text:
|Numéro de publication
|Type de publication
|Numéro de demande
|Date de publication
|20 avr. 2006
|Date de dépôt
|2 déc. 2005
|Date de priorité
|19 oct. 2001
|Autre référence de publication
|Numéro de publication
|Hao Pan, Baoxin Li
|Cessionnaire d'origine
|Sharp Laboratories Of America, Inc.
|Exporter la citation
|BiBTeX, EndNote, RefMan
|Référencé par (9), Classifications (45), Événements juridiques (6)
|Liens externes: USPTO, Cession USPTO, Espacenet
The present invention primarily relates to replay detection techniques.
The amount of video content is expanding at an ever increasing rate, some of which includes sporting events. Simultaneously, the available time for viewers to consume or otherwise view all of the desirable video content is decreasing. With the increased amount of video content coupled with the decreasing time available to view the video content, it becomes increasingly problematic for viewers to view all of the potentially desirable content in its entirety. Accordingly, viewers are increasingly selective regarding the video content that they select to view. To accommodate viewer demands, techniques have been developed to provide a summarization of the video representative in some manner of the entire video. Video summarization likewise facilitates additional features including browsing, filtering, indexing, retrieval, etc. The typical purpose for creating a video summarization is to obtain a compact representation of the original video for subsequent viewing.
There are two major approaches to video summarization. The first approach for video summarization is key frame detection. Key frame detection includes mechanisms that process low level characteristics of the video, such as its color distribution, to determine those particular isolated frames that are most representative of particular portions of the video. For example, a key frame summarization of a video may contain only a few isolated key frames which potentially highlight the most important events in the video. Thus some limited information about the video can be inferred from the selection of key frames. Key frame techniques are especially suitable for indexing video content but are not especially suitable for summarizing sporting content.
The second approach for video summarization is directed at detecting events that are important for the particular video content. Such techniques normally include a definition and model of anticipated events of particular importance for a particular type of content. The video summarization may consist of many video segments, each of which is a continuous portion in the original video, allowing some detailed information from the video to be viewed by the user in a time effective manner. Such techniques are especially suitable for the efficient consumption of the content of a video by browsing only its summary. Such approaches facilitate what is sometimes referred to as "semantic summaries".
Babaguchi et al. propose a technique to link live and replay scenes in American football broadcast video. The replay scenes are detected by the sandwiching of digital video effects (DVEs) on either side of the replay segment. Babaguchi et al. note that it is impossible to design a general detector applicable to all DVEs, because there are a considerable number of DVE patterns that appear in everyday broadcasts. The DVE effect taught by Babaguchi et al. is a gradual shot change operation related to spatial aspects. The linking of live and replay scenes is performed based upon the dominant color of the key frame and the ratio of the number of vertical lines to that of horizontal lines on the field, which is representative of the camera angle. In effect, Babaguchi et al. detect replay segments by detecting special transition effects, by the manipulation of two natural scenes from the game, usually as a unique type of wipe. The technique for linking using the technique taught by Babaguchi et al., namely using the ratio, is suitable for the intended application, namely, American Football. In addition, the use of motion vectors and segmented spatial regions are inherently unreliable, and the technique can not automatically detect the transitions if the features of the transitions are not previously known.
Pan et al. propose a technique to detect slow-motion replay segments in sports video for highlights generation. The technique localizes semantically important events in sport programs by detecting slow motion replays of these events, and then generates highlights of these events at multiple levels. A hidden Markov model is used to model slow motion replays, and an inference algorithm computes the probability of a slow motion replay segment, and localizes the boundaries of the segment as well. The technique characterizes the pattern of slow motion as discontinuity on pixel wise intensity-based differences of adjacent frames that could be described by three features, and characterizes boundaries of replays segments as another feature: video-evolution ratio that is extracted from color histograms. The hidden-Markov model is applied to make the final decision on slow motion and the boundary of segments containing slow motion replay. However, the technique taught by Pan et al. is limited only to the detection of slow motion replay segments. The system taught by Pan et al. is unable to detect replay segments that are played back at the regular video rate, which are common in sports broadcast.
What is desired, therefore, is a robust replay detection technique.
It is conceivably possible to develop highly sophisticated models of a typical sports video to identify potentially relevant portions of the video, and many attempts have been made. However, such highly sophisticated models are difficult to create and are not normally robust. Further, the likelihood that a majority of the highly relevant portions of the video will be included in such a video summarization is low because of the selectivity of the model. Thus the resulting video summarization of the video may simply be unsatisfactory to the average viewer.
After consideration of the difficulty of developing highly sophisticated models of a sports based video to analyze the content of the sporting events, as the sole basis upon which to create a summarization, the present inventors determined that this technique is ultimately flawed as the models will likely never be sufficiently robust to detect all the desirable content. Moreover, the number of different types of model sequences of potentially desirable content is difficult to quantify. In contrast to attempting to detect particular model sequences, the present inventors determined that the desirable segments of a sporting event are preferably selected based upon characteristics of the broadcast video. The characteristics of the broadcast video which the present inventors identified as providing an indication of an exciting portion of the sporting event is the "replay". Furthermore, the present inventors determined that the "replay" within sporting activities may be identified based upon the presentation by the broadcaster of a logo, together with any special effects if any, during the transition between the "live" and the "replay" events. Frequently the logos include trademarks or otherwise a consistent image (albeit perhaps changing during the transition) provided by the broadcaster, and are used while phasing in and out of the replay segments, because the viewer typically prefers a replay with some sort of transition to signal the replay to the viewer. These may be referred to as "logo transitions" merely for purposes of identification. Also, the logo transitions typically remain the same at least for an entire game, or portion thereof, if not for an entire season.
The logos used for transitions are normally specific to the broadcaster. Such logos are typically graphical in nature and are independent of the corresponding visual content being displayed. Therefore, the logo will not typically change depending on the type of content that is being displayed. Most broadcasters do not vary the logo within a particular sporting activity and they tend to use the same logo at least for an entire season. Therefore, it is feasible to efficiently and robustly model transitions that sandwich replay segments on the basis of the features of the logo and identify replay segments by detecting the logos. The logo detection system may be manual with the logo being identified in some manner or otherwise provided, or fully automatic without the need to know the actual logo a priori where the logos are learned in an unsupervised manner.
The summarization of video may be based upon identifying replay segments. Then a modified video stream may be created based upon the identification of the replays. The modified stream may include the replays without additional material there between, or may include additional video material between the replay segments. In any case, the resulting video stream is of a shorter duration than the original video stream, or otherwise the replay segments are temporally spaced closer together in the modified video stream. Moreover, the modified video stream may be a set of pointers to the original video stream indicating those portions of the original video stream that comprise the summarization, while avoiding the need to actually modify the video stream itself.
A replay segment in sport programs, as depicted in
The starting logo transition and the ending logo transitions marking the start and the end of replay segments, respectively, typically comprise a gradual transition incorporating a fade in and out, cross/additive-dissolve and wipes, and contain the logo(s) of the broadcaster or a special event. Normally the length of a logo transition is about 1 second. By way of illustration, a starting logo transition in the CBS 2000 Super Bowl broadcast, containing the CBS logo and the Super Bowl logo, is shown in
It may be observed that a logo transition contains a set of consecutive frames each of which includes an image of the logo, albeit with a slightly different appearance. In some cases, the logo will not change appearance and/or location during the transition. It is normally sufficient to select one frame with a distinguishable logo as a logo template to represent the starting or ending logo transition. In some cases, a logo image may be obtained from a source other than the video or otherwise based upon multiple frames, as desired. In addition, the logo template is preferably represented as an image, but may likewise be represented by other characteristics, such as for example, a set of textures, a set of image edges, a color pattern, dynamic features across frames (such as motion vectors and "flying" patterns of the logo), a set of discrete cosine transform coefficients, etc., all generally referred to as image characteristics.
Referring again to
While the selection of the logo may performed manually or otherwise obtained, the system preferably identifies a logo template, which may include the logo image, the logo size, the logo color, the logo shape, and/or the logo position, in an unsupervised fashion free from user input, as illustrated in
Identifying the logo template for the starting or ending logo transition is preferably based upon the same structure, namely, the color logo image with a consistent size and shape at a consistent spatial region in the frame. Except for the logo, the image content of a logo transition is arbitrarily different as they are usually spaced apart by at least several hundreds of frames, as depicted in
One automated technique to identify a logo template is to directly find frames with the same logo(s) in a long sporting program. However, there are typically many frames in the video that are very similar but are not related to logo transitions, such as totally black or white frames within commercial segments. While potentially feasible, and within the scope of the invention, the direct searching technique is difficult and computationally expensive.
Another automated technique to identify a logo template is to directly search for logos that occur only in certain portions of a frame. While this may reduce the search field, and within the scope of the invention, this restricted direct searching technique is still difficult and computationally expensive.
Referring to
While temporal restriction of the search region is typically sufficient, the logos normally appear within the same general spatial region of the frame, such as the central portion of the frame. This consistent presentation of the logo may be as a result of the broadcast provider's desire to have their logo readily observed by the viewer. A spatial search region within the central portion of the frame, such as a square or rectangle, may be used for logo template identification. The search region may be varied in size, as desired. The best logo template for a particular logo image may be defined by a search region (for a given spatial region) having the greatest amount of the logo contained therein with the least corresponding background.
Referring to
The slow motion replay segments may be contained within a commercial. Accordingly, such slow motion segments contained within commercial segments are eliminated as potential candidates for logo template identification. A suitable commercial filter is disclosed by Pan et al. in a paper entitled Detection Of Slow-Motion Replay Segments In Sports Video For Highlights Generation, Proceedings of International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2001.
With the slow-motion replays detected and the commercials filtered a logo template identification is performed, as described below.
After commercial filtering, the logo template identifier preferably uses M slow motion segments (M>2). A logo template may be determined from just 2 slow motion segments. Specifically, the system first detects two most similar frames and areas in two sets of a few hundreds frames that surround the two slow motion segments. The most similar frames and areas are potentially identified as the logo frames and logo areas, which for purposes of discussion may be referred to as the "2-Slow-Motion Segments algorithm". The system then uses the detected logo frame as a template to detect the rest of the logo frames in a sports video.
In practice, however, the logo template identified based only by two slow-motion segments is potentially unreliable due to the following reasons:
Therefore a M-Slow Motion Segment detection algorithm increases the reliability. Such a technique uses slow-motion M−1 detected logo frames and logo areas by pairing with M−1 other slow-motion segments and repeatedly running the 2-slow-motion segments algorithm M−1 times. This "M−1 to 1" redundancy helps ensures the robustness and accuracy of the algorithm.
The 2-slow-motion segments algorithm will be discussed which is then extended to the M-slow-motion segments algorithm, and the distance measurements. An example result of the 2-slow-motion segments algorithm is illustrated. Then a table is presented that slows a MXM table that is used to organize the results from different pairs of slow-motion segments. Finally a discussion of the criteria of choosing a logo template from multiple detected logo frames is presented.
The 2-slow motion segment technique, which is then expanded for the M-slow motion segments technique may include the following steps:
The distance d(k)(i, j), may be defined as
d (k)(, j)=βd (k)P(, j)+(1−β)d (k)H(i, j),
where 0<β<1 is the weight, d(k)P(i, j) is the pixel wise intensity-based mean-square difference (MSD), and d(k)H(i, j) is the color histogram-based MSD. Pixel wise intensity-based MSD may be defined as:
where I(n, i) is the intensity of the pixel with index n in frame i, and N(k) are the size of area k. Because of this normalization, d(k)P (i, j) may be independent from the size of area k. Color histogram-based MSD may be defined as:
where H(k)(l, i) is the value of bin l in the color histogram of area k of frame i and is normalize so that ΣlH(k)(l, i)=1. d(k)H(i j) may also be independent from the size of area k. The color space where H(k)(l, i) is calculated (RGB, HSV, or others), the number of bins, and the configuration of bins are chosen by considering various factors, such as computational cost. For example, one could choose the RGB space, 16 bins that are evenly distributed from 0 to 1 for the normalized R component, R/(R+G+B), and the normalized G component, G/(R+G+B), respectively.
The pixel wise intensity-based MSD d(k)P(i, j) captures structural information while the color histogram-based MSD d(k)H(i, j) captures color information. Nevertheless, the two MSDs have different ranges: d(k)P(i, j) could be as big as thousands, while d(k)H(i, j) usually is smaller than 1. Therefore, the weight b is chosen so that d(k)P(i, j) and d(k)H(i, j) make the same (or similar) contribution in d(k)(i, j).
A typical result obtained by the 2-slow motion segment algorithm is depicted in
Because each pair of slow motion segments creates two detected logo frames and areas, and M slow motion segments create M(M−1)/2 pairs, M slow motion segments have M(M−1) detected logo frames and areas. The detected results in an M×M table without the M diagonal elements, as shown in
Cell (n, n) of the table is empty. Cell (m, n) (m≠n) contains indmn and kmn, the indexes of the logo frame and area, of slow motion segment m, which is obtained from slow motion segments m and n. Therefore, Row m of the table contains M−1 indexes of the logo frames in slow motion segment m. Cell (n, m) contains indnm and knm, the indexes of the logo frame and area of slow motion segment n, which is also obtained from slow motion segments m and n (kmn=kmn). Therefore cells (m, n) and (n, m) are filled simultaneously, and the system may need to run the two slow motion segments algorithm M(M−1)/2 times.
Some of the M slow motion segments may not contain logo transitions. As a result, running the two-slow motion segments algorithm on them may at times generate false logo frames with big minimum distance dmn. Therefore, when cells (m, n) and (n, m) are filled, the system may also checks the value of dmn: Only if dmn is above a threshold should the two cells be left empty. This threshold may be determined empirically, for example.
The above threshold reduces the false logo frames with large minimum distance dmn. To eliminate those false logo frames with small minimum distance dmn, the system uses the fact that up to M−1 detected logo frames of one slow motion segment should be within the temporal distance range of approximately 1 second because logo transitions usually are shorter than 1 second. To exploit this temporal constraint, the system may take advantage of the M×M table in
In the table, row m in the table contains M−1 indexes of the detected logo frames of slow motion segment m. If N of the M slow motion segments have the same logo transitions, then in the table, there are N−1 rows, each of which has N−1 detected temporally close logo frames. For example, if slow motion segments 1, 2, and 3 of the six slow motion segments have logo transitions, then each of rows 1, 2, and 3 have two close indmn, that is, the differences of ind12 and ind13, ind21 and ind23, and ind31 and ind32 are within approximately 1 second.
To detect a "true" logo template, the system may consider the detection as a success when N, N>M/2, of M slow motion segments containing logo transitions and their logo frames have been correctly detected. Due to the noise or other factors, in practice, the system may not be able to find in the table N−1 rows, each of which contains N−1 temporally close logo frames. Therefore, a suitable relaxed criterion is that if in any row(s) of the table, N−1 logo frames are within the temporal distance of approximately 1 second, then the corresponding slow motion segment of the row contains a logo transition and these detected logo frames are true.
If the above criterion is satisfied, the system may then choose the logo template and logo area by finding minimum dmn associated with the "true" detected logo frames. If one minimum dmn is associated with two logo frames, and the system may randomly pick one of them as the logo template. If N<M, then the system may go back and replace those slow motion segments without two true logo frames with new slow motion segments and recalculate the table.
After the system detects the logo template and its area, the system uses these pieces of information to locate logo frames in the game in any suitable manner. In the preferred system, the system first calculates the distances between the detected logo template with every frame in the game using the two distance measurements: pixel wise intensity-based MSDs and color histogram-based MSDs. Assuming the independence of the two MSDs, the system may use two separate probability models for the two MSDs. From the two models, the system may then locate those potential logo frames with their a posteriori probabilities.
The system may designate dP(i) as pixel wise intensity-based MSDs,
where I(n, i) is the intensity of the pixel with index n in frame i, Ilogo(n) is the intensity of the pixel with index n in the logo frame, and N is the size of the logo area. One example of log(dP(i)) is shown in
To describe the pixel wise intensity-based MSDs using the probability language, two classes may be introduced: L represents the class of non-logo frames and Q represents the class of logo frames, and two probability densities associated with the two classes: pP(x|Q) and pP(x|P), where x=log(dP(i)). The logarithm of dP(i) is easy to model.
Probability density pP(x|Q), a probability describing non-logo frames, is modeled by a Gaussian distribution. The mean mp and covariance sp of pP(x|Q) are estimated by log(dP(i)) of all the frames including both logo and non-logo frames. Because the number of logo frames is much smaller than the number of non-logo frames, the estimation is reasonable. The histogram of log(dP(i)) is shown in
Probability density pP(x|P), a probability describing non-logo frames, is difficult to estimate because logo frames are unknown and they are in small number. For the sake of simplicity, the system may assume that pP(x|P) is a uniform distribution between 0 and x0, where x0 denotes the threshold that separates the classes of logo and non-logo frames and its value is set to mp−2sp, as shown in
The system may designate dH(i) as color histogram-based MSDs,
where H(k)(l, i) is the value of bin l in the normalized color histogram of logo area k of frame i and H(k) logo(l) is for the logo template.
Because the logo template typically has a small range of colors, it may be inefficient to uniformly distribute the color histogram bins over the entire color space. This is in contrast to the previous learning stage. To achieve the best results, when the number of bins is fixed, the position and the width of each bin should be adjusted to capture maximal color information in the logo template. To reduce the computational complexity, the system may set the widths of the bins in the following manner. The starting and the ending bins of every dimension of the color space have much wider and adjustable widths than the rest of the bins with fixed widths. When the width of the starting bin is adjusted, the system may also change the position (but not the width) of the rest of the bins, as shown in
and then picks the configuration that has the maximum enplogo.
As the pixel wise intensity-based MSDs, the system may also introduce two probability densities: pH(y|Q) and PH(y|P), where y=log(dH(i)) to model color histogram-based MSDs. pH(y|Q) is Gaussian distribution with parameters mH and sH, and pP(y|P) is the uniform distribution between 0 and y0, where y0 denotes the threshold that separates classes of logo and non-logo frames. The value of the threshold y0 is set to be mH−2sH, Therefore, pH(y|p)=1/(mH−2sH).
The logo frames are detected by considering both MSDs. Specifically, to mark frame j as a logo frame, both log(dP(i)) and log(dH(i)) must be smaller than the thresholds x0 and y0, respectively. In other words, both MSDs need to recognize the frame as a logo frame. The corresponding a posteriori probability p(P|dP(j), dH(j)), which describes the reliability of the detected logo frame j, is calculated by the Bayes' rule in the probability theory under the assumption that p(dP(j), dH(j)|P)=p(dP(j)|P) p(dH(j)|P).
The detected logo frames may not be identified as starting or ending logo transitions, although the system uses different starting and ending logo templates. Because of the similarities between the starting and the ending logo transitions, some starting logo frames may be identified as ending logo frames, and vice versa. On the other hand, because of the difference between starting and ending logo transitions of sports programs, if the system uses only one logo template for both starting and logo transitions, some logo frames may be missed.
After successfully detecting the logo transitions, the remaining task is to identify the replay segments. This is not a trivial task, as the logo transition detection algorithm may not necessarily differentiate between starting and ending logo transitions. In detecting the replay segments, a priori information on the lengths of replays provides useful information. Usually replays are 10-20 seconds long and the temporal distances between replays are much longer. Nevertheless, when exceptions happen, for example, replays are very long, or several replays are positioned back-to-back, using the information on the lengths of replays alone may not be enough.
To solve this problem, besides the a priori information on the lengths of replays, the system may once again use its previous slow motion detection algorithm Specifically, the system may use patterns of relationships between the detected logo frames and slow motion segments to guide the grouping of them into replays because a slow motion segment is typically sandwiched by two logo transitions.
The rest of this discussion discusses five patterns of the relationships between slow motion segments and logo frames, and a Rayleigh density function that may be used to model the duration of replays. Then a five-scan probabilistic inference algorithm groups the detected logo frames and slow motion segments into the five patterns of replays based on the probability of duration of replays and the a posteriori probabilities associated with detected logo frames.
A typically replay contains a slow motion segments and is sandwiched by starting and ending logo transitions. By way of example, as shown in
When replays in the five patterns are back-to-back connected together, the system may use the following information on the duration of replays to separate them.
In sports programs, duration of replays can be well modeled by a Rayleigh distribution:
where G represent the class of replays, L represents the duration a replay, SL is the covariance of L, and u(x) is the unit step, that is, u(x)=1 when x≧0; u(x)=0 when x<0. Because the duration of replays is normally longer than three seconds, the origin of the Rayleigh distribution is offset by L0=3 seconds.
The five patterns should not be determined simultaneously. For example, pattern 2 may wait until pattern 1 has been determined, because pattern 1 may be grouped into pattern 2 while pattern 2 does not have the risk of being grouped into pattern 1. Therefore, the system develops an algorithm that scans the detected slow motion segments and logo frames by 5 times. Each scan only groups some slow motion segments and logo frames into one pattern of replays.
The preferred order of the five scans in the algorithm is following
In the first four scans of the algorithm, a probability describing the confidence on the possible replays is inferred from the a posteriori probabilities of logo frames and the probability of replay duration p(L|G):
Finally after completing five passes, the system may leave remaining ambiguous logo frames as errors.
Information of replay segments may be automatically extracted using the described technique or in combination with other types of information in providing accurate and versatile video summary highlights.
A number of techniques have been developed to detect the existence of text in video. These text identification techniques attempt to solve the general problem of identifying text in video and localization in video. In many cases these techniques identify potential regions of text in video as artificial regions that have high contrast and high spatial frequency, and remain stable along the time axis for tens to hundreds of frames. Many of the features identified in these techniques include corners, edges, and texture, which may be extracted in the spatial domain or in a converted domain such as the wavelet domain. High-level classifiers are usually employed by the techniques such as multi-layer neural network, K-means algorithm, or preset thresholds. See, A. Wernicke, R. Lienhart, "On the segmentation of text in videos," IEEE ICME 2000, vol. 3, pp 1511-1514, 2000; H. Li, D. Doermann, O. Kia, "Automatic text detection and tracking in digital video," IEEE tran. Image processing, vol. 9 no. 1, January 2000; T. Sato, T. Kanade, E. K. Hughes, M. A. Smith, S. Satoh, "Video OCR: indexing digital news libraries by recognition of superimposed captions," in ACM Multimedia Systems Special Issue on Video Libraries, February 1998; J. Xie, X.-S. Hua, X.-R. Chen, W. Liu, H.-J. Zhang, "A video text detection and recognition system," IEEE ICAIE 2001, 2001; M. Bertini, C. Colombo, A. Del Bimbo, "Automatic caption localization in videos using salient points," IEEE ICME 2001, 2001; V. WU, R. Manmatha, E. I. Riseman, "TextFinder: an automatic system to detect and recognize text in images," IEEE PAMI, vol. 21, no. 11, November 1999. Unfortunately, these techniques are highly computationally intensive and the resulting performance is poor.
In order to determine interesting segments of the video for summarization, such as replay segments, the present inventors determined that the existence of a scoreboard in the video provides an indication of an associated replay segment and also potentially an interesting event. For example, a scoreboard is overlaid on the frames of football and baseball broadcast video. As an initial matter the spatial position of the scoreboard, if it exists in a frame, should be determined.
To determine important segments, such as replay segments, the present inventors determined that typical production practice followed by the producers of football and baseball broadcast programs: scoreboards are not overlaid on frames during replays, may be used to determine or otherwise verify the identification of replay segments. It may be desirable to detect and filter out replay segments, for example in generation of game summaries composed of only unique event segments, from a set of segments containing events and replays of those events. Events and replays of events can be automatically detected using any suitable technique, such as slow motion replay techniques.
The present inventors determined that the scoreboard may be detected by considering unique features associated with the scoreboard in broadcast video. Specifically, the system may make use of one or more of the following cues:
As discuss below, a scoreboard detection technique may be used in combination with automatic identification of replay segments in broadcast video. A segment in general may be further characterized by the existence or non-existence of an associated scoreboard.
A scoreboard is rich with information that is useful for an audience to understand and follow a game. For example, a scoreboard in a baseball game typically shows the scores, the current inning number, the strike count, and the out count. For example, a scoreboard in a football game broadcast typically shows the scores, the quarter number, the game clock, the play clock, and the attempt number. For example, in a soccer game broadcast, the scoreboard typically shows the score and the game time. Detection of the existence scoreboard is an initial task in identifying the information in the scoreboard.
The existence or non-existence of a scoreboard overlay on the video gives information about the current status of a sporting event, such as a football game or a baseball game. It indicates whether the game is being actively played, or paused/stopped, respectively. In baseball or football broadcast video, a scoreboard is normally only shown in the frames of an ongoing play segment; scoreboard is not shown in replay or non-play segments. In a soccer game, on the other hand, a scoreboard is normally always on at every frame because soccer is not the type of "play" and "non-play" game. Furthermore, locations of the scoreboard also tell audiences which team is at offence or defence in a football game. For football video, the scoreboard on the left side of the screen indicates that the team on left-hand side is at offence and the team on right-hand side is at defence, and vice versa.
The shape of a scoreboard and its position within a frame are broadcaster-dependent. For example, as shown in
Initially the system should automatically detect the frames that contain a scoreboard overlay in broadcast sports video, and accordingly localize the position of the scoreboard. It is also be to be understood that the system may include manual identification with the assistance of user input of the location of the scoreboard overlay. The preferred scoreboard technique has two main parts: training and inference. In many cases it is desirable to perform the training in an on-line, low-latency implementation. For a low-latency implementation the training is preferably performed in two parts, where the second training is invoked only when the first training is not sufficient and the second training is integrated into the inference phase.
To meet the requirements of on-line, low latency implementation, the first training may use a limited number of frames for training. In the case of baseball, where the scoreboard location does not change, the first training is sufficient and the second training step is skipped. This is also the case for football video if the broadcaster uses a single scoreboard as shown in
In the inference phase, if all the necessary parameters are obtained in the first training step, the learned template is compared to the corresponding area of each frame to determine the existence of a scoreboard. Otherwise, the inference process engages the second training step till it is completed. After completion of the second training step, the inference continues without further training.
The scoreboard detection may be applied to filtering out replays from the "candidate plays" otherwise detected. Candidate play segments may be plays or replays of plays, non-plays that are falsely detected, or otherwise general segments of the video. The existence/non-existence of a scoreboard may be used as a cue in discriminating plays from replay and non-play segments in case of football and baseball broadcast videos. Using an additional cue provided by slow motion detection, the system may resolve candidate plays into plays, replays, likely replays or non-plays. Otherwise, the existence or non-existence of an associated scoreboard may be used to select segments in general.
Training in general is based on the fact that during an entire broadcast video, scoreboards are normally at the same location(s) in different frames, and the remaining areas of frames are dominated by the field or court in most times. Therefore, if a sufficient number of frames containing scoreboard are averaged, then the resulting average image has a clear scoreboard and smeared background that is dominated with the color of the field or the court. In essence, the scoreboard is less blurred than the background. It is to be understood that any statistical process using a plurality of frame may be used, as desired. As an example, an average of 3500 frames containing scoreboard in a football broadcast video is shown in
The preferred framework of training is shown in
A sufficient number of frames (e.g., >1) with scoreboard are used as training data. To make the training fully automatic, the system may use "candidate plays" in baseball and football games that are detected by other techniques. While some frames in detected candidate plays may not have a scoreboard, in most cases a substantial number (e.g., a majority) of the frames in the detected candidate plays do contain a scoreboard. By averaging (any statistical process) a sufficient number of these frames, the content present in a sufficient number of the frames will dominate the average image. In other words, the scoreboard in the average image remains substantially clear while the rest of the average image is blurred due to averaging a large number of frames in detected plays.
For an online, low-latency real-time video processing system, short training time is useful. In one particular implementation, the minimum number of training frames is set to 3500. A multitude of candidate plays are typically used in order to reach the minimum number of training frames. Averaging is conducted in the RGB color space. It is to be understood that other color spaces may also be used, as desired. One example of the image generated by averaging 3500 frames is shown in
As shown in
The first kernel aims at detecting horizontal edges and the second kernel aims at detecting vertical edges. Thereafter, the detected edge image may be binarized. One example of the resulting binary image is shown in
The 2-D edge images may be projected horizontally and vertically, and obtain two 1-D distributions, as shown in FIGS. 20(b) and 20(c).
The thresholds to the two 1-D projections may be set in order to determine the position(s) and size of the scoreboard. Specifically, the starting and ending points of the peak of the vertically projected distribution, (y1, y2), define the vertical position and size of the scoreboard. The situation is the same for the horizontally projected distribution in the case of a baseball game broadcast video. In broadcast video of a football game, however, a scoreboard may have two possible positions in a frame, and two positions are horizontally symmetric along the vertical central line of the frame. Therefore, there are two peaks in the horizontal distribution (but still one peak in the vertical distribution), and two sets of starting and end points, (x1, x2) and (x3, x4), define the two possible positions of the scoreboard. Finally, a template is built, consisting of the colored average image and position information (x1, x2) and (x3, x4) and (y1, y).
In the training phase for a football video, however, if scoreboards in the most frames in the training data set are dominantly on the one single side due to the particular game dynamics, then the training is only able to yield one position. In that case, the additional online training step is necessary to train for the other position.
The framework of the inference process is shown in
If tmptR(x,y) is the R component of pixel (x,y) in the template image and IR(x,y) is the R component of pixel (x, y) in the input image, then the correlation coefficient, coffR, between the R components of the template and of the input image at the scoreboard area, defined by (x1, x2) and (y1, y2), is shown in
Similarly, the system may also define coffG for the G component and coffB for the B component. The final correlation coefficient coff is the average of coffR, coffG, and coffB.
If the coefficient coff is above a pre-selected threshold, then the frame is considered to contain a scoreboard at (x1, x2) and (y1, y2). In football broadcast video, if a second scoreboard position at (x3, x4) and (y1, y2) is trained, then the similar method is applied to determine if a frame contains a scoreboard at (x3, x4) and (y1, y2).
If the game is a baseball game, the second training is preferably not used. If the game is a football game, but the scoreboard position information indicates that the scoreboard is of type shown in
For football videos where the learned template is not a strip spanning the top of the frames, and the position information indicates a single scoreboard position, the inference step may include the second learning step.
The second training is based on the two observances valid in many cases: (1) the structures of scoreboards at the two different locations are usually very similar, but not identical; (2) the two possible locations are horizontally symmetric about the vertical central line of a frame. The first fact indicates that the structural content of the scoreboard at the other position is unknown, while the second fact indicates that the location of the second scoreboard is known. Other structures may likewise be determined.
The second learning uses the color histogram of the scoreboard template at the already learned position, henceforth referred to as "template color histogram". The inference module may execute the following steps when the correlation module fails to detect a scoreboard.
Once the number of frames used in averaging in Step (3) reaches N, the second training is disengaged and pure inference continues without training.
Candidate plays detected by existing algorithms may correspond to plays, replays, or non-plays. Using the results of scoreboard detection, replays or non-plays can be filtered out from the set of candidate plays in order to obtain a set of unique plays. Unique plays may be desired in forming compact summaries with no redundancy. The following 3-step decision process may be applied to the candidate plays in order to identify replays and non-plays.
Under many conditions, the second learning is not needed in a batch mode implementation where the entire video to be processed is accessible. Hence, in a batch mode implementation, the second learning step within the inference process does not have to be invoked.
Since a scoreboard is widely used in many other types of broadcast sports, the methodology in this invention is also applicable to many other types of sports. Although in some sports, such as soccer, scoreboard is always shown and existence of scoreboard gives little information, detection of scoreboard as discussed here could serve as the first step of retrieving the contents in scoreboard.
A summarization presented to the user may be hierarchical in nature, if desired. The desired summary may be selected by the user. Referring to
The summary description defines those portions of the video sequence that contain the relevant segments for the video summarization. The summary description may be compliant with the MPEG-7 Summary Description Scheme or TV-Anytime Segmentation Description Scheme. A compliant media browser may apply the summary description to the input video to provide summarized viewing of the input video without modifying it. Alternatively, the summary description may be used to edit the input video and create a separate video sequence. The summarized video sequence may comprise the selected segments which excludes at least a portion of the original video other than the plurality of segments. Preferably, the summarized video sequence excludes all portions of the original video other than the plurality of segments.
A commercial detection module may be used to further refine the potential replay segments by removing those segments that are contained within commercials. In the broadcast industry, one or more black frames are inserted into the program to separate different commercials in the same commercial session. Referring to
Referring to
Referring to
In the case that the summarization is performed at a server or service provider, the user downloads and receives the summary description encoded in MPEG-7 format. Alternatively, in an interactive video on demand (VOD) application, the media and its summary description reside at the provider's VOD server and the user (e.g., remote) consumes the summary via a user-side browser interface. In this case, the summary may be enriched further by additional information that may be added by the service provider. Further, summarization may also be performed by the client.
Referring to
It is to be understood that the locating of the "logo transitions" may be manually identified, if desired. Then other parts of the system may be based upon the manual identification.
The terms and expressions which have been employed in the foregoing specification are used therein as terms of description and not of limitation, and there is no intention, in the use of such terms and expressions, of excluding equivalents of the features shown and described or portions thereof, it being recognized that the scope of the invention is defined and limited only by the claims which follow. All references cited herein are hereby incorporated by reference.
|Brevet citant
|Date de dépôt
|Date de publication
|26 févr. 2008
|21 févr. 2012
|Cyberlink Corp.
|Method for handling static text and logos in stabilized images
|7 déc. 2011
|7 mai 2013
|Google Inc.
|Automatic learning of logos for visual recognition
|22 déc. 2011
|4 juin 2013
|Cyberlink Corp.
|Method for handling static text and logos in stabilized images
|12 janv. 2012
|25 mars 2014
|Casio Computer Co., Ltd.
|Apparatus and method for processing moving image data
|18 avr. 2013
|2 sept. 2014
|Google Inc.
|Automatic learning of logos for visual recognition
|26 févr. 2008
|27 aoűt 2009
|Chia-Chen Kuo
|Method for Handling Static Text and Logos in Stabilized Images
|22 déc. 2011
|19 avr. 2012
|Chia-Chen Kuo
|Method for Handling Static Text and Logos in Stabilized Images
|26 juil. 2012
|Casio Computer Co., Ltd.
|Apparatus and method for processing moving image data
|30 juil. 2014
|18 juin 2015
|Google Inc.
|Automatic Learning of Logos For Visual Recognition
|Classification aux États-Unis
|Classification internationale
|Classification coopérative
|Classification européenne
|2 déc. 2005
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Stila Sheer Tinted Moisturizer Spf 20SKU: #7759439
Product Information
- The ever-popular Stila™ tinted moisturizer just got better! Now with SPF 20 and a new easy-to-use tube container.
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In a very hot and extremely humid day Ana allowed Mauresmo to sail into Miami Semifinals.
Ana never really found to her rhythm as she felt the affects of playing a long singles match against Kusnetsova and a doubles match on Tuesday. Mauresmo raced into 6-1 3-0 lead before Ana began to fight back, but Mauresmo eventually closed out the encounter on her fourth match point, 6-1 6-4.
Despite the loss, Ana had a terrific two-week stay in Miami that included victories against No.10 seed Nadia Petrova and No.6 Svetlana Kuznetsova, which was her first career Top 10 victory.
Ana was playing through a pair of injuries this week in order to reach her first Tier I quarterfinal. She came to Miami nursing a sore hamstring and wound up twisting her ankle in her first practice session at the event. Despite the aches and pains, Ana is pleased with her performances during the fortnight.


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Deer are certainly persistent! Like all repellents, deer repellents are most effective if spray applications are thorough and repeated as needed. New foliage may require bi-weekly applications under severe pressure from deer. We have found that using repellents in rotation is more effective than using only one product. If all else fails, fencing is seen by many as the ultimate protection.
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How can I change the location of the log file in PostgreSQL? Can it be done without stopping the cluster?
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Database Administrators Stack Exchange is a question and answer site for database professionals who wish to improve their database skills and learn from others in the community. It's 100% free, no registration required.
re 1) See the chapter "Where to log" in the manual.
You want to change the
re 2) No, you need to restart Postgres when you change this.


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This story was updated at 9:15 a.m. June 11, 2013
A Roberto Clemente Middle School student has been arrested for spraying pepper spray in a science classroom Friday and several more are being punished for not telling administration what happened.
The student was charged with disturbing school operations, said Montgomery County police spokeswoman Angela Cruz. It is possible there will be additional charges against other students, as the investigation is still ongoing, Cruz wrote in an email Tuesday.The name of the student charged is not being released because that student is a minor.
After the student sprayed the gas around 11:30 a.m. Friday, five students and a staff member were taken to the hospital to be evaluated, the entire school was evacuated and hazmat teams were called to the school.
Students stood in pouring rain for about an hour and buses began to shuttle them to Seneca Valley High School, before hazmat teams determined the school was safe for students.
Khadija Barkley, the school's principal, has punished several involved, she said Monday.
Although just one student sprayed the gas, other students knew what happened and did not immediately tell staff, causing administration to evacuate the school for precautionary measures, Barkley said.
If administration would have known it was pepper spray, she said, they would have just cleared the rooms involved, rather than disrupt the entire school day.
Barkley said the students are facing "appropriate consequences." She said she has the authority to request that a student is expelled, but said she could not say if that is the action she took.
The student caused a major disruption to the school day, Barkley said, adding that the school tries to teach students to take responsibility for their actions. "I believe in the best in our students and our staff, and I feel strongly that we have given the appropriate consequences and the school community will move beyond this," she said.
Up to ten students and one teacher reported having breathing problems, said Beth Anne Nesselt, a Montgomery County Fire and Rescue spokeswoman.
Twelve students were in the immediate area where the smell was detected, but 24 students were evaluated by medical personnel, according to Nesselt.
About eight ambulances responded to the scene. Five students and one teacher were transported to a local hospital for an evaluation and check-up, Nesselt said.
The school has 1,175 students and 200 staff members, and those at the school Friday were evacuated to the front of the school. Few had umbrellas, so their clothes and notebooks quickly became drenched.
Both students and staff handled the situation well, Barkley said.
"The school acted swiftly to ensure the safety of all students and staff," according to Beth Thomas, director of school support and improvement for Montgomery County schools.
Many parents lined the school's sidewalks trying to find their child and asking staff members questions.
Kim Photinakis, who has a daughter in seventh grade, said she got a call from her daughter at 11:45 a.m. She said her daughter thought there was a fire and smelled something. Photinakis said she was relieved to see that her daughter and her friends were OK when she got to the school.
The school's final exams were set to begin Friday. The school has created an alternate schedule for exams since the event, Barkley said.


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Retired products and apps:
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If you have purchased an earlier version of certain ArcSoft software, we recommend you upgrade it to the latest version. The functionalities and operations of the earlier version might be different from the latest version, please download the trial version of the latest version from our official website to try it out first.


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Congratulations to Selena for winning a moon man at the 2013 VMA's! She took home the award for BEST POP VIDEO for "Come And Get It". And of course - thank you to everyone who voted!
#StarsDance is part of the back-to-school sale on iTunes! Get it for $7.99 here - http://smarturl.it/sgiTunesa2
Selena's adidas NEO Label collection is now available in the US! Check it out at www.adidas.com/neo & www.Kohls.com/adidas!
#StarsDanceWorldTour Vancouver are you ready to kick this off?
Now-legal Selena Gomez just got another great reason to turn up at a club : her first #1 debut. Gomez's solo bow, Stars Dance, will top the Billboard 200 album chart after moving nearly 97,000 copies, according to figures provided by Nielsen SoundScan...
Check out the full article at mtv.com


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In this lesson, students will conduct labs to investigate three drivers of climate change: adding atmospheric greenhouse gases, melting glacial ice, and decreasing sea ice. Students, working in groups, will conduct experiments, connect them to... (View More) real-world data, and make posters to present their findings to the class. A teacher's guide, student worksheets, lab instructions, and a Powerpoint presentation (which incorporates related NASA videos) are included. This lesson uses the 5E instructional sequence. (View Less)
Materials Cost: $5 - $10 per group of students
This is an activity about the requirements of life. Learners will explore what living things need to survive and thrive by creating and caring for a garden plot (outdoors where appropriate) or a container garden (indoors) at the program facility.... (View More) The garden will be used to beautify the facility with plant life with many planting and landscaping options provided. Children will consider the requirements of living things, compare the surface conditions on Mars to those found on Earth, view images/video of a NASA Astrobiology Institute "garden" where astrobiologists are studying life under extreme conditions, and consider the similarities and differences in the type of life that would be possible on Mars as compared to their garden on Earth. It also includes specific tips for effectively engaging girls in STEM. This is activity 3 in Explore: Life on Mars? that was developed specifically for use in libraries. (View Less)
This is an activity about the Moon's influence on Earth. Learners think like a scientist — with reasoning skills and a healthy amount of skepticism — to sort puzzle pieces containing statements about the Moon into two images. The "Far-out Far... (View More) Side" has incorrect statements about the Moon (urban myths), and "True-Blue Blue Moon" has true facts about the Moon's influence on Earth and life. This activity is part of Explore! Marvel Moon. (View Less)
Materials Cost: 1 cent - $1 per group of students
This is an activity about Lunar samples. Learners will see snapshots of the Moon's history and hold an important artifact of American history with a Lunar Sample Disk. Earth rocks and soil of similar types as the lunar samples may be provided and... (View More) explored with hands, eyes, noses, and tools. Please note, checking out a Lunar Sample Disk requires training, a secure storage facility, and a minimum of a 6-week lead time. This activity is part of Explore! Marvel Moon. (View Less)
Learners will record detailed observations of a simulated surface and core sample of Mars, analyze a simulated surface and core sample of Mars, compare and contrast an unknown foreign object with the surface and interior of Mars, and learn... (View More) preliminary facts about Mars, its surface, and its place in the Solar System. The lesson uses the 5E instructional model and includes: TEKS Details (Texas Standards alignment), Essential Question, Science Notebook, Vocabulary Definitions for Students, Vocabulary Definitions for Teachers, three Vocabulary Cards, and a Mini-Lesson. This is lesson 2 of the Mars Rover Celebration Unit, a six week long curriculum. (View Less)
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Learners will review what they have learned about scientific and engineering investigation, construct a valid scientific question that can be answered by data and/or modeling, and choose an appropriate mission for their rover that will answer their... (View More) scientific question. The lesson uses the 5E instructional model and includes: TEKS Details (Texas Standards alignment), Essential Question, Science Notebook, Vocabulary Definitions for Students, Vocabulary Definitions for Teachers, four Vocabulary Cards, and supplements on Writing a Scientific Question and Mission Choices. This is lesson 5 of the Mars Rover Celebration Unit, a six week long curriculum. (View Less)
Learners will brainstorm ideas to be developed into a team skit, work cooperatively to assign duties and write a team skit, and collaborate with team members to complete the Mars Rover Manual. The lesson uses the 5E instructional model and includes:... (View More) TEKS Details (Texas Standards alignment), Essential Question, Science Notebook, Vocabulary Definitions for Students, Vocabulary Definitions for Teachers, three Vocabulary Cards, the Manual Template, and a presentation writing Mini-Lesson. This is lesson 13 of the Mars Rover Celebration Unit, a six week long curriculum. (View Less)
This is a lesson about generating hypotheses and testable questions. Learners will use critical thinking and a collaborative approach to pose questions related to the study of Mars and evaluate the quality of their questions. They will explore... (View More) remote-sensing data collected by a camera orbiting Mars - the Thermal Emission Imaging System (THEMIS) and develop a team science question. Students will practice critical thinking skills, use a collaborative approach to this first critical step of the scientific process. Exploring the images of the surface of Mars in Visible (VIS) images, students will come up with a topic of study, their team science question and hypotheses. The lesson models scientific inquiry using the 5E instructional model and includes teacher notes and vocabulary. (View Less)
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This online, facilitated course is designed for middle-school educators wishing to teach global climate change using an inquiry/problem-based approach. Teachers access the course by registering with PBS TeacherLine and enrolling in the course. The... (View More) course supports teaching global climate change using a problem-solving approach and STEM (science, technology, engineering, and mathematics) methodology to engage middle-school students and help them understand the causes and effects of climate change and learn about the differences between climate and weather and how actions and nature affect the environment. The course includes pedagogic support for educators who are interested in using Web 2.0 tools when teaching about climate change in the classroom. Enhance content knowledge of climate change and learn how to effectively implement STEM instructional strategies using resources from NASA and WGBH's Teachers' Domain. (View Less)
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Students will use NASA's Global Climate Change website to research five of the key indicators (vital signs) of Earth's climate health. These indicators are: global surface temperature, carbon dioxide concentrations, sea level, Arctic sea ice, and... (View More) land ice. They will use this information, shared in their expert groups, to create an informative poster about their assigned key indicator. The poster will be used by other groups to learn about all five of the key indicators and how Earth scientists use these indicators to analyze changes in Earth's climate. The lesson plan uses the 5E instructional sequence. (View Less)
Materials Cost: Free


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Man I hate the Patriots, sorry Cheatriots!! I can't wait til coach Philbin starts kicking their butts!
Man I hate the Patriots, sorry Cheatriots!! I can't wait til coach Philbin starts kicking their butts!
Looking forward to the evil empire getting slammed by the NFC Champs.
I'm with you. Philbin, lets get working on that game plan!
It makes me sick to my stomach. Really does...
You cannot settle for field goals like Harbaugh did...
once again belicheat get bailed out after having some horrible coaching calls during the game..... what the hell was he thinking having julian edelman playing CB on the last drive... it almost and should have cost them the game!!!! seems to always fall in there favor though
Mr. Ross: Give all the resources and energy to Dolphins to match and beat the New England Patriots.
They make me sick.
Can't believe the receiver didn't secure the ball when he caught and leaves it wide open like that, what an idiot.
Need the G-Men to win now, they have the defence to batter Brady and win. I can't stomach watching Brady and co win the big one again.
It actually doesn't bother me. I accepted along time ago that until Brady or Belichick retire, the Pats will always be the team to beat. So them getting to another Super Bowl isn't a big surprise. My hatred is reserved solely for the Jets.


text:
About the BioLogos Forum
The BioLogos Forum is designed to foster a serious and comprehensive discussion of Christian faith and the sciences. We believe that charitable engagement of different perspectives within the Church helps sharpen our thinking and deepen our commitment to the truth that is hidden in Christ. So while many of the articles and videos under the distinctive Forum banner come from BioLogos staff and Senior Fellows, we feature a range of voices, including those that disagree with us and with each other. Unless otherwise noted, views expressed are those of the authors, not necessarily of The BioLogos Foundation. You can read more about what we believe here, and join the conversation in the comments section at the end of each post.
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Keep teething comfortable for puppy with a safe, acceptable outlet for chewing. Sentry Petrodex Puppy Teething Twists for Puppies satisfy your puppy's teething needs with a firm yet ductile chew-texture. Chewing action provides comfort during teething and also helps clean teeth and gums. Easy-to-digest Teething Twists offer a safe alternative to shoes, furniture and other items puppy might chew during teething.
Sentry Petrodex Puppy Teething Twists for Puppies each measure approximately 1/2" x 4" long. Sold by weight. 3.7 oz re-sealable pouch contains about 12 Teething Twists.Shop all Dog Dental Care Products.
Please click on "More Information" for directions for use.


text:
JOHNSONBURG – The Johnsonburg major league all-stars won 6-2 over Kane Friday night at Knothole Area Recreational Park in District 10 tournament play.
Johnsonburg led throughout much of the game, scoring two runs in the first and three runs in the third.
WILCOX – The Wilcox American Legion baseball swept a doubleheader with Bradford on a sweltering Sunday afternoon at Jones Township Community Park.
Wilcox won the first game 7-0 and then took the nightcap 8-1 over Bradford to take all three games from the Bradford Legion in the season series.
WILCOX – Another big first inning, another Wilcox victory.
Wilcox scored six runs in the opening frame to take control of the game en route to a 12-0 win over Fox Township Friday night at Jones Township Community Park.
The 6-run first inning Friday comes off a 9-run first inning against Bradford Wednesday night.
"We hit the ball early, everybody was clicking a little bit," Wilcox coach Jeff Peterson said.
WILCOX – Wilcox's starting nine hit early and often against Bradford Wednesday night.
All nine starters for the Wilcox American Legion baseball team scored a run and the team put together 16 hits in a 16-6, 5-inning victory over Bradford Wednesday night at Jones Township Community Park in Wilcox.
UNIVERSITY PARK – Johnsonburg senior second baseman Quinn Luhr summed up the emotions of the Rams after winning the Class A baseball championship game and having gold medals placed around their necks.
"It's a lot better than a silver," Luhr said.
It was certainly a golden day for Johnsonburg baseball as the Rams captured the PIAA Class A baseball championship with a 5-0 win over Canton in the title game Friday at Medlar Field at Lubrano Park.
HOMER CITY – Johnsonburg's school year may have ended over a week ago but the Rams' success in the PIAA playoffs this season sure hasn't.
Johnsonburg advanced to the PIAA Class A championship game with an 8-1 win over WPIAL champion California in the PIAA semifinals Tuesday night at First Commonwealth Bank Field in Homer City.
PUNXSUTAWNEY – It was an extra special winning moment for Quinn Luhr in the PIAA quarterfinal.
After keeping Johnsonburg baseball even with Bishop McCort, Luhr's RBI single in the bottom of the 11th helped the Rams to a 7-6, 11-inning victory over the Crushers Thursday at Rich Kuntz Memorial Field in Punxsutawney.
The win means there will be a new state champion in Class A baseball as Bishop McCort took the title for the 2012 season.
BUTLER – Johnsonburg is making quite a habit of winning in the PIAA playoffs.
After a run to the state title game in basketball earlier this year, Johnsonburg baseball is no exception as the Rams won their PIAA playoff opener 5-0 over Western Beaver Monday afternoon at Pullman Park in Butler.
Johnsonburg will now play District 6 champion Bishop McCort Thursday in the PIAA quarterfinals with a game site and time yet to be announced.
Johnsonburg starting pitcher Cole Peterson was dominant as he struck out 14 Western Beaver hitters in the complete game shutout.


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Posts Tagged 'gunslinger girl'
Anime Club 2010January 4, 2010
Tomorrow night is the first Anime Club meeting of 2010!
We'll be kicking things off with Anime Jeopardy so put your thinking caps on and tromp on over to Fairmount by 5:30.
Then we'll be checking out the latest DVD sent to us by FUNimation called Gunslinger Girl.
And maybe…just maybe…if the Anime Karaoke CD I've reserved arrives in time we'll have to have way too much fun!!!!!!!!!!!!!


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When developing Common Content or Core Standards the first step is understanding that, more often than not, they get in the way of promoting democratic school structures. There is nothing more "undemocratic" than mandating specific knowledge for every individual to know and eliminating all possible choice when it comes to scope and sequence. There is nothing that insults the democratic process more than putting a timeline to statements like "students will know" or "students will be able to" and then punishing them for not being diligent soldiers and memorizing this useless mass of factoids and minutiae while mandating that specific readings and texts be covered within a certain time period.
Even still, rest assured all of you core standard crazies with your request that every kid be able to accomplish "x" when they are "x" years old because I have a proposition. Rather than saying students must master a majority of these standards to be deemed acceptable, why don't we give students options as to what avenue they choose to pursue and mandate that the core standards revolve around critical thinking and synthesis of ideas rather than core areas. Students would still educated in the core areas (so you can keep having them read Beowulf), but the standards would emphasize that teachers instruct to thought processes and cognitive development rather than "who kicked a pile of dirt in 1700?" I assure you that the former is exponentially more important to students' future than the latter.
The key with anything that pertains to democracy, or child development for that matter, revolves around the ability to make choices and to understand that there will be different outcomes for each of those choices. Students that are passionate about specific subjects should be afforded the opportunity to develop and display their passion for that course. Making every student take the same assessment devoids the democratic process. While students should have to take four years of English in high school, the specific type of English course they take should be dependent upon their own personal desires and interests as opposed to assuming that all kids need to take one generic, boring English class. This is part of the reason why people enjoy college more and develop more autonomy in their studies during their post-secondary career than they do in grade school.
Having standards are important, but we need to be mindful of the impact that such standards have on engagement and invoking a feeling of democracy, or choice, in students. If we truly care about having our students grow up as participatory members of society and being invested and interested in their own education then we need to eliminate some of the compulsory feeling and replace it with some participatory ideas. That all has to start with how we view standards and what goes on in everyday classes.


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1. Dirt
2. Circuit boards
3. Brick ovens with dark roofs we live in
4. DIYers & Hacks
5. Manufs building junk
6. Insects
7. Pets
"And remember my sentimental friend......that a heart is not judged by how much you love, but by how much you are loved by others" - Wizard of Oz.
calling back in a timly fasion
answering all concerns
respect to the coustomer
8. "Smart" Valves
Cool tools.
To put the world in order, we must first put the nation in order; to put the nation in order, we must put the family in order; to put the family in order, we must cultivate our personal life; and to cultivate our personal life, we must first set our hearts right.
-- Confucius
9. Hotty milfs
Originally posted by coolwhip
Cool tools.
Dam straight.
I was going to agree with the cool tools, but I really like...
Not having a Steve Wiggins in every state.
Government is a disease... ...masquerading as its own cure
Ecclesiastes 10:2 NIV
Let's not forget following behind the Property Maintenance Proffessionals ;-)
AllTemp Heating & Cooling
Originally posted by AllTemp
Let's not forget following behind the Property Maintenance Proffessionals ;-)
See! Something good does come out of it
Originally posted by RoBoTeq
Not having a Steve Wiggins in every state.
Not to fear, even now we here in Texas are working out nation wide distribution of Steve Wiggins, with an international rollout at a later date!
If more government is the answer, then it's a really stupid question.
Steve Wiggins said "brick ovens with dark roofs". Does this mean it's more difficult to cool a brick veneer house than a home with hardiplank or stucco?
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Pink Martini is a 13-member "little orchestra" from Portland, Oregon, formed in 1994 by pianist Thomas Lauderdale. They draw inspiration from music from all over the world â[][] crossing genres of classical, jazz and old-fashioned pop.
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The Beauty of Vintage Photobooths
|From an original photobooth manual
In an era of instant gratification and Instagram, there remains one photographic tool that holds on to its nostalgic charm—the vintage photobooth. The photobooth—that box with a stool and curtain that we have all been in at one point or another—is as ubiquitous as photography itself. Both have firmly lodged themselves in our collective memory, as recognizable as white bread or football. But while one has managed to adapt itself to the charge of progress, the other has held fast to its original intent and in turn become a casualty of evolution. Both hold important places in the history of art and popular culture. And while one, photography, has melded and morphed into a form that fits the times, the photobooth is quickly being forgotten to the past. Those of us who bother with these machines today come at it from varying angles. Some of us see it as an important art medium, one that should have its own place in the history books. Others are technicians and mechanics who have a love for the machines the same way some people fawn over classic Corvettes. Some are purely in it for the kitsch, seeing the machines as a quirky sideshow act. Regardless, most of us share a little bit of all these things. And to many outsiders we look like happy fools following a rusting, obsolete metal box full of photo chemicals into obscurity. This, we can be certain, we definitely are.
|A vintage photobooth picture, c. 1950s
The booth has three main components: electronic, mechanical, and photographic. The electronics control the firing signals that tell the mechanical components to start, which then activates a series of movements through gears and switches that takes four pictures on a strip of paper and carries it through 14 tubs of chemicals. Because of the repetitive action of the developing process, the photobooths have earned the nickname "dip-and-dunk" machines. It truly is a marvel of turn-of-the-century inventiveness. An entire photography studio, complete with strobe lights and developing darkroom, is crammed into a metal box and electrified. I remember the Frankenstein feeling I had after completing an overhaul on a decrepit photobooth. After many hours of trial and error, and endless tweaking and retro-fitting, you hit the switch and four minutes later a little strip of paper spits out with your smiling face on it. It's alive!!
|L: The delivery unit inside a booth, R: The camera inside a booth
(For more photos from inside of a photo booth, click here)
|Tanks inside a photobooth
|Conceptualization in the photobooth
|Fun in the photobooth
For a more in-depth look at the history of photobooths, I recommend checking out Nakki Goranin's great book, American Photobooth.
Contributor Bio: Russ Goeken is a Savannah-based photographer and collector who manages a handful of cranky dip-and-dunk machines for location and rental use.
Retro Photobooths
Obscura Photoworks (you can rent a booth here)
Russ Goeken Photography


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Hang tight!
Recipe by Donna
"A delicious cookie with chunks of white chocolate and macadamia nuts. A great combination."
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1/2 cup
butter, softened
1/2 cup
3/4 cup
white sugar
1/2 cup
packed brown sugar
1 3/4 cups
all-purpose flour
1 teaspoon
baking soda
1/2 teaspoon
2 teaspoons
vanilla extract
10 ounces
white chocolate, chopped
1/2 cup
chopped and toasted macadamia nuts
So good!! These cookies have always been a favorite and I happened to eat one at Subway the other day and it sent me on a mission to find a great recipe--and this is it! They are the best looking and tasting cookies I've ever made. Next time I will take them out a little sooner than I think they should--to make a softer cookie but even though I don't like cookies on the crispy side I love these! I'm so glad I tried this recipe first! I've made cookies with white chocolate chips before and the chopped white chocolate bar/almond bark makes a big difference!
I tried this recipe and the cookies tasted great, but they were much too thin and frail. They tended to fall apart when removing from the cookie sheet.
70 Ratings
The best cookies I've ever made...we ate a whole sheet of them that night! I used baking powder instead of soda by accident and omitted the nuts.I also didn't chill the dough and even with minor changes they came out great.We took them out when the edges started to brown and they were perfect, I even overcooked one batch by 3-4 minutes and they were still very good.
This cookie was excellent!! Here's an idea for this recipe too: add in 10 oz. of Ghirardelli White Chocolate chips (instead of other white chocolate component) and add about 1 tbs. of fine grounds of macadamia nuts! It is delicious! Kids have fun making them too!
These cookies are a lot like the Subway cookie.
Really good.
A very good recipe for White Chocolate Chunk and Macadamia cookies! I baked them 10-12 minutes to make them more chewy, rather than on the crispy side.
These are awesome. I use less than 10 ounces of chocolate... because that just seems excessive in relation to the amount of dough. I used 8 ounces this time. Whenever a recipe says to chill for on hour, I chill overnight. It doesn't hurt and it usually helps by making the cookies spread out less. Chilling overnight might help those who say they turned out too flat. I have made them twice and everyone loves them. Way better than the Subway ones, which I love!
My family loved these cookies...they did spread a little too much for my liking but that is b/c I missed the part about chilling the dough first.
* Percent Daily Values are based on a 2,000 calorie diet.
White Chocolate Chunk Cookies
Serving Size: 1/24 of a recipe
Servings Per Recipe: 24
Amount Per Serving
Calories: 237
Calories from Fat: 132
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Concession Equipment
Our catalog of concession stand equipment and concession supplies includes everything for making and vending cotton candy, hot dogs, snow cones, popcorn, nachos and more. We offer hot dog carts and snow cone carts for mobile concession vending, whether in the park or at the stadium. Our popcorn machines are great for bars, pubs, lobby areas and even break rooms. Hot dog roller grills, hot dog steamers, and hot dog merchandisers are a great way for convenience stores to entice customers for an additional profit stream. Check out our hot dog supplies and selection of nacho supplies, including chip warmers and nacho cheese warmers, to outfit your ball park concession stand or festival food booth. Compliment your concession stand equipment with condiments servers and dispensers. Offer ketchup and mustard toppings for hot dogs and hamburgers with our condiment dispensers. We sell cotton candy floss for use with your commercial cotton candy machine and popcorn kernels to get popping with your popcorn popper. MissionRS.com has a wide range of concession supplies and concession equipment for sale and we invite you to step right up to see how we can help you complete your concession food offering.


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|Pete Prisco has covered the NFL for three decades, including working as a beat reporter in Jacksonville for the Jaguars. He hosted his own radio show for seven years, and can be seen throughout the week on CBS Sports' NFL shows. When he's not watching game tape you can find Pete on Twitter or dreaming of an Arizona State national title in football.
|Sunday, Feb. 1, 2015
New England
There are two big reasons why I like the New England Patriots to beat the Seattle Seahawks in Super Bowl XLIX: Tom Brady and Bill Belichick. Their run as one of the league's best coach-quarterback tandems of all-time began 13 years ago, when many of the same players who will be on the same University of Phoenix Stadium field Sunday were still hoping to show off for their high-school coaches and win a letterman jacket. That's staying power. The reason it's worked, even as the other names change and the roster turns over year by year, is because they're both among the greats at their respective jobs. Give Belichick two weeks to prepare to stop a gimmicky offense like the one Seattle runs, and I will take that any day. He will come up with something to limit running back Marshawn Lynch, force Russell Wilson to win the game in the pocket, which will play right into the Seattle weakness. That's the Belichick way. I think Brady will have time to make smart decisions against that great Seattle defense. He will be patient. He will use Shane Vereen as a check-down king to move the football. If the Seahawks double Rob Gronkowski, which they will do, he will go to others behind the double. He's smart that way and he will play well enough to win the Super Bowl MVP. The Patriots haven't won a title in over a decade. They've dealt with Spygate and now Deflategate, but none of that will matter when the night is over in the desert. Brady and Belichick will be atop the football mountain again -- maybe earning that spot permanently if they indeed do win another.
Result: New England Patriots 28, Seattle Seahawks 24
TicketCity New England vs Seattle


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Nicki Minaj, Jay-Z Are the Same Person? Prove Us Wrong — Video
Is Nicki Minaj really Jay-Z? Can you prove otherwise?
A recent Vice post contends that the Young Money queen is actually Young Hova with his voice sped up, based on irrefutable evidence and some incontestable speculation, including a video of Nicki Minaj's 'Super Bass' slowed down to half speed, which sounds a tiny bit like Jay's


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Ban Hin Kiong Temple
Home » Ban Hin Kiong Temple » Find Hotel » Lembeh Hills Resort
Lembeh Hills Resort
Nestled in a secluded bay overlooking the Lembeh Strait, the Lembeh Hills Resort brings the sophisticated luxury of a boutique resort to one of Asia ' s most renowned diving venues. Elegant, environmentally responsible architecture, a skilled, enthusiastic staff, and exquisite attention to detail will guarantee a relaxing and romantic experience for divers and non-divers alike.
The 23 spacious villas, which include two deluxe suites, set the standard for luxury in the region.
Ingenious use of natural topology brings the panoramic seascape into every corner of each villa, even the outdoor bathroom, while maintaining the highest degree of privacy.
The teeming waters of the Gulf of Tomini and the agricultural wealth of North Sulawesi provide the ingredients for our chefs to concoct innovative explorations of Asian fusion cuisine as well as the wholesome meals need to satisfy the appetites of active divers and guests.
A camera room and other dedicated facilities cater to the specific requirements of divers, while the horizon swimming pool, luxury spa and fully equipped fitness center gratify the universal desires for pampering and relaxation.
There is also a meeting room and office facilities for work-related gatherings.
The Lembeh Strait is one of the world ' s foremost venues for muck diving, explorations of sea-floor sediment home to exotic, colourful, and often bizarre creatures. The shallow depth and gentle current make the Lembeh Strait ideal for inexperienced divers.
For beginners, individual instruction by PADI-licensed dive masters or instructors will ensure rewarding dives during a short stay. More demanding reef, wreck, and pinnacle diving sites are only a few minutes away.
The resort is also adjacent to Tangkoko National Park, home to the elusive Tarsier, a tiny nocturnal primate resembling a mythic gremlin.
The Lembeh Hills Resort is located on the east coast of North Sulawesi, a 90-minute drive from Sam Ratulangi International Airport in Manado. There are frequent air services from Singapore, Jakarta, Surabaya, Bali, Makassar and Balikpapan.
|Desa Makawidey - Lembeh Strait North Sulawesi, Indonesia
|+62 81 340 545678
|+62 21 781 6489


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Your best accessory is your stunning smile, but this vivid cap from South African style supplier Babatunde is a close second. Combining the all-American ball cap with bright and bold Dutch wax cloth, this head-turning head topper is guaranteed to get passersby grinning.
Product photos do not represent the exact pattern positioning due to the handmade process.
Size S diameter: 22".
Size M diameter: 22.5".
Size L diameter: 23.25".
Size XL diameter: 24".
Read more about Babatunde and designer Gareth Crowden on our blog.
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Some product exclusions may apply. See product page details for more information. Clearance items are final sale. Price matching does not apply to this or any promotion. Offer is valid until September 13 at 11:59 PM PST. May not be combined with any other offer/discount (unless specified), including invitation credits, promotional gift cards and/or special discount links. Discount applied upon checkout. Log in to your account to see your savings.
Heron and Lamb puts the spotlight on Mother Nature with its self-contained terrariums that can be worn anytime, anywhere. Incorporating antiqued elements and plant specimens, these beautiful adornments are organically enchanting. The Lichen Terrarium Studs feature glass cabochon pendants that are framed in a hand-forged bezel and contain preserved tree lichen.
Stud diameter: 0.75". Copper ear wire.
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The Aqua Zinger is a stainless steel, double-walled beverage container with a bottom-mounted grinder cup designed to add instant zest to your drink (not to mention a nutritional bump). To get fortifying, simply place fresh ingredients—like fruits, vegetables, and herbs—in the grinder cup, and allow the extracted flavors to travel through a stainless steel screen and into your newly revamped beverage.
Capacity: 20 oz. All natural nutrients, flavors, and aromas are released into the water, while any pulp and rinds are kept at the bottom for further steeping.
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Adam Wolf
He and others on this page were in Peter Asturgus' Company of Kentucky County Militia, under the command of General Rogers Clark. July 18, 1780 - Aug 21, 1780 on an expedition against the Shawnee. Adam is listed as "absent" on the payroll record.
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