. OVERALL: I just make things up as I go. LOL.


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What's your next adventure?
Discover the amazing world of Canyon Adventures and the powerful lessons that can be learned by simply experiencing the great outdoors with others. At Canyon Adventures we love to share the joy of nature through hiking and technical canyoneering. Whether you've never stepped foot on a canyon trail or regularly navigate through them using gadgets and gizmos, we hope you will join us on our next adventure!


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I'm writing an InformationSpan coverage report on emerging technology. It's rapidly narrowed down to reviewing insight sources which are of use to technology strategists, who need to review emerging trends and novel technologies, and assess them for potential impact, and figure out what to pilot.
Among the major service providers, of course Gartner's long established Emerging Trends and Technologies group led by Jackie Fenn holds the lead; I recently covered Jackie's and Mark Raskino's book Mastering the Hype Cycle. But the point of this post is to give readers a sneak preview of the report (it's still far from finished) by highlighting a discovery.
Harbor Research reckon to focus on the "pervasive internet" or what Forrester call the X (for "Extended") Internet – sometimes expressed as "Everything connected, everything communicating". In writing my couple of short paragraphs, I signed up for their free access and downloaded a recent white paper called Designing the Future of Information. It's my kind of paper. It's strongly focussed. It covers a couple of novel technology developments, the academic groups which originated them (at MIT in this case), and their status as they come to market. It sets them in the wider context, it links to open standards, and it assesses (in this case, enthusiastically) their potential impact. It makes connections between them. And it suggests an impact from the synergy, which might not be seen looking at the technologies individually.
I've only one quibble, which is that the report doesn't carry a date (except the year, in the copyright claim). This is a shame, since one of the things you need to know about a report like this is its topicality in months, not years. Otherwise, for a technology specialist, this is a rich resource. Have a look at Harbor!
Harbor Research is online at http://www.harborresearch.com/


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Public Speaking and Presentations
Audrey continues to impress her audience! She has presented at numerous events and functions sharing tips, important information and stories that assist professionals and families prepare for their caregiving journey. Need a speaker? See her eSpeaker profile
What the audience has to say:
• I was just on the call for Long Term Caring. Thank you for this. It was really enlightening. My parents are still independent, but I am relieved to know that there are plenty of resources out there to help us out when we need it. (Toronto)
• This morning's Long Term Caring presentation is fantastic. (Victoria)
• It is very important to me to learn all I can about the aging population, It makes sense from a business model but even more so from a need and service to our elderly clients. …I want to thank you for having this presentation. (Edmonton)
• Absolutely fantastic! Thanks so much – this has been something that I have been very concerned about given the aging population and knowing that a few of our clients are starting to go through this very thing. Well worth the listen, Audrey is a real gem. (Hamilton)
What advisors have to say:
Thanks so much Audrey. The feedback has been tremendous and your message, content, delivery, and depth of knowledge resonated with the audience. – Bev Moir, MHSA, FCSI, CLU | Associate Director, Senior Wealth Advisor, Rosedale Office, Scotia Wealth Management™
Ontario Bar Association:
Scotia Wealth: Burlington office
Audrey interviewed on CTV Your Morning with Ben Mulroney on Senior's issues and the upcoming election:
Financial Services/ Law firms:
•Ontario Bar Association: Effective Estate Planning for Persons With Disabilities
•Fidelity Investments- Female Advisors Education Day (Vancouver)
•Fidelity Investments- Female Advisors Education Day (Toronto)
• Fogler Rubinoff LLP
• Mills & Mills
• RBC Dominion Securities Inc. Advisors and clients Calgary
• WeirFoulds LLP
• Schnurr Kirsh Oelbaum Tator LLP
• Hull & Hull LLP
• Blaney McMurty LLP
• Goodmans LLP
• Fasken Martineau
• MD Financial Management: Trust advisors and clients
• RBC Dominions Securities Inc: Preferred Clients
• Royal Trust Corporation of Canada: World Elder Abuse Awareness National Education Presentation to staff
• Whaley Estate Litigation Partners
• Goddard Gamage LLP
• de VRIES Litigation LLP
• MD Financial Management: Clients
• Toronto Dominion Wealth, Private Bankers
• Wynn Harvey Wealth Counsel • Richardson GMP Limited: Preferred clients
• Scotia Wealth Management Rosedale Office: Advisors and preferred clients
• 53rd at Raymond James Ltd
• STEP (The Society of Trust and Estate Practitioners -Toronto) – Elder Care: A Practical Approach- Panelist
• CIBC: Education Series 'Learn From The Pro'
• Simcoe Estate Planning Council
• RBC Dominion Securities: Preferred clients
• Blakely Wealth Management: Preferred clients
• CIBC Trusts: Lunch and Learn, Selected Topics in Elder Care.
• The Canadian Bar Association, Trusts & Estates Law – speaker ' De-Escalating the Family Feud'
• Hull and Hull LLP: Lawyers
• StoneGate Financial: Client Appreciation
• United Financial: Teleseminar across Canada: Advisors
• Berkshire Securities: Preferred clients
• Scotia Estate Planners National Conference: Capacity: Advisors
• Scotia Trust Private Client Group webinar across Canada: Advisors
• TD Waterhouse Private Client Services and TD Waterhouse Investment Services Inc: Advisors and preferred clients
• Borden Ladner: Lawyers and staff
• Freedom 55: Preferred clients
• Kingsway Financial Group Companies: Employees
• The Royal Bank of Canada: Advisors
Community Groups:
- CAGP Leave A Legacy Series
- UJA York Region Division, Keynote speaker "Ask the Experts", The Sandwich Generation
- Mt. Sinai/UHN Geriatrics Institute – Conference speaker: The Force is Aging
- RBC Canadian Wealth Management Womens' Symposium "Capitalize on Change"- Conference speaker
- Institute of Law Clerks of Ontario: Annual Conference Speaker 2017
- CIEPS, Annual Conference: June 2019
- Prosserman JCC
- Canadian Association of Gift Planners: Ethics and Other Challenging Issues
for Gift Planners & Charities- Panelist
- Four Seasons Hotel
- Alzheimers Society Kingston
- Markland Woods Golf – Tee Talks
- Ontario March of Dimes
- Wellness Fair CBC Building
- Revera Retirement Living Regional Marketing Meeting, Toronto, Ontario
- National Women's Show
- Elder Care Fair, University of Toronto
- Women's Health Matters
- Legislative Assembly
- KPMG, IT Women
- Caregiver Matter's Elder Abuse Awareness Project at Ryerson University
- ETFO: Elementary Teachers Federation of Ontario, workshop presenter
- Verity Club
- Ladies Golf Club of Toronto
- Rotary Club of Toronto, (Forest Hill branch)
- Parkinson Society Central & Northern Ontario: Educational Session
- Embracing Aging Speaker Series: Eglinton St. George's United Church
- Circle of Care/Sinai Health System: Keynote