Titlebook: Robust Subspace Estimation Using Low-Rank Optimization; Theory and Applicati Omar Oreifej,Mubarak Shah Book 2014 Springer International Pub

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Book 2014ns include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization p

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Background and Literature Review,rious low-rank formulations discussed in this book fall into several computer vision domains, we additionally review the latest techniques in each domain, including video denosing, turbulence mitigation, background subtraction, and activity recognition.

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Background and Literature Review,ly used. Consequently, we discuss the most prominent advances in low-rank optimization, which is the main theoretical topic of this book. Since the various low-rank formulations discussed in this book fall into several computer vision domains, we additionally review the latest techniques in each dom

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Simultaneous Turbulence Mitigation and Moving Object Detection,es for turbulence mitigation follow averaging or de-warping techniques. Although these methods can reduce the turbulence, they distort the independently moving objects which can often be of great interest. In this chapter, we address the problem of simultaneous turbulence mitigation and moving objec

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Complex Event Recognition Using Constrained Rank Optimization,a tire, making a fire …etc. In this extremely challenging task, concepts have recently shown a promising direction, where core low-level events referred to as concepts are annotated and modelled using a portion of the training data, then each complex event is described using concept scores, which ar