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Book synopsis

Recovering the low-rank structure of a linear subspace using a small set of corrupted examples has recently been made feasible through substantial advances in the area of matrix completion and nuclear-norm minimization. Such low-rank structures appear in certain conditions heavily in computer vision, for instance, in the frames of a video, the camera motion, and a picture of a building façade. In this book, we discuss several formulations and extensions of low-rank optimization, and demonstrate how recovering the underlying basis and detecting the corresponding outliers allow us to solve fundamental computer vision problems, including video denoising, background subtraction, action detection, and complex event recognition.§

Book details

Book category Books in English Computing & information technology Computer science Artificial intelligence

• Full title: Robust Subspace Estimation Using Low-Rank Optimization
• Author: Omar Oreifej, Mubarak Shah
• Language: English
• Binding: Hardback
• Number of pages: 114
• EAN: 9783319041834
• ISBN: 3319041835
• ID: 02300982
• Publisher: Springer International Publishing AG
• Weight: 344 g
• Dimensions: 237 × 162 × 13 mm
• Date of publishing: 25. March 2014

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