| 书目名称 | Robust Subspace Estimation Using Low-Rank Optimization |
| 副标题 | Theory and Applicati |
| 编辑 | Omar Oreifej,Mubarak Shah |
| 视频video | |
| 概述 | Provides a comprehensive summary of the state-of-the-art methods and applications of Low-Rank Optimization.Reviews the latest approaches in a wide range of computer vision problems, including: Scene R |
| 丛书名称 | The International Series in Video Computing |
| 图书封面 |  |
| 描述 | .Various fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications 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 problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book, the authors discuss fundamental formulations and extensions for low-rank optimization-based subspace estimation and representation. By minimizing the rank of the matrix containing observations drawn from images, the authors demonstrate how to solve four fundamental computer vision problems, including video denosing, background subtraction, motion estimation, and activity recognition.. |
| 出版日期 | Book 2014 |
| 关键词 | Activity recognition; complex event recognition; computer vision; image processing; low-rank optimizatio |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-04184-1 |
| isbn_softcover | 978-3-319-35248-0 |
| isbn_ebook | 978-3-319-04184-1Series ISSN 1571-5205 |
| issn_series | 1571-5205 |
| copyright | Springer International Publishing Switzerland 2014 |
发表于 2025-3-21 18:40:37
