Central Library

Normal view MARC view ISBD view

Robust Subspace Estimation Using Low-Rank Optimization [electronic resource] : Theory and Applications / by Omar Oreifej, Mubarak Shah.

By: Oreifej, Omar [author.].
Contributor(s): Shah, Mubarak [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: The International Series in Video Computing: 12Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: VI, 114 p. 41 illus., 39 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319041841.Subject(s): Computer science | Computer graphics | Computer Science | Computer Imaging, Vision, Pattern Recognition and GraphicsAdditional physical formats: Printed edition:: No titleDDC classification: 006.6 Online resources: Click here to access online
Contents:
Introduction -- Background and Literature Review -- Seeing Through Water: Underwater Scene Reconstruction -- Simultaneous Turbulence Mitigation and Moving Object Detection -- Action Recognition by Motion Trajectory Decomposition -- Complex Event Recognition Using Constrained Rank Optimization -- Concluding Remarks -- Extended Derivations for Chapter 4.
In: Springer eBooksSummary: 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.
    average rating: 0.0 (0 votes)
No physical items for this record

Introduction -- Background and Literature Review -- Seeing Through Water: Underwater Scene Reconstruction -- Simultaneous Turbulence Mitigation and Moving Object Detection -- Action Recognition by Motion Trajectory Decomposition -- Complex Event Recognition Using Constrained Rank Optimization -- Concluding Remarks -- Extended Derivations for Chapter 4.

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.

There are no comments for this item.

Log in to your account to post a comment.