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Semantic Video Object Segmentation for Content-Based Multimedia Applications

  • Book
  • © 2002

Overview

Authors:
  1. Ju Guo

    1. Intervideo Inc., USA

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    You can also search for this author in PubMed   Google Scholar

  2. C.-C. Jay Kuo

    1. University of Southern California, USA

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    You can also search for this author in PubMed   Google Scholar

Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 639)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xxi
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  2. Introduction

    • Ju Guo, C.-C. Jay Kuo
    Pages 1-7

  3. Review of Previous Work

    • Ju Guo, C.-C. Jay Kuo
    Pages 9-27

  4. Automatic Segmentation

    • Ju Guo, C.-C. Jay Kuo
    Pages 29-43

  5. Object Shape Postprocessing

    • Ju Guo, C.-C. Jay Kuo
    Pages 45-51

  6. Interactive Segmentation Algorithms

    • Ju Guo, C.-C. Jay Kuo
    Pages 53-73

  7. Temporal and Spatial Adaptive Processing

    • Ju Guo, C.-C. Jay Kuo
    Pages 75-93

  8. Summary and Future Work

    • Ju Guo, C.-C. Jay Kuo
    Pages 95-97

  9. Back Matter

    Pages 99-109
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Keywords

About this book

Semantic Video Object Segmentation for Content-Based Multimedia Applications provides a thorough review of state-of-the-art techniques as well as describing several novel ideas and algorithms for semantic object extraction from image sequences. Semantic object extraction is an essential element in content-based multimedia services, such as the newly developed MPEG4 and MPEG7 standards. An interactive system called SIVOG (Smart Interactive Video Object Generation) is presented, which converts user's semantic input into a form that can be conveniently integrated with low-level video processing. Thus, high-level semantic information and low-level video features are integrated seamlessly into a smart segmentation system. A region and temporal adaptive algorithm was further proposed to improve the efficiency of the SIVOG system so that it is feasible to achieve nearly real-time video object segmentation with robust and accurate performances. Also included is an examination of the shape coding problem and the object segmentation problem simultaneously.
Semantic Video Object Segmentation for Content-Based Multimedia Applications will be of great interest to research scientists and graduate-level students working in the area of content-based multimedia representation and applications and its related fields.

Authors and Affiliations

  • Intervideo Inc., USA

    Ju Guo

  • University of Southern California, USA

    C.-C. Jay Kuo

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