This session extends the techniques to enable the analysis of large batches of visual data. We will show how tools and ideas from convex optimization give simple, robust algorithms for recovering low-rank matrices from incomplete, corrupted and noisy observations. Participants will learn how to identify problems for which these tools may be appropriate, and how to apply them effectively to solve practical problems such as robust batch image alignment and the detection of symmetric structures in images. We will illustrate the power and potential of these revolutionary tools in a wide range of applications in computer visions including Face and Text Recognition, Texture Repairing, Video Panorama, Camera Calibration, Holistic Reconstruction of Urban Scenes, etc. Finally, we will show generalizations to the problem of learning sparse codes for large sets of visual data, give example applications. Joint talk by Yi Ma and John Wright
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