Video Mining

  • Azriel Rosenfeld
  • David Doermann
  • Daniel DeMenthon

Part of the The Springer International Series in Video Computing book series (VICO, volume 6)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Arnon Amir, Savitha Srinivasan, Dulce Ponceleon
    Pages 1-30
  3. Nevenka Dimitrova, Lalitha Agnihotri, Radu Jasinschi
    Pages 61-90
  4. Ajay Divakaran, Kadir A. Peker, Regunathan Radhakrishnan, Ziyou Xiong, Romain Cabasson
    Pages 91-121
  5. Ying Li, Shrikanth Narayanan, C.-C. Jay Kuo
    Pages 123-154
  6. Rainer Lienhart
    Pages 155-183
  7. Zeeshan Rasheed, Mubarak Shah
    Pages 185-217
  8. Malcolm Slaney, Dulce Ponceleon, James Kaufman
    Pages 219-252
  9. John R. Smith, Ching-Yung Lin, Milind Naphade, Apostol Paul Natsev, Belle Tseng
    Pages 253-277
  10. Lexing Xie, Shih-Fu Chang, Ajay Divakaran, Huifang Sun
    Pages 279-307
  11. Rong Yan, Alexander G. Hauptmann, Rong Jin
    Pages 309-338
  12. Back Matter
    Pages 339-340

About this book

Introduction

Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyzing image sequences, or video Video understanding deals with understanding of video understanding. sequences, e.g., recognition of gestures, activities, facial expressions, etc. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Video understanding has overlapping research problems with other fields, therefore blurring the fixed boundaries. Computer graphics, image processing, and video databases have obvi­ ous overlap with computer vision. The main goal of computer graphics is to generate and animate realistic looking images, and videos. Re­ searchers in computer graphics are increasingly employing techniques from computer vision to generate the synthetic imagery. A good exam­ pIe of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is derived from real images using computer vision techniques. Here the shift is from synthesis to analy­ sis followed by synthesis. Image processing has always overlapped with computer vision because they both inherently work directly with images.

Keywords

Audio Frames information multimedia optical character recognition (OCR) production video video mining video on demand

Editors and affiliations

  • Azriel Rosenfeld
    • 1
  • David Doermann
    • 1
  • Daniel DeMenthon
    • 1
  1. 1.University of MarylandCollege ParkUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4757-6928-9
  • Copyright Information Springer-Verlag US 2003
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4419-5383-4
  • Online ISBN 978-1-4757-6928-9
  • Series Print ISSN 1571-5205
  • About this book