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By: Joseph Miller
Brazil's richest citizen, Jorge Paulo Lemann, may not be a household name in America yet, but the brands he controls are icons of U.S. consumer culture around the world. With his two longtime partners Marcel Telles and Carlos Sicupira, Lemann runs the makers of Budweiser beer, the Whopper and even Heinz ketchup. The combined market value of the companies they run is $187 billion—larger than that of Citigroup.
Lemann made international headlines when he partnered with Warren Buffet in the largest deal in the food industry's history, the $23 billion acquisition of Heinz, the 144-year-old American iconic brand.
Buffet is excited to partner with Lemann, "this is my kind of deal and my kind of partner… I want to learn more about Brazil and Jorge [Paulo Lemann] is a great professor," said the Oracle of Omaha.
Worth an estimated $17.8 billion as of FORBES' last World's Billionaires list, Lemann come up with the idea to buy Heinz and brought it up to Buffett. Longtime friends, the two first met a decade ago when both served on the board of Gillette. Buffett was on the board because Berkshire then owned close to 10% of Gillette. Lemann's primary business affiliation at the time was with the Brazilian brewery AmBev.
With Lemann leading the drive, AmBev merged with Belgium's Interbrew to form InBev. Few years late, he made headlines when Inbev acquired Anheuser-Busch (Budweiser´s maker) for $52 billion. The amalgamation of all of these companies formed Anheuser-Busch InBev BUD, the world's largest brewer, in which the billionaire holds a 10% stake and remains a powerful force. Berkshire Hathaway also held a significant stake in Anheuser-Busch before InBev's takeover bid, which Buffett regrettably believed would fail.
In 2004, Lemann and his partners founded the company 3G Capital in New York to buy U.S. companies with the cash they would earned from more than two decades of takeovers and turnarounds in Brazil.
Lemann´s appetite for American companies emerged once again in 2010, when he, Telles and Sicupira, through 3G Capital, bought Burger King Holdings for $3.3 billion. The chain, fortunate to say, is a large user of Heinz ketchup.
Beyond that cameo example of good judgment, the Lemann troops are known for their tight control of costs and for their focus on creating long-term value. Buffett is in general a great admirer of how his friend — "Georgie Paulo," as Buffett calls him — manages his businesses.
At Berkshire Hathaway's (BRK/A) annual shareholders' meeting, Buffett explained his confidence in 3G's 74-year-old chief investor and strategist. He called Lemann "classy." As a sign of Buffett's esteem, 3G and Berkshire have equal stakes in Heinz despite Berkshire putting up three times as much cash. Heinz, Buffett said, would be Lemann's show.
The company's shares ceased trading and Heinz officially went private, hint at the sort of shake-ups that Lemann and his partners are specialize.
Their first move was to replace Heinz's long-serving chief executive officer, William Johnson, with Bernardo Hees, a former Brazilian railroad executive who had most recently run Burger King (BKW), where is forbidden to make color copies without permission. At Anheuser, employees no longer have access to free beer. According to a book published in Brazil this year, Sonho Grande (Big Dream), Lemann's partner Sicupira has a favorite phrase: "Costs are like fingernails: You have to cut them constantly."
The formula has done wonders for the profitability of Anheuser-Busch InBev (Budweiser); its stock price has increased about 150 percent in the past five years. In two years, Burger King doubled its margins as measured by Ebitda and is already value at more than twice the $3.3 billion Lemann bought it for in 2010.
Lemann is No. 32 on the Bloomberg Billionaires Index, seven slots behind George Soros and three ahead of Carl Icahn. The Harvard-educated former tennis player, who once played in the Wimbledon championship tournament, has become a major player on the global mergers-and-acquisition stage, something that also reflects the rise of Brazil as an economic power. People in the U.S. are moved by the American dream, but Lemann as soon as he fulfills one dream; he is on to the next.
According to Lemann´s biography book – Big Dream, there is a reason why Lemann and Buffett are becoming even closer and start doing more business together: they could be considering the takeover of the most iconic of American brands, none other than Coca-Cola. In which Buffett's Berkshire Hathaway is already the largest shareholder, with a 9% stake. In Berkshire Hathaway's annual report, Buffet said, "With the Heinz purchase, moreover, we created a partnership template that may be used by Berkshire in future acquisitions of size."
Considering the two men's reputations and respective histories, it is fair to say that The Oracle of Omaha may indeed have found his soul mate in business, since they share the same simple values and are not afraid of dreaming big.
Joseph Miller is a freelance writer with experience in reporting about finance/business, economy and entrepreneurship


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Radar Beep is a highly recommendable app for Android in français to help you fully benefit from your device. Developed by Radar Beep, it's an app that is -1% sûr as verified by the complete report by Virus Total. Just like other similar apps such as Apk GbWhatsapp for Android, Android Auto, Google Earth, Waze, Google Maps, MAPS.ME Lite, Radar Beep packs a number of features into its 8.57MB in comparison with the average app size of 10.18MB in its category. Additionally, the app can be used in Android devices with the version 4.0.3, 4.0.4 or higher. Radar Beep has a total of 12,599 downloads on Uptodown, currently. Furthermore, in the latest version, 18.104.22.168, uploaded on 19.07.18, the program includes improvements and slight error corrections.
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subset: commoncrawl


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Why Bailout? - Emergency Economic Summit (11 of 14)
On September 30, 2008, the Seidman College of Business hosted an emergency summit at Grand Valley State University to explore the intricacies of the global financial crisis in the United States and in West Michigan. "Historic Financial Crisis: A West Michigan Conversation," included panel presentations from and discussion between six academics and practitioners.
Length: 06:32


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For more details
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This short film presents the first striking stages of Regis Loisel' s work 'Peter Pan' where he puts forward his dark vision of the world in the way of a fairy tale for adults. The film displays Peter as a child, years before Sir James Matthew Barrie's one. We all know that Peter Pan is an eternal child who hates adults and whose sworn enemy is captain Crochet. But how did he come to this ? London in 1887 hunger coldness and poverty provide the setting.
Brought up by a tyrannical and alcoholic mother Peter finds his salute only in the dreams and tales which free his imagination. One night, after a violent quarrel with his mother he takes refuge on the docks. There a small and attractive fairy who needs help will take him to the imaginary country.
Thus begins the fabulous story of Peter a child from the poor area of White-Chapel who will later become Peter Pan. He will have to face the awful captain Hook in an everlasting fight for the treasure and survival of the island.


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Soapy love, that is! Back in the saddle for some soap crafting! I've been slowly coming back to it and bought some new soapy supplies recently.
Can you guess what I'm making here? Hint: it's not what you think.
Speaking of love, the month of LUV is coming up. In celebration I'm making these candy-scented exfoliating soaps made with real sugar. It's sweet upon sweet. Here's before the pour. After pics to come.
And if that weren't enough, I played around and make a rice flower scented soap. Since I couldn't decide what to make it look like, I opted for simple and clean, just like the fragrance.


text: Start collecting Provenance1917 gift of Denman Waldo RossCredit LineDenman Waldo Ross Collection Shinsho ryuzo Japanese, Kamakura period, 14th century Catalogue Raisonné Kajima Volume 1: Chapter 2, pg. 41: no. 51 Dimensions approximately 20 cm (7 7/8 in.) (height of each figure) Accession Number 17.1017 Medium or Technique Japanese cypress CollectionsAsiaClassificationsSculpture


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Emergent Biosolutions (EBS) Jumps 6.12% on January 20Equities Staff Follow |
Emergent Biosolutions (EBS) was among the biggest gainers on the Russell 2000 for Wednesday January 20 as the stock popped 6.12% to $35.02, representing a gain of $2.02 per share. Some 349,307 shares traded hands on 3,370 trades, compared with an average daily volume of 353,526 shares out of a total float of 38.94 million. The stock opened at $32.65 and traded with an intraday range of $35.16 to $32.13.
After today's gains, Emergent Biosolutions reached a market cap of $1.36 billion. Emergent Biosolutions has had a trading range between $40.49 and $26.36 over the last year, and it had a 50-day SMA of $37.07 and a 200-day SMA of $33.14.
The stock has a P/E Ratio of 26.4.
Emergent BioSolutions Inc is a specialty biopharmaceutical company operating in two divisions: Biodefense and Biosciences. Its products include BioThrax, BAT, Anthrasil, VIGIV, RSDL, NuThrax, PreviThrax and GC-072.
Emergent Biosolutions is based out of Gaithersburg, MD and has some 1,280 employees. Its CEO is Daniel J. Abdun-Nabi.
For a complete fundamental analysis analysis of Emergent Biosolutions, check out Equities.com's Stock Valuation Analysis report for EBS. To see the latest independent stock recommendations from Equities.com's analysts, visit our Research section.
The Russell 2000 is one of the leading indices tracking small-cap companies in the United States. It's maintained by Russell Investments, an industry leader in creating and maintaining indices, and consists of the smallest 2000 stocks from the broader Russell 3000 index.
Russell's indices differ from traditional indices like the Dow Jones Industrial Average (DJIA) or S&P 500, whose members are selected by committee, because they base membership entirely on an objective, rules based methodology. The 3,000 largest companies by market cap make up the Russell 3000, with the 2,000 smaller companies making up the Russell 2000. It's a simple approach that gives a broad, unbiased look at the small-cap market as a whole.
For more news on the financial markets, go to Equities.com. Also, learn more about our independent proprietary equity research reports and our robust do-it-yourself Stock Valuation Analysis reports in our Research section.
All data provided by QuoteMedia and was accurate as of 4:30PM ET.
DISCLOSURE: The views and opinions expressed in this article are those of the authors, and do not represent the views of equities.com. Readers should not consider statements made by the author as formal recommendations and should consult their financial advisor before making any investment decisions. To read our full disclosure, please go to: http://www.equities.com/disclaimer


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Today's Expert Advice is a DIY tutorial on how to create these pretty baby's breath ornaments!
|clouds of baby's breath
Step One: Soak Oasis floral balls in water and then attach fishing line that is strong enough to hold the water weight of the soaked sphere. Mark your fishing line with tape, so it doesn't get lost amongst the clouds of baby's breath.
Step Three: Gather several stems together
Voila! Aren't they adorable?


text: MUSIC Today: The Bethel College Band and String Chamber Orchestra perform at 7:30 p.m. in Everest-Rohrer Chapel/Fine Arts Auditorium, Bethel College, 1001 W. McKinley Ave., Mishawaka. For more information, call (574) 257-3393. MUSIC Tuesday: The Avalon String Studio performs at 8 p.m. in Recital Hall, Indiana University South Bend. For more information, call (574) 520-4203. TV Today: Dougray Scott replaces Charlton Heston in a new version of "The Ten Commandments" (9 p.m., ABC).


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What do you get when you pit the inebriated minds that brought us Drunkgamers.com against half of IGN AU's Pubcast team? Probably a dubious carpet stain or two and a series of lawsuits just waiting to happen, but also the second episode in our series of Pubcast Happy Hour specials. What is the Pubcast Happy Hour? Well, just in case you missed our first episode with Naughty Dog's Ricky Cambier, these special editions of the IGN AU Pubcast allow us to invite a special guest (or guests) to come and hang with us, talk about their passions and hopefully down enough beers to forget that we're actually recording the whole thing.
This time around we catch up with Rooster Teeth's own Burnie Burns and Jack Pattillo - part of the team behind massively popular series' like Red vs. Blue and Achievement Hunter - to discuss how they built an empire in online video production, to decide which types of wounds are appropriate to piss on, and to find out what the latest is with Burnie's ongoing fued with the town of Burnie, Tasmania.
Alongside Pubcast regulars Tristan, Cam and Baker, Burnie and Jack also talk Xbox One vs PS4, explain why the only thing deadlier than Australian spiders are Australian gamers, and take time to answer reader questions from our Facebook page. Take a large cock bite out of this!
Download Pubcast Happy Hour 2!
And don't forget you can check out (almost) all the previous episodes of the regular Pubcast on iTunes. And hey, why not subscribe while you're there? Also! Come join the IGN Australia Facebook community.


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A New Compressive Imaging Camera Architecture using Optical-Domain Compression
- Dustin Bradford
- 3 years ago
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1 A New Compressive Imaging Camera Architecture using Optical-Domain Compression Dharmpal Takhar, Jason N. Laska, Michael B. Wakin, Marco F. Duarte, Dror Baron Shriram Sarvotham, Kevin F. Kelly, Richard G. Baraniuk Department of Electrical and Computer Engineering Rice University ABSTRACT Compressive Sensing is an emerging field based on the revelation that a small number of linear projections of a compressible signal contain enough information for reconstruction and processing. It has many promising implications and enables the design of new kinds of Compressive Imaging systems and cameras. In this paper, we develop a new camera architecture that employs a digital micromirror array to perform optical calculations of linear projections of an image onto pseudorandom binary patterns. Its hallmarks include the ability to obtain an image with a single detection element while sampling the image fewer times than the number of pixels. Other attractive properties include its universality, robustness, scalability, progressivity, and computational asymmetry. The most intriguing feature of the system is that, since it relies on a single photon detector, it can be adapted to image at wavelengths that are currently impossible with conventional CCD and CMOS imagers. Keywords: Compressed sensing, sparsity, incoherent projections, random matrices, linear programming, imaging, camera 1. INTRODUCTION Imaging sensors, hardware, and algorithms are under increasing pressure to accommodate ever larger and higherdimensional data sets; ever faster capture, sampling, and processing rates; ever lower power consumption; communication over ever more difficult channels; and radically new sensing modalities. Fortunately, over the past few decades, there has been an enormous increase in computational power and data storage capacity, which provides a new angle to tackle these challenges. We could be on the verge of moving from a digital signal processing (DSP) paradigm, where analog signals (including light fields) are sampled periodically to create their digital counterparts for processing, to a computational signal processing (CSP) paradigm, where analog signals are converted directly to any of a number of intermediate, condensed representations for processing using various nonlinear techniques Compressive sensing CSP builds upon a core tenet of signal processing and information theory: that signals, images, and other data often contain some type of structure that enables intelligent representation and processing. The notion of structure has been characterized and exploited in a variety of ways for a variety of purposes. Current state-of-the-art compression algorithms employ a decorrelating transform to compact a correlated signal s energy into just a few essential coefficients. 1 3 Such transform coders exploit the fact that many signals have a sparse representation in terms of some basis, meaning that a small number K of adaptively chosen transform coefficients can be transmitted or stored rather than N K signal samples. For example, smooth images are sparse in the Fourier basis, and piecewise smooth images are sparse in a wavelet basis; 4 the commercial coding standards JPEG 5 and JPEG directly exploit this sparsity. The standard procedure for transform coding of sparse signals is to (i) acquire the full N-sample signal x; (ii) compute the complete set of transform coefficients; (iii) locate the K largest, significant coefficients and discard the (many) small coefficients; and (iv) encode the values and locations of the largest coefficients. In cases where N is large and K is small, this procedure can be quite inefficient. Much of the output of the analog-to-digital conversion process ends up being discarded (though it is not known a priori which pieces are needed). Arguably, this sample first, ask questions later Supported by NSF, ONR, AFOSR, and the Texas Instruments Leadership University Program. {kaka, laska, wakin, duarte, drorb, shri, kkelly, Web: dsp.rice.edu/cs. Copyright 2006 Society of Photo-Optical Instrumentation Engineers. This paper was published in Proc. Computational Imaging IV and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
2 process places unnecessary demands on DSP systems, particularly imaging systems where each digital sample requires its own imaging sensor (pixel). This raises a simple question: For a given signal, is it possible to directly estimate the set of large coefficients that will not be discarded by the transform coder? While this seems improbable, the recent theory of Compressive Sensing (also known as Compressed Sensing, or CS) introduced by Candès, Romberg, and Tao 7,8 and Donoho 9 demonstrates that a signal that is K-sparse in one basis (call it the sparsity basis) can be recovered from ck nonadaptive linear projections onto a second basis (call it the measurement basis) that is incoherent with the first, where where c is a small oversampling constant. While the measurement process is linear, the reconstruction process is decidedly nonlinear Compressive imaging A critical aspect of CS measurements is multiplexing: each measurement is a function of several of the signal samples or image pixels. From this reduced set of measurements, it can still be possible (using CS techniques) to extract the salient signal information. This principle of sample less, compute later shifts the technological burden from the sensor to the processing. Thus, CS is an enabling framework for the CSP paradigm. In this paper, we develop a new system to support what can be called Compressive Imaging (CI). Our system incorporates a microcontrolled mirror array driven by pseudorandom and other measurement bases and a single or multiple photodiode optical sensor. This hardware optically computes incoherent image measurements as dictated by the CS theory; we then apply CS reconstruction algorithms described below to obtain the acquired images. Our imaging system enjoys a number of desirable features: Single detector: By time multiplexing a single detector, we can use a less expensive and yet more sensitive photon detector. This is particularly important when the detector is expensive, making an N-pixel array prohibitive. A single detector camera can also be adapted to image at wavelengths that are currently impossible with conventional CCD and CMOS imagers. Universality: Random and pseudorandom measurement bases are universal in the sense that they can be paired with any sparse basis. This allows exactly the same encoding strategy to be applied in a variety of different sensing environments; knowledge of the nuances of the environment are needed only at the decoder. Random measurements are also future-proof: if future research in image processing yields a better sparsity-inducing basis, then the same set of random measurements can be used to reconstruct an even better quality image. Encryption: A pseudorandom basis can be generated using a simple algorithm according to a random seed. Such encoding effectively implements a form of encryption: the randomized measurements will themselves resemble noise and be meaningless to an observer who does not know the associated seed. Robustness and progressivity: Random coding is robust in that the randomized measurements have equal priority, unlike the Fourier or wavelet coefficients in current transform coders. Thus they allow a progressively better reconstruction of the data as more measurements are obtained; one or more measurements can also be lost without corrupting the entire reconstruction. Scalability: We can adaptively select how many measurements to compute in order to trade off the amount of compression of the acquired image versus acquisition time; in contrast, conventional cameras trade off resolution versus the number of pixel sensors. Computational asymmetry: Finally, CI places most of its computational complexity in the decoder, which will often have more substantial computational resources than the encoder/imager. The encoder is very simple; it merely computes incoherent projections and makes no decisions. This paper is organized as follows. Section 2 provides a brief overview of the CS theory. Section 3 outlines our proposed CI system. Section 4 presents preliminary experimental results, and Section 5 concludes.
3 2.1. Sparse representations 2. COMPRESSIVE SENSING BACKGROUND Consider a length-n, real-valued signal x of any dimension indexed as x(n), n {1, 2,..., N}. For two-dimensional (2D) images we simply choose a 1D ordering of the N pixels. We use the terms signal and image interchangeably below. Suppose that the basis Ψ = [ψ 1,..., ψ N ] provides a K-sparse representation of x; that is x = N K θ(n)ψ n = θ(n l )ψ nl. (1) n=1 Here x is a linear combination of K vectors chosen from Ψ, {n l } are the indices of those vectors, and {θ(n)} are the coefficients; the concept is extendable to tight frames. 9 Alternatively, we can write in matrix notation l=1 x = Ψθ, (2) where x is an N 1 column vector, the sparse basis matrix Ψ is N N with the basis vectors ψ n as columns, and θ is an N 1 column vector with K nonzero elements. Using p to denote the l p norm, we can write that θ 0 = K. Various expansions, including wavelets, 4 Gabor bases, 4 and curvelets 10 are widely used for representation and compression of natural images and other data. For the moment, we will focus on exactly K-sparse signals and defer discussion of the more general situation where the coefficients decay rapidly but not to zero. Similar principles hold for compressible signals that are well-approximated using K terms, and this robustness is critical to the success of our imaging system Incoherent projections In CS, we do not measure or encode the K significant θ(n) directly. Rather, we measure and encode M < N projections y(m) = x, φ T m of the signal onto a second set of basis functions {φ m}, m {1, 2,..., M}, where φ T m denotes the transpose of φ m and, denotes the inner product. In matrix notation, we measure y = Φx, (3) where y is an M 1 column vector, and the measurement basis matrix Φ is M N with each row a basis vector φ m. Since M < N, recovery of the signal x from the measurements y is ill-posed in general; however the additional assumption of signal sparsity makes recovery possible and practical. The CS theory tells us that when certain conditions hold, namely that the basis {φ m } cannot sparsely represent the elements of the basis {ψ n } (a condition known as incoherence of the two bases 7 9, 11 ) and the number of measurements M is large enough, then it is indeed possible to recover the set of large {θ(n)} (and thus the signal x) from a similarly sized set of measurements {y(m)}. This incoherence property holds for many pairs of bases, including for example, delta spikes and the sine waves of the Fourier basis, or the Fourier basis and wavelets. Significantly, this incoherence also holds with high probability between an arbitrary fixed basis and a randomly generated one (consisting of i.i.d. Gaussian or Bernoulli/Rademacher ±1 vectors). Signals that are sparsely represented in frames or unions of bases can be recovered from incoherent measurements in the same fashion Signal recovery via l 0 optimization The recovery of the sparse set of significant coefficients {θ(n)} can be achieved using optimization by searching for the signal with l 0 -sparsest coefficients {θ(n)} that agrees with the M observed measurements in y (recall that M < N). Reconstruction relies on the key observation that, given some technical conditions on Φ and Ψ, the coefficient vector θ is the solution to the l 0 minimization θ = arg min θ 0 s.t. y = ΦΨθ (4) with overwhelming probability. (Thanks to the incoherence between the two bases, if the original signal is sparse in the θ coefficients, then no other set of sparse signal coefficients θ can yield the same projections y.) The l 0 norm θ 0 merely counts the number of nonzero entries in the vector θ.
4 In principle, remarkably few incoherent measurements are required to recover a K-sparse signal via l 0 minimization. Clearly, more than K measurements must be taken to avoid ambiguity; it has been established that K + 1 random measurements will suffice. 12,13 Unfortunately, solving this l 0 optimization problem is prohibitively complex, requiring a combinatorial enumeration of the ( N K) possible sparse subspaces. In fact, the l0 -recovery problem is known to be NP-complete. 14 Yet another challenge is robustness, since the recovery may be very poorly conditioned. In fact, both of these considerations (computational complexity and robustness) can be addressed, but at the expense of slightly more measurements Signal recovery via l 1 optimization The practical revelation that supports the new CS theory is that it is not necessary to solve the l 0 -minimization problem to recover the set of significant {θ(n)}. In fact, a much easier problem yields an equivalent solution (thanks again to the incoherency of the bases); we need only solve for the l 1 -sparsest coefficients θ that agree with the measurements y 7 9,15 19 θ = argmin θ 1 s.t. y = ΦΨθ. (5) This optimization problem, also known as Basis Pursuit, 20 is significantly more approachable and can be solved with traditional linear programming techniques whose computational complexities are polynomial in N. There is no free lunch, however; according to the theory, more than K +1 measurements are required to recover sparse signals via Basis Pursuit. Instead, one typically requires M ck measurements, where c > 1 is an oversampling factor. As an example, for Gaussian random matrices, the rule of thumb c log 2 ( N K + 1) provides a convenient approximation to the required oversampling factor Signal recovery via greedy pursuit At the expense of slightly more measurements, iterative greedy algorithms have also been developed to recover the signal x from the measurements y. Examples include the iterative Orthogonal Matching Pursuit (OMP), 11 matching pursuit (MP), 21 and tree matching pursuit (TMP) 21,22 algorithms. OMP, for example, iteratively selects the vectors from ΦΨ that contain most of the energy of the measurement vector y. The selection at each iteration is made based on inner products between the columns of ΦΨ and a residual; the residual reflects the component of y that is orthogonal to the previously selected columns. OMP is guaranteed to converge within a finite number of iterations. In CS applications, OMP requires c 2 ln(n) to succeed with high probability. 11 TMP algorithms are especially well suited for reconstructing natural images. This class of algorithms exploits the structure of images that are sparse in a wavelet (or curvelet) basis; the wavelet basis functions can be grouped by scale and sorted in each group by offset. Two wavelet functions in consecutive scales are said to be linked by a father-child relationship when the support of the coarser-scale wavelet completely contains that of the finer-scale wavelet. For 2D signals such as images, each parent wavelet is linked to four children wavelets; the graph described by these links is a quad-tree. Thanks to the singularity analysis properties of wavelets, wavelet coefficient values tend to propagate through scales. A large wavelet coefficient (in magnitude) generally indicates the presence of a singularity inside its support; a small wavelet coefficient generally indicates a smooth region. Thanks to the nesting of child wavelets inside their parents, edges manifest themselves in the wavelet domain as chains of large coefficients propagating through the scales of the wavelet quad-tree. Wavelet coefficients also have decaying magnitudes at finer scales. 4 This induces the large wavelet coefficients of piecewise smooth images to form a connected subtree within the wavelet quad-tree. Since greedy algorithms select the dictionary vector that explains the most energy from the image, it turns out to be unnecessary to check all possible coefficients at each iteration. Rather, the next most significant coefficient at each stage is likely to be among the children of the currently selected coefficients. By limiting the greedy algorithm to search only among these elements of the dictionary, the computational complexity of the TMP algorithm is significantly reduced compared to the original MP algorithm. 21 Additionally, by considering the contribution of all the elements of the chain of coefficients anchored at the root of the quad-tree, the salient edge features of the signal under consideration can reconstructed with higher priority and accuracy by the TMP algorithm. 22 While requiring a similar order of computation to linear programming-based reconstruction algorithms, greedy algorithms are often significantly faster in practice.
5 Figure 1. Compressive Imaging (CI) camera block diagram. Incident lightfield (corresponding to the desired image x) is reflected off a digital micro-mirror device (DMD) array whose mirror orientations are modulated in the pseudorandom pattern supplied by the random number generators (RNG). Each different mirror pattern produces a voltage at the single photodiode (PD) that corresponds to one measurement y(m). From M measurements y we can reconstruct a sparse approximation to the desired image x using CS techniques Other signal recovery algorithms The introduction of CS has inspired new measurement schemes and fast reconstruction algorithms. For instance, Ref. 23 employs group testing and random subset selection to estimate the locations and values of the nonzero coefficients in strictly sparse signals. The scheme provides a compact representation of the measurement basis as well as a fast O(NpolylogN) reconstruction algorithm that succeeds with high probability. Additionally, the complexity of the measurement calculation (3) is very low for signals that are sparse in the space domain. The algorithm can be extended to signals sparse in other bases and to compressible signals; however its main drawback is the large oversampling factor c required for high probability of successful reconstruction. Similar algorithms have been described elsewhere A COMPRESSIVE IMAGING TESTBED CS/CI principles enable the design of flexible new imaging devices and techniques. Our hardware realization of the CI concept is a single pixel camera; it combines a micro-controlled mirror array displaying a time sequence of M pseudorandom basis functions with a single optical sensor to compute incoherent image measurements y as in (3) (see Figure 1). By time multiplexing a single detector, 25 we can employ a less expensive and yet more sensitive photon sensor. We can also adaptively select how many measurements to compute in order to trade off the amount of compression of the acquired image versus acquisition time; in contrast, conventional cameras trade off resolution versus the number of pixel sensors Camera hardware Micro-actuated mirrors have proven to be a commercially viable MEMS technology for the video/projector display market as well as laser systems and telescope optics. 26 In our system, we employ a Texas Instruments (TI) digital micromirror device (DMD). The combination of a TI DMD developer s kit and accessory light modulator package (ALP) allows us to operate in the variety of modes necessary to test various CS approaches. The DMD consists of an array of electrostatically actuated micro-mirrors where each mirror the array is suspended above an individual SRAM cell. The DMD micro-mirrors form a pixel array of size Each mirror rotates about a hinge and can be positioned in one of two states (+12 degrees and 12 degrees from horizontal); thus light falling on the DMD may be reflected in two directions depending on the orientation of the mirrors. While the DMD was originally created for displaying images in televisions and digital projectors, other groups have recently begun to use it for enhanced image acquisition In our CI setup (see Figures 1 and 2), the desired image is formed on the DMD plane with the help of a biconvex lens; this image acts as an object for the second biconvex lens which focuses the image onto the photodiode. The light is collected from one out of the two directions in which it is reflected (e.g., the light reflected by mirrors in the +12 degree state). The light from a given configuration of the DMD mirrors is summed at the photodiode to yield an absolute voltage
6 Figure 2. Compressive Imaging (CI) camera hardware setup. that yields a coefficient y(m) for that configuration. The output is amplified through an op-amp circuit and then digitized by a 12-bit analog-to-digital converter Optical multiplexing The output voltage of the photodiode can be interpreted as the inner product of the desired image x with a measurement basis vector φ(m). In particular, letting ρ(m) denote the mirror positions of the m-th measurement pattern, the voltage reading from the photodiode v can be written as where v(m) x, φ(m) + DC offset, (6) φ(m) = 1 {ρ(m)=+12 degrees} (7) and 1 is the indicator function. (The DC offset can be measured by setting all mirrors to 12 degrees; it can then subtracted off.) Equation (6) holds the key for implementing a CI system. For a given image x, we take M measurements {y(1), y(2),...,y(m)} corresponding to mirror configurations {ρ(1)ρ(2)...,ρ(m)}. Since the patterns ρ(m) are programmable, we can select them to be incoherent with the sparsity-inducing basis (e.g., wavelets or curvelets). As mentioned previously, random or pseudorandom measurement patterns enjoy a useful universal incoherence property with any fixed basis, and so we employ pseudorandom ±12 degree patterns on the mirrors. These correspond to pseudorandom 0/1 Bernoulli measurement vectors φ m = 1 {ρ(m)=+12 degrees}. (The measurements may easily be converted to ±1 Rademacher patterns by setting all mirrors in ρ(1) to +12 degrees and then letting y(m) 2y(m) y(1) for m > 1.) Other options for incoherent CI mirror patterns include 1/0/1 group-testing patterns. 23,24 Mirrors can also be dutycycled to give the elements of φ finer precision, for example to approximate Gaussian measurement vectors. 8,9 This system directly acquires a reduced set of M incoherent projections of an N-pixel image x without first acquiring the N pixel values. The key idea is that each measurement multiplexes several pixel values, and CS methodologies can be used to tease them out. Since the camera is progressive, better quality images (larger K) can be obtained by taking more measurements M. Also, since the data measured by the camera is future-proof, new reconstruction algorithms based on better sparsifying image transforms can be applied at a later date to obtain even better quality images. We assume that the image x is stationary during the acquisition process. Currently, this is possible in controlled environments; ultimately the fast switching rates of the DMD will allow for applications in practical settings. In our setup, the number of reconstructed pixels (and thus the resolution of the final image) corresponds to the number of micromirrors in the DMD array N =
7 3.3. Related work Two notable previous DMD-driven applications involve confocal microscopy 31,32 and micro-optoelectromechanical (MOEM) systems The three primary differences between our CI/DMD camera and MOEM systems are the placement of the DMD between the image and the detector (as opposed to placement between the light source and the image); the replacement of the CCD detector with a single photodiode; and the large size of MOEM data sets. In a MOEM system, a DMD is positioned between the image source and the sensing element; its function is to limit the number of image pixel columns being sensed at a given time by reflecting off light from unwanted pixels and allowing light from the desired pixels to pass through. A MOEM system obtains the sum of the intensities of sets of columns from the sensed image; the purpose of multiplexing is to increase the signal-to-noise ratio of the measurements, which is lower when each column is sensed separately due to the low illumination caused by DMD modulation. In Refs the authors propose sets of N Hadamard patterns, which allows for simple demultiplexing, and randomized Hadamard patterns, which yield a uniform signal-tonoise ratio among the measurements. MOEM data sets are very large, requiring a large amount of storage and processing. In contrast, in our CI/DMD camera, compression in the optical domain greatly reduces the storage and processing of the image. Other efforts on CI include Refs. 33, 34, which employ optical elements to perform transform coding of multispectral images. These designs obtain sampled outputs that correspond to coded information of interest, such as the wavelength of a given light signal or the transform coefficients in a basis of interest. The elegant hardware designed for these purposes uses concepts that include optical projections, group testing, 23 and signal inference Hardware configuration 4. EXPERIMENTAL RESULTS In the optical setup depicted in Figure 2, the object is front illuminated by an LED light source. To minimize noise, the initial measurements have been performed by lock-in detection with the LED driven at 1kHz. For simplicity, the initial images are square in shape and therefore use only a array of pixels on the DMD. This array can be further sub-divided into blocks depending on the desired resolution for the reconstructed image, e.g., for a pixel image we actuate blocks of micro-mirrors in unison. Additionally, since the DMD array is programmed on the fly, it can be adaptively sectioned to highlight (zoom in on) part of the image (as opposed to acquiring the entire image and then digitally zooming through post-processing) or to adaptively modify the projection basis functions during acquisition for enhanced reconstruction performance Imaging results For our imaging experiment, we displayed a printout of the letter R in front of the camera; Figure 3(a) shows the printout. We set the camera to acquire a pixel image (hence, N = 4096). This size was chosen to ensure quick reconstruction during tests so that focusing and other adjustments could be made. The image quality benefits from the reduced resolution since activating the DMD in blocks of pixels reflects more light, thus reducing the sensitivity requirement on the photodetector and thus noise. As we discuss later, better hardware exists for some of the components, and higher resolution images will result. Since our test image is piecewise constant (with sharp edges) it can be sparsely represented in the wavelet domain. Figures 3(b) and 3(c) show the best K-term Haar wavelet approximation of the idealized image in Figure 3(a) with K = 400 and 675, respectively. Figure 3(d) shows the projection of the original test image onto the surface of the DMD; this image is a still frame from a video taken with a conventional digital camera, and the poor quality is due to the low resolution of the obtained NTSC signal with the video capture card and the low light level. Using M = 1600 and 2700 pseudorandom projections (roughly 4 the K used in (b) and (c)), we reconstructed the images shown in Figures 3(e) and 3(f) using Basis Pursuit. From these results, it is clear that the recognizable features of the R can be recovered, even with half as many measurements as the number of pixels acquired. The reconstruction quality is also progressively better with higher M as well as more robust to noisy measurements, enhancing the reconstruction of the singularities (sharp edges). The sources of noise include subtle nonlinearities in the photodiode, nonuniform reflectance of the mirrors through the lens that focuses
8 (a) ideal image (b) 400 largest wavelets (c) 675 largest wavelets (d) image on DMD (e) 1600 measurements (f) 2700 measurements Figure 3. CI/DMD imaging of a (N = 4096 pixel) image. Ideal image (a) of full resolution and approximated by its (b) largest 400 wavelet coefficients and (c) largest 675 wavelet coefficients. (d) Conventional camera image acquired at the DMD plane. CS reconstruction from (e) 1600 random measurements and (f) 2700 random measurements. In all cases, Haar wavelets were used for approximation or reconstruction. onto the photodiode (thus changing the weighting of the pattern blocks), and nonuniform mirror positions; work is ongoing to remove these sources of noise at the measurement setup. The robustness of the CS reconstruction will tend to suppress quantization noise from the A/D converter and photodiode circuit noise during detection. 5. DISCUSSION AND CONCLUSIONS In this paper, we have presented a first prototype imaging system that successfully employs compressive sensing (CS) principles. The camera has many attractive features, including simplicity, universality, robustness, and scalability, that should enable it to impact a variety of different applications. Another interesting and potentially useful practical feature of our system is that it off-loads processing from data collection into data reconstruction. Not only will this lower the complexity and power consumption of the device, but it will enable adaptive new measurements schemes. The most intriguing feature of the system is that, since it relies on a single photon detector, it can be adapted to image at wavelengths that are currently impossible with conventional CCD and CMOS imagers. In current research, we are developing sequences of projection functions that reduce the mechanical movement of the mirrors, designing measurement matrices with lower oversampling factors c, analyzing and characterizing the noise and resolution effects on reconstruction, and designing and implementing fast reconstruction algorithms. There are many possibilities for extensions to our system, including: Plenty of measurement bases can be implemented with the DMD beyond Rademacher and Gaussian vectors. Reconstruction in a curvelet frame 10 or joint wavelet/curvelet frame could provide higher-quality images due to their pumped-up sparsification cabilities. Color, multispectral, and hyperspectral imaging can be achieved using a more complex photon sensing element such as a dual sandwich photodetector or by combining multiple photodiodes.
9 We should be able to greatly reduce the size and complexity of the setup by replacing the DMD chip with a MEMSbased shutter array placed directly over the photodiode. 35 This will also enable imaging of wavelengths that poorly reflect from the aluminum micromirror array. We can implement super-resolution where multiple shifted offsets are incorporated to reconstruct images with subpixel resolution. 36 In our setup, the DMD can be mounted onto a micro/nano-positioning device to make measurements with different lateral translations. Video encoding using CI can be achieved in several ways. Spatiotemporal random patterns can be used to encode a clip of video that is sparse in a 3D wavelet/curvelet domain. Also, separate frames of video can be encoded independently and decoded jointly by exploiting the correlation between the acquired frames. 12 Preliminary experiments with 3D incoherent measurements appear promising. CS principles can be extended to multiple imagers acquiring correlated images as in a camera network 37,38 or lightfield/lumigraph capture system. 39 ACKNOWLEDGEMENTS Thanks to Texas Instruments for providing the TI DMD developer s kit and accessory light modulator package (ALP). Thanks also to Dave Brady and Dennis Healy for enlightening discussions. REFERENCES 1. R. A. DeVore, B. Jawerth, and B. J. Lucier, Image compression through wavelet transform coding, IEEE Trans. Inform. Theory, vol. 38, pp , Mar J. Shapiro, Embedded image coding using zerotrees of wavelet coefficients, IEEE Trans. Signal Processing, vol. 41, pp , Dec Z. Xiong, K. Ramchandran, and M. T. Orchard, Space-frequency quantization for wavelet image coding, IEEE Trans. Image Processing, vol. 6, no. 5, pp , S. Mallat, A Wavelet Tour of Signal Processing. San Diego: Academic Press, W. Pennebaker and J. Mitchell, JPEG: Still image data compression standard, Van Nostrand Reinhold, D. S. Taubman and M. W. Marcellin, JPEG 2000: Image Compression Fundamentals, Standards and Practice. Kluwer, E. Candès, J. Romberg, and T. Tao, Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Trans. Inform. Theory, Submitted. 8. E. Candès and T. Tao, Near optimal signal recovery from random projections and universal encoding strategies, IEEE Trans. Inform. Theory, Submitted. 9. D. Donoho, Compressed sensing, Preprint. 10. E. Candès and D. Donoho, Curvelets A surprisingly effective nonadaptive representation for objects with edges, Curves and Surfaces, J. Tropp and A. C. Gilbert, Signal recovery from partial information via orthogonal matching pursuit, Apr Preprint. 12. D. Baron, M. B. Wakin, M. F. Duarte, S. Sarvotham, and R. G. Baraniuk, Distributed compressed sensing, Preprint. 13. R. Venkataramani and Y. Bresler, Further results on spectrum blind sampling of 2D signals, in Proc. IEEE Int. Conf. Image Proc. (ICIP), vol. 2, (Chicago), Oct E. Candès and T. Tao, Error correction via linear programming, Found. of Comp. Math., Submitted. 15. E. Candès and J. Romberg, Quantitative robust uncertainty principles and optimally sparse decompositions, Found. of Comp. Math., Submitted. 16. E. Candès, J. Romberg, and T. Tao, Stable signal recovery from incomplete and inaccurate measurements, Comm. on Pure and Applied Math., Submitted. 17. E. Candès and T. Tao, Decoding by linear programming, IEEE Trans. Inform. Theory, vol. 51, pp , Dec E. Candès and J. Romberg, Practical signal recovery from random projections, IEEE Trans. Signal Processing, Submitted.
10 19. D. Donoho and Y. Tsaig, Extensions of compressed sensing, Preprint. 20. S. Chen, D. Donoho, and M. Saunders, Atomic decomposition by basis pursuit, SIAM J. on Sci. Comp., vol. 20, no. 1, pp , M. F. Duarte, M. B. Wakin, and R. G. Baraniuk, Fast reconstruction of piecewise smooth signals from random projections, in Proc. SPARS05, (Rennes, France), Nov C. La and M. N. Do, Signal reconstruction using sparse tree representation, in Proc. Wavelets XI at SPIE Optics and Photonics, (San Diego), Aug G. Cormode and S. Muthukrishnan, Towards an algorithmic theory of compressed sensing, DIMACS Tech. Report , September A. C. Gilbert, M. J. Strauss, J. Tropp, and R. Vershynin, Sublinear, small space approximation of compressible signals and uniform algorithmic embeddings, Preprint, November D. Brady, Multiplex sensors and the constant radiance theorem, Optics Letters, vol. 27, no. 1, pp , J. Sampsell, An overview of the digital micromirror device (DMD) and its application to projection displays, in 1993 SID International Symposium Digest of Technical Papers, vol. 24, p. 1012, R. A. DeVerse, R. R. Coifman, A. C. Coppi, W. G. Fateley, F. Geshwind, R. M. Hammaker, S. Valenti, and F. J. Warner, Application of spatial light modulators for new modalities in spectrometry and imaging, in Proc. SPIE, vol. 4959, pp , mmm82/hyperspectral.html. 28. G. L. Davis, M. Maggioni, F. J. Warner, F. B. Geshwind, A. C. Coppi, R. A. DeVerse, and R. Coifman, Hyperspectral analysis of normal and malignant colon tissue microarray sections using a novel DMDsystem, in NIH Optical Imaging Workshop (poster), mmm82/hyperspectral.html. 29. G. L. Davis, M. Maggioni, F. J. Warner, F. B. Geshwind, A. C. Coppi, R. A. DeVerse, and R. R.Coifman, Hyperspectral analysis of normal and malignant microarray tissue sections using a novel micro-optoelectrical-mechanical system, Mod. Pathol., vol. 17 Suppl. 1:358A, R. Muise and A. Mahalanobis, Target detection using integrated hyper spectral sensing and processing. IMA Workshop on Integration of Sensing and Processing, December P. M. Lane, R. P. Elliott, and C. E. MacAulay, Confocal microendoscopy with chromatic sectioning, in Proc. SPIE, vol. 4959, pp , V. Bansal, S. Patel, and P. Saggau, High-speed confocal laser scanning microscopy using acousto-optic deflectors and a digital micromirror device, in Proc. SPIE, vol. 5324, pp , N. P. Pitsianis, D. J. Brady, and X. Sun, Sensor-layer image compression based on the quantized cosine transform, in Visual Information Processing XIV, vol. 5817, (Orlando, FL, USA), p. 250, SPIE, D. J. Brady, M. Feldman, N. Pitsianis, J. P. Guo, A. Portnoy, and M. Fiddy, Compressive optical montage photography, in Photonic Devices and Algorithms for Computing VII, vol. 5907, (San Diego, CA, USA), p , SPIE, M. Pizzi, V. Koniachkine, N. Nieri, S. Sinesi, and P. Perlo, Electrostatically driven film light modulators for display applications, Microsystem Technologies - Micro- and Nanosystems-Information Storage and Processing Systems, vol. 10, pp , J. Tanida, T. Kumagai, K. Yamada, S. Miyatake, K. Ishida, T. Morimoto, N. Kondou, D. Miyazaki, and Y. Ichioka, Thin observation module by bound optics (TOMBO): Concept and experimental verification, Applied Optics, vol. 40, no. 11, pp , R. Wagner, R. G. Baraniuk, and R. Nowak, Distributed image compression for sensor networks using correspondence analysis and super-resolution, Proceedings of IEEE ICIP, vol. 1, pp , W. E. Mantzel, H. Choi, and R. G. Baraniuk, Distributed camera network localization, Proceedings of the 38th Asilomar Conference on Signals, Systems and Computers, vol. 2, pp , S. J. Gortler, R. Grzeszczuk, R. Szeliski, and M. F. Cohen, The lumigraph, in SIGGRAPH 96: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, (New York, NY, USA), pp , ACM Press, 1996.
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Survivor South Pacific – Iconic, Crazy or Just Plain Stupid?
There have been some iconic otherwise known as crazy moves on Survivor but Ozzy sending himself to Redemption Island is certainly up there. It is in the category when the ice cream scooper handed over his idol to Amanda who then turned around and voted him out.
After seeing Christina win at Redemption Island for the fifth straight time, Ozzy decided he should go there to knock her out just in case when they merge, she might stick with her original tribe.
However Christina sticking with her former tribe is unlikely, considering one of those tribe members was clearly barracking for Mikayla in the challenge.
Also he was going there on the assumption the merge was coming, and also if they merge occurred that the person on Redemption Island was going to be put back into the game. This may not happen.
Personally I think there are too many variables relying on outside forces to make it work. Also Ozzy actually has to beat Christina.
At the immunity challenge Ozzy's team lost because of Cochrane's stuff up, and at that time it was decided he should go to "redeem" himself. Cochran just had to sit there and cop it but as he said he wasn't very happy they were talking about voting him about, but also they were trying to make it a positive kumbaya thing.
Lucky for him Ozzy had a dream that he interpreted that he should go to Redemption Island. He handed over his idol to Cochran and went. It is a ballsy move, and if Ozzy does get back in the game he will have all of his team mates respect, and it could be a game winner for him.
Do you think Ozzy is stupid or brave?
Mikayla was knocked out of Redemption Island here is an exit interview with her at Examiner.
Related Posts
24 comments
- 1 Calliegirl { 11.02.11 at 8:12 am }
Thanks for the post RR. Just lurve this show – so many twists and turns. Still recovering from the gross 'meat eating' challenge!
- 2 brandy { 11.02.11 at 8:48 am }
Just plain stupid,with a very short attention span. Every dual at Redemption has involved Christina being surly towards her former tribe. she'd flip in an instant, there's no need to beat her to get the numbers, and she's a challenge monster. Plus it's all based on the assumption the merge is next. Stupid.
- 3 Wurstsemmel { 11.02.11 at 10:52 am }
Can't wait to see this decision bite him in the butt!
- 4 PinkPatentMaryJanes { 11.02.11 at 11:49 am }
Damn it – had a migraine last night and missed what sounds like an absolute RIPPER of a show. Thanks for the recap – now to track down the episode on-line.
Also, just a thought – what if they NEVER merge… there's a twist!
- 5 Cheryl { 11.02.11 at 12:15 pm }
@PPMJ – They tried not merging in Guatemala, it failed miserably when one team ended up with just one person (Stephanie) so they ended up having to merge – at which point she was promptly voted out by the other team.
Ozzy has never had much of a social game so of course he wouldn't have realised that Christine hates her old tribe for voting her out first. He is looking for "redemption" (god I hate that word as applied to Survivor now!) so he thinks that making a seemingly huge strategic move will gain him the respect his social skills haven't. What will actually happen if he does manage to win the RI challenge(s) and get back into the game is that people will see him as even more of a physical threat and he will be voted out for real.
As for the merge – they learnt from Fiji not to merge after the jury phase starts, they like even numbers at the merge and they usually have 7 jurors for a Final 3 so merging at 10 is pretty much the last minute. Merging at 12 is quite likely so it's not unreasonable to assume that they are just about to merge.
- 6 PollyB { 11.02.11 at 12:33 pm }
Nevermind Oz and his delusions, Coach is a complete tosser. I'm not religious, but spiritual, and the 'i am not worthy Father' act was so fucking staged. And then he practically drags people to their knees after the winning the challenge, before some even had removed their blindfolds. This line is so condescending to the viewer, however it is the usual US rant of the low brow. He and Hantz make a good pair.
I don't want Ozzie to win but for some reason Christine shits me more.
- 7 Chunks { 11.02.11 at 12:36 pm }
Insane. Coach, Ozzy and Brandon, what a lot of fruitloops. Most crazies ever. At this stage I think Coach is playing an incredibly controlled game and is on track to win – but I'll bet his ego and self-confidence get away with him and he ends up being blindsided.
- 9 Calliegirl { 11.02.11 at 1:42 pm }
I was disgusted when Coach yelled out "On your knees, on your knees', after they had won!! I was glad that nobody actually did kneel down, but they still had to suffer a group 'prayer'. Coach is just so offensive, manipulating 'his' team like that! It sickens me…
- 10 Calliegirl { 11.02.11 at 2:11 pm }
PinkPatentMaryJanes – Survivor is repeated each Sunday at 5.30pm on Go! In case you cannot find it on the Internet.
- 11 PinkPatentMaryJanes { 11.02.11 at 4:22 pm }
Oh Calliegirl I love you – thank you!
Cheryl I think I missed Guatemala – there was one series I missed due to shocking timing on Channel 9's part. Thanks for the heads-up.
- 12 JStar { 11.02.11 at 7:47 pm }
Hey RR. Ozzy was on the jury on Fans vs Favourites when Eric got played by the black widow brigade which resulted in that move being voted one of the top three dumbest moves ever on Survivor! So why on earth would he take such a risk in this game when he doesn't have such a strong core alliance? Christine was voted out first on her tribe and Albert was actively barracking for Mikayla at the last duel. Christine also flipped the bird at Mr Invisible (Rick) last time and constantly refuses to look at her former tribemates when she is at the duelling arena. Why on earth do they think she would go back to her former tribe if she gets back into the game? Is he that blind that he cannot read her body language? Like it has been said earlier, Ozzy has always lacked a social game (french kissing Amanda doesn't count). He doesn't take the time to read the dynamics in the game.
For this move to work, a lot hinges on the merge happening right after the next duel. Survivor has had a history of commencing the jury portion of the evictees prior to the merge when down to 12 players (the last time was on Heroes vs Villains when Coach and Courtney were booted off before the merge). Ozzy's plan is a risky, dumb move from someone who is not getting a "winner" edit. The second part of this equation relies on the fact all of Ozzy's tribemates will stay loyal to him.
My thoughts (and these are mine as the next episode has not aired yet in America – not spoilers, just speculation) is that judging by the title of the next episode – "Double Agent" – it will centre around Cochran. Survivor is well known for dropping hints in its editing on what to expect during the season. Richard Hatch declared he would win the million dollars in the first episode of Survivor and went on to win. Last season's edit of Matt made it so obvious that he would be the returnee from Redemption Island. Unfortunately, Christine (whom I like) is not getting nearly enough of that air time confessionals about "wanting to get back into the game." I think Ozzy is going to end up beating her.
Three things have stuck out for me thus far this season. Firstly, Coach saying in the first or second episode that this is the third time he has played this game and possibly the last one so he has to play smart. The fact that we see him so much (and his antics are so much more divisive than previously) makes me think he gets to the final five. Same with Brandon. The editors are really setting up the whole "redemption vibe" again through these two players finding redemption through god in their gameplay. Secondly, the constant reference by Coach trusting Edna and how he is a loyal man. You just know that some time down the track, Coach is going to stab her in the back and there will be tears but Coach will someone justify it in his head that he did the honourable thing.
Thirdly, Cochran has voiced several times during the previous episodes that he feels like an outsider in his tribe. He had to sit there whilst the others openly told him he would be the next to go but don't worry, "if you believe in yourself, you'll beat Christine." Then when Ozzy was explaining his "grand plan" to Jeff at Tribal Council, he said that he hoped Cochran would be the villain, the "double agent." And that folks, is the title of the next episode. The editors have provided all viewers with this major clue. My belief (noting the episode has not aired in America yet) is that I suspect Ozzy will beat Christine and a merge will immediately happen. However due to Ozzy's overacting (seriously, wasn't he just a little too earnest when telling Christine how he got voted out) I don't think the Upolu tribe will buy Ozzy's story. Recognising that Cochran is the true outsider of the Savaii tribe, Upolu (or most likely Coach) will make a deal with Cochran. Cochran will play both sides during the episode (hence the "double agent" title) but in the end will act like the "villain" and end up blindsiding his former tribemates and vote with Upolu in sending either Ozzy or Keith (back) to Redemption Island.
[One of two things will occur in a week's time. Either I can revel in smugness for being right or I'll ask RR to delete this post! ]
- 13 Izobel2 { 11.02.11 at 10:54 pm }
Wow JStar thanks for your post! Very interesting! You really know your stuff. You too should be known by your last name like all the true legends (like Donaldson, Mariano, and of course Cochran)!!!
Can I ask what the other 2 top dumbest moves are? (your 1st paragraph). Is it James getting blindsided with the 2 idols, and JT giving his idol to Evil Russell?
I'm really enjoying this season and I too can't believe Ozzy would do such a move, considering he has no idea if he'd even beat Christine or even if they are merging. And I too agree – why would Christine go back to her old tribe, she clearly doesn't enjoy them. And we saw loads of Matt at Redemption Island last season, and we've never seen one bit of what Christine's been up to. What on earth would she be doing every day… Thanks RR for the recap too.
PS where are you Sourkraut??
- 14 badguykris13 { 11.03.11 at 1:58 am }
very risky move indeed… Ozzy's fate in Redemption island will make him either a hero or a zero…
- 15 Dazza { 11.03.11 at 10:31 am }
So looking forward to dowlnloading todays episode and no I will not spoil
- 16 Susan { 11.03.11 at 12:22 pm }
Thanks JStar – you certainly know your game! I have watched all bar 3 of the 25 odd seasons of Survivor and can barely remember any of them!!! oh, good ole Twila does stick in the memory – poor luv!
I just cant bear the thought of Coach or Ozzy winning – ala Boston Rob from last season – have been backing Dawn for a bit but am also enjoying Jim's game too…………
Either way Christine or Ozzy coming back in the game, Upoloa should stay strong – the whole Cochrane 'double agent' will def come into play.
Can't get enough of this show and spewing we not fast tracked to the US more closely – I accidentally found out last seasons winner from a US gossip site – so may have to steer clear of that towards the end – BTW – does anyone know how many weeks we behind US?
- 17 TMC { 11.03.11 at 12:25 pm }
Ozzy move is so very Ballsy as you say RR, so Ballsy I would assume he has large bruises on this inner thighs.
Biggest move EVER
- 18 JStar { 11.03.11 at 12:29 pm }
There is something intrinsically sad Izobel2 that I know too much about Survivor. With the last name tag, maybe I'll just call myself Star on these posts from now on, particularly if I'm right! You also mentioned the "last name" for Cochran. He was shown in the first episode wanting to be known by his last name as iconic (male) players in Survivor are referred to by their last names. I just believe the editing is pointing towards Cochran being remembered in some iconic way for this season.
Two other dumb moves. Hard to say, really. I guess if I'm not counting "non-moves" as a move (i.e. how stupid were Rob's tribe last season not willing to vote out a power player, who had played three previous times, at any point?), I too would nominate JT giving an idol to Russell (someone he did not know prior to the game on the outside) based on an assumption that Parvati was using the same tricks and running and all-girl alliance. And to prove that Villains can be just as dumb as Heroes, I would nominate Tyson in not sticking to the agreed vote and effectively voting himself out of the game when Russell played the idol for Parvati and not himself (as Tyson assumed he would).
I too would love to know what Christine has been up to. Did she and Stacey make an effigy of "Benjamin" and toast it like a marshmallow? Who knows. We don't see enough of her bitterness but get plenty of scenes of Coach and Brandon forcing everyone to pray.
And Susan, thanks for the feedback. Loved Twila and Eliza's hate-hate relationship too. We are at the same point with the US but their latest episodes air (our time) Thursday afternoon. So don't go on any gossip site from today onwards to find out what happened!
- 19 Susan { 11.03.11 at 12:37 pm }
JStar – you rock!
Also have to say what a friggin hypocrite Coach is being too – hiding the idol and then lying to his tribe about it – makes me blood curl – hee hee!
- 20 Dazza { 11.03.11 at 1:47 pm }
Todays episode in US is played next Tuesday night, so about 4 days behind. Not bad for Channel 9
- 21 Izobel2 { 11.03.11 at 6:10 pm }
Hey 'Star' – I saw the beginning where Cochran asked to be referred to by his last name, he had me right there, I think he's kinda cool – in a nerd kinda way. I have another question for you – what's the scoop with the RIDICULOUS outfits in every season of Survivor?? Like who'd wear a suit and dress shoes, or a stupid dress?? Why not rock up with the camo gear in multi layers and good strong shoes etc? I've never understood it….
- 22 Susan { 11.03.11 at 10:39 pm }
Izobel2 – I saw in a few episodes ago they only got their bathers delivered – and some of them seem delighted they got their fave pair. I also wondered about the wrong choice of clothing they appear to be wearing – Whitney is sporting jeans/boots!
Maybe they have to just give up an array of their clothing and then the producers deem what 'suits' – but it would be interesting to find out how it all really works …….
- 23 JStar { 11.04.11 at 5:04 am }
Izobel2 and Susan – hmm…what do you mean ridiculous clothing? If it wasn't for those knee-high socks Sally ran around in every challenge during Exile Island, I wouldn't have remembered her at all. Wearing something ridiculous gets a contestant notice and possibly score some cheap Big W print modelling gig (yes, I'm looking at you Philip from Redemption Island and your horrible holey fuchsia y-fronts-of-horror that was thrusted on our television sets last season). I think in most cases, contestants can wear whatever outfit they choose as long as it doesn't contravene the show's sponsors (long gone are the good old early seasons when Reebok sponsored the show and supplied the clothing). But in seasons where teams are already pre-divided, sometimes they are told to wear a piece of clothing (that was non-bikini and non-underwear) that had a colour that would match their buffs (think Samoa with Russell and Shambo when one team had an item of clothing that was purple, whilst the other had an item of clothing that was yellow).
But it is also fair to say that some contestants are completely blindsided by the producers and are caught out with their clothing that they probably did not intend to play the game in. It started with Pearl Islands when the contestants were told they were getting their pre-show publicity photos done on a ship only for Jeff to appear and tell them that the game was actually starting now and to jump off the ship to reach their destination. One contestant was wearing an Armani suit, another one did not have a bra on. (This was the season prior to All-Stars and I heard that apparently in that latter season, many contestants were freaked out that the producers would do the same thing that they deliberately wore three layers of clothing for all pre-game publicity work just to be on the safe side). This trick caught out contestants in Gabon (three male contestants were wearing suits) and again at Nicaragua (think Dan and him walking in a jungle with $1,500 alligator-leather shoes).
Sigh, so much useless knowledge about Survivor. Okay, I confess. I AM Cochran! 😛
- 24 Susan { 11.04.11 at 3:14 pm }
JStar – I knew it!!!!
Yeah – remeber very well that one they all on the boat and then they had to ditch it into the ocean and last person to shore was penalised – if memery serves me right – ah the good ole days – I could ramble about this show forever!!!!!!!


text:
I'll get to the kids check ups in a minute, but first, we have some really exciting news – James got a new job!!! He starts mid-July and it means a lot of changes, but we'll talk about those once it's closer. Right now, we're sort of swirling from the news…but it's definitely exciting and very well deserved. Congratulations, James!!
The kids are doing great and are healthy. No shots this time around (no one is due until the age of 10) and just a finger poke for Conrad to check his iron levels.
Natalie's stats:
7 year old height: 47.25″ 7 year old weight: 51lbs
8 year old height: 49.5″ 8 year old weight: 63lbs
Conrad's stats:
4 year old height: 40.5″ 4 year old weight: 35lbs
5 year old height: 42.5″ 5 year old weight: 38lbs
Their pediatrician is happy with their growth projection and it was a good visit. We have noticed that with snacks being readily accessible, the kids will simply eat a ton of those, so this summer, we're starting to change up our snack options and require the kids to eat more vegetables with meals and more fruits for snacks. It's a healthier option for all of us anyways! It does require that I do more prep work, but we'll get into a groove. With the kids being older, it does make it easier for me to prep fresh instead of frozen.
Today we're all sort of tired from traveling yesterday, so it'll be a quiet day at home for us. Natalie's room is getting a mini redo, which I'll reveal later! Some cleaning and organizing and purging needs to happen first. Happy Friday!