Front-End Vision and Multi-Scale Image Analysis

Multi-Scale Computer Vision Theory and Applications, written in Mathematics

  • Bart M. ter Haar Romeny

Part of the Computational Imaging and Vision book series (CIVI, volume 27)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Pages 37-51
  3. Pages 53-69
  4. Erik Dam, Bart M. ter Haar Romeny
    Pages 215-240
  5. Erik Dam, Bart M. ter Haar Romeny
    Pages 241-256
  6. Bart M. ter Haar Romeny, Erik Dam
    Pages 257-276
  7. Pages 277-284
  8. Bart ter Haar Romeny, Luc Florack, Avan Suinesiaputra
    Pages 285-310
  9. Jan-Mark Geusebroek, Bart M. ter Haar Romeny, Jan J. Koenderink, Rein van den Boomgaard, Peter Van Osta
    Pages 311-327
  10. Pages 329-343
  11. Pages 345-360
  12. Pages 393-394
  13. Back Matter
    Pages 395-466

About this book


Many approaches have been proposed to solve the problem of finding the optic flow field of an image sequence. Three major classes of optic flow computation techniques can discriminated (see for a good overview Beauchemin and Barron IBeauchemin19951): gradient based (or differential) methods; phase based (or frequency domain) methods; correlation based (or area) methods; feature point (or sparse data) tracking methods; In this chapter we compute the optic flow as a dense optic flow field with a multi scale differential method. The method, originally proposed by Florack and Nielsen [Florack1998a] is known as the Multiscale Optic Flow Constrain Equation (MOFCE). This is a scale space version of the well known computer vision implementation of the optic flow constraint equation, as originally proposed by Horn and Schunck [Horn1981]. This scale space variation, as usual, consists of the introduction of the aperture of the observation in the process. The application to stereo has been described by Maas et al. [Maas 1995a, Maas 1996a]. Of course, difficulties arise when structure emerges or disappears, such as with occlusion, cloud formation etc. Then knowledge is needed about the processes and objects involved. In this chapter we focus on the scale space approach to the local measurement of optic flow, as we may expect the visual front end to do. 17. 2 Motion detection with pairs of receptive fields As a biologically motivated start, we begin with discussing some neurophysiological findings in the visual system with respect to motion detection.


computer vision Diffusion image analysis kernel knowledge Pattern Matching tracking

Authors and affiliations

  • Bart M. ter Haar Romeny
    • 1
  1. 1.Fac. Biomedical Engineering Dept. Image Analysis & InterpretationEindhoven University of TechnologyMB EindhovenNetherlands

Bibliographic information

  • DOI
  • Copyright Information Springer Netherlands 2003
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4020-1503-8
  • Online ISBN 978-1-4020-8840-7
  • Series Print ISSN 1381-6446
  • Buy this book on publisher's site
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