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Statistical inference / Statistical classification / Statistical models / Loss function / Parametric model / Maximum likelihood / Support vector machine / Expectation–maximization algorithm / Supervised learning / Statistics / Statistical theory / Estimation theory


Learning for Stereo Vision Using the Structured Support Vector Machine Yunpeng Li Daniel P. Huttenlocher Department of Computer Science, Cornell University Ithaca, NY 14853 {yuli,dph}@cs.cornell.edu
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Document Date: 2008-07-27 07:57:45


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Washington / DC / Springer-Verlag / /

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P. Corke / M. Bennamoun / /

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Jordan / United States / /

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energy minimization / inner product / lattice systems / stereo algorithms / stereo correspondence algorithms / approximate energy minimization / energy minimization techniques / pixel-based stereo algorithms / approximate solutions / energy minimization methods / learning algorithm / approximation algorithms / energy / be obtained using energy minimization techniques / /

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Congress / Structured Support Vector Machine Yunpeng Li Daniel P. Huttenlocher Department of Computer Science / National Science Foundation / IEEE Computer Society / Cornell University / /

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B. Taskar / V / Thorsten Joachims / O. Veksler / V / C. Strecha / R. Fransens / L. Van Gool / /

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C / /

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Illinois / New York / /

Technology

learning algorithm / dense two-frame stereo correspondence algorithms / approximation algorithms / machine learning / pixel-based stereo algorithms / stereo algorithms / /

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