Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing
暫譯: 穩健低秩與稀疏矩陣分解手冊：在影像與視頻處理中的應用
Bouwmans, Thierry, Aybat, Necdet Serhat, Zahzah, El-Hadi
- 出版商: CRC
- 出版日期: 2020-06-30
- 售價: $2,350
- 貴賓價: 9.5 折 $2,233
- 語言: 英文
- 頁數: 552
- 裝訂: Quality Paper - also called trade paper
- ISBN: 0367574780
- ISBN-13: 9780367574789
海外代購書籍(需單獨結帳)
商品描述
Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications. Incorporating both existing and new ideas, the book conveniently gives you one-stop access to a number of different decompositions, algorithms, implementations, and benchmarking techniques.
Divided into five parts, the book begins with an overall introduction to robust principal component analysis (PCA) via decomposition into low-rank and sparse matrices. The second part addresses robust matrix factorization/completion problems while the third part focuses on robust online subspace estimation, learning, and tracking. Covering applications in image and video processing, the fourth part discusses image analysis, image denoising, motion saliency detection, video coding, key frame extraction, and hyperspectral video processing. The final part presents resources and applications in background/foreground separation for video surveillance.
With contributions from leading teams around the world, this handbook provides a complete overview of the concepts, theories, algorithms, and applications related to robust low-rank and sparse matrix decompositions. It is designed for researchers, developers, and graduate students in computer vision, image and video processing, real-time architecture, machine learning, and data mining.
商品描述(中文翻譯)
《穩健低秩與稀疏矩陣分解手冊：在影像與視頻處理中的應用》展示了如何通過將數據分解為低秩和稀疏矩陣來進行穩健的子空間學習和追蹤，為計算機視覺應用提供合適的框架。這本書結合了現有的和新的想法，方便地為您提供多種不同的分解、算法、實現和基準技術的一站式訪問。
本書分為五個部分，首先對通過低秩和稀疏矩陣分解的穩健主成分分析（PCA）進行總體介紹。第二部分探討穩健的矩陣因式分解/補全問題，而第三部分則專注於穩健的在線子空間估計、學習和追蹤。第四部分涵蓋影像和視頻處理的應用，討論影像分析、影像去噪、運動顯著性檢測、視頻編碼、關鍵幀提取和高光譜視頻處理。最後一部分介紹了視頻監控中背景/前景分離的資源和應用。
本手冊匯集了來自全球領先團隊的貢獻，提供了有關穩健低秩和稀疏矩陣分解的概念、理論、算法和應用的完整概述。它旨在為計算機視覺、影像和視頻處理、實時架構、機器學習和數據挖掘的研究人員、開發人員和研究生提供參考。
作者簡介
Thierry Bouwmans is an associate professor at the University of La Rochelle. He is the author of more than 30 papers on background modeling and foreground detection and is the creator and administrator of the Background Subtraction website and DLAM website. He has also served as a reviewer for numerous international conferences and journals. His research interests focus on the detection of moving objects in challenging environments.
Necdet Serhat Aybat is an assistant professor in the Department of Industrial and Manufacturing Engineering at Pennsylvania State University. He received his PhD in operations research from Columbia University. His research focuses on developing fast first-order algorithms for large-scale convex optimization problems from diverse application areas, such as compressed sensing, matrix completion, convex regression, and distributed optimization.
El-hadi Zahzah is an associate professor at the University of La Rochelle. He is the author of more than 60 papers on fuzzy logic, expert systems, image analysis, spatio-temporal modeling, and background modeling and foreground detection. His research interests focus on the spatio-temporal relations and detection of moving objects in challenging environments.
作者簡介(中文翻譯)
Thierry Bouwmans 是法國拉羅謝爾大學的副教授。他是超過30篇有關背景建模和前景檢測的論文的作者，也是背景減除網站和DLAM網站的創建者及管理員。他還擔任過多個國際會議和期刊的審稿人。他的研究興趣集中在挑戰性環境中移動物體的檢測。
Necdet Serhat Aybat 是賓夕法尼亞州立大學工業與製造工程系的助理教授。他在哥倫比亞大學獲得運籌學博士學位。他的研究專注於為來自不同應用領域的大規模凸優化問題開發快速的一階算法，這些應用領域包括壓縮感知、矩陣補全、凸回歸和分散優化。
El-hadi Zahzah 是法國拉羅謝爾大學的副教授。他是超過60篇有關模糊邏輯、專家系統、圖像分析、時空建模以及背景建模和前景檢測的論文的作者。他的研究興趣集中在挑戰性環境中移動物體的時空關係和檢測。
