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Lecture IV: A Bayesian Viewpoint on Sparse Models

Lecture IV: A Bayesian Viewpoint on Sparse Models

Lecture IV: A Bayesian Viewpoint on Sparse Models. Yi Ma John Wright Microsoft Research Asia Columbia University (Slides courtesy of David Wipf, MSRA). IPAM Computer Vision Summer School, 2013. Convex Approach to Sparse Inverse Problems.

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836 views • 71 slides
Lecture IV: A Bayesian Viewpoint on Sparse Models

Lecture IV: A Bayesian Viewpoint on Sparse Models

Lecture IV: A Bayesian Viewpoint on Sparse Models. Yi Ma John Wright Microsoft Research Asia Columbia University (Slides courtesy of David Wipf, MSRA). IPAM Computer Vision Summer School, 2013. Convex Approach to Sparse Inverse Problems.

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798 views • 71 slides
Research: Background & Plan

Research: Background & Plan

Research: Background & Plan. Presented by: Kourosh Meshgi Committee: Prof. Ishii Prof. Maeda Dr. Oba Presented as a part of fulfillment to be accepted as a member of Ishii Lab, Kyoto University. Content. 0. Background Vision in Human Brain Efficient Coding Theory Motivation

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596 views • 48 slides
A Database of Human Segmented Natural Images and Two Applications

A Database of Human Segmented Natural Images and Two Applications

A Database of Human Segmented Natural Images and Two Applications. David Martin, Charless Fowlkes, Doron Tal, Jitendra Malik UC Berkeley {dmartin,fowlkes,doron,malik}@eecs.berkeley.edu. Motivation. Berkeley Segmentation Dataset ? Groundtruth for image segmentation of natural images

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402 views • 33 slides
Research Methods

Research Methods

Research Methods. Lecturer: Steve Maybank Department of Computer Science and Information Systems sjmaybank@dcs.bbk.ac.uk Autumn 2013 Data Research Methods in Computer Vision. Digital Images. A digital image is a rectangular array of pixels. Each pixel has a position and a value.

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427 views • 27 slides
Algorithms and Applications in Computer Vision

Algorithms and Applications in Computer Vision

Algorithms and Applications in Computer Vision. Lihi Zelnik -Manor lihi@ee.technion.ac.il Lecture 5: Pyramids. Image information occurs at all spatial scales. Image pyramids. Gaussian pyramid Laplacian pyramid. The Gaussian pyramid. Smooth with gaussians, because

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346 views • 20 slides
Algorithms and Applications in Computer Vision

Algorithms and Applications in Computer Vision

Algorithms and Applications in Computer Vision. Lihi Zelnik -Manor lihi@ee.technion.ac.il Lecture 5: Pyramids. Image information occurs at all spatial scales. Image pyramids. Gaussian pyramid Laplacian pyramid. The Gaussian pyramid. Smooth with gaussians, because

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526 views • 20 slides
Bayesian Image Super-resolution, Continued

Bayesian Image Super-resolution, Continued

Best Huber-MAP reconstruction. Error compared to known ground truth image. RMSE = 15.5 grey levels. Best registration-integrating reconstruction. Note improvement of black-white edge detail in some letters on the bottom line. RMSE = 14.8 grey levels. image y (1).

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149 views • 1 slides

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