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Front-End Vision and Multi-Scale Image Analysis - Multi-scale Computer Vision Theory and Applications, written in Mathematica (Hardcover, 2003 ed.)
Loot Price: R4,631
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Front-End Vision and Multi-Scale Image Analysis - Multi-scale Computer Vision Theory and Applications, written in Mathematica (Hardcover, 2003 ed.)
Series: Computational Imaging and Vision, 27
Expected to ship within 10 - 15 working days
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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.
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