Media Computing

1. Front Matter

   Pages i-xxiii

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2. Bridging the Semantic Gap in Content Management Systems

   • Chitra Dorai, Svetha Venkatesh

   Pages 1-9

3. Essentials of Applied Media Aesthetics

   • Herbert Zettl

   Pages 11-38

4. Space-Time Mappings as Database Browsing Tools

   • Stephen Mamber

   Pages 39-55

5. Formulating Film Tempo

   • Brett Adams, Chitra Dorai, Svetha Venkatesh

   Pages 57-84

6. Modeling Color Dynamics for the Semantics of Commercials

   • Alberto Del Bimbo, Pietro Pala, Enrico Vicario

   Pages 85-104

7. Scene Determination Using Auditive Segmentation Models of Edited Video

   • Silvia Pfeiffer, Uma Srinivasan

   Pages 105-129

8. Determining Affective Events Through Film Audio

   • Simon Moncrieff, Chitra Dorai, Svetha Venkatesh

   Pages 131-157

9. The Future of Media Computing

   • Frank Nack

   Pages 159-196

10. Back Matter

    Pages 197-198

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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 analyz­ ing image sequences, or video understanding. Video understanding deals with understanding of video sequences, e. g. , recognition of gestures, activities, fa­ cial 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 obvious overlap with computer vision. The main goal of computer graphics is to gener­ ate and animate realistic looking images, and videos. Researchers in computer graphics are increasingly employing techniques from computer vision to gen­ erate the synthetic imagery. A good example of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is de­ rived from real images using computer vision techniques. Here the shift is from synthesis to analysis followed by synthesis.

• MPEG
• MPEG-7
• Mapping
• image processing
• information
• modeling
• multimedia
• signal processing

• IBM Thomas J. Watson Research Center, Yorktown Heights, USA

  Chitra Dorai

• Curtin University, Perth, Australia

  Svetha Venkatesh

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