Video Object Extraction and Representation

Lin, I-Jong; Kung, S. Y.

ISBN 10: 0792379748 / ISBN 13: 9780792379744

Published by Kluwer Academic Publishers, 2000

About this Item

Focuses upon two problems at the heart of the algorithmic/computational infrastructure: video object extraction, or how to automatically package raw visual information by content; and video object representation, or how to automatically index and catalogue extracted content for browsing and retrieval. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 177 pages, biography. BIC Classification: TJKV; UGV. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 12. Weight in Grams: 1000. . 2000. Hardback. . . . . Books ship from the US and Ireland.

Seller Inventory # V9780792379744

Bibliographic Details

• Title: Video Object Extraction and Representation
• Publisher: Kluwer Academic Publishers
• Publication Date: 2000
• Language: English
• ISBN 10: 0792379748
• ISBN 13: 9780792379744
• Binding: Hardcover
• Condition: New

About this title

Synopsis

“If you have built castles in the air, your work need not be lost; that is where they should be. Now put the foundations under them. ” - Henry David Thoreau, Walden Although engineering is a study entrenched firmly in belief of pr- matism, I have always believed its impact need not be limited to pr- matism. Pragmatism is not the boundaries that define engineering, just the (sometimes unforgiving) rules by which we sight our goals. This book studies two major problems of content-based video proce- ing for a media-based technology: Video Object Plane (VOP) Extr- tion and Representation, in support of the MPEG-4 and MPEG-7 video standards, respectively. After reviewing relevant image and video p- cessing techniques, we introduce the concept of Voronoi Ordered Spaces for both VOP extraction and representation to integrate shape infor- tion into low-level optimization algorithms and to derive robust shape descriptors, respectively. We implement a video object segmentation system with a novel surface optimization scheme that integrates Voronoi Ordered Spaces with existing techniques to balance visual information against predictions of models of a priori information. With these VOPs, we have explicit forms of video objects that give users the ability to - dress and manipulate video content. We outline a general methodology of robust data representation and comparison through the concept of complex partitioning mapped onto Directed Acyclic Graphs (DAGs).