Behavior of Images and Image Sequences in Phase Space
Pp. 51-113 (63)
Michael Edward Farmer
Chaotic systems are created by an underlying non-linear dynamics. In the
previous chapter we explored the characteristics of chaotic systems. Objects under
motion within an image can be modeled as aperiodic forcing functions impacting the
amplitude response of the imaging sensors in computer vision. Recall from the previous
chapter that systems undergoing aperiodic forcing functions exhibit chaos-like behavior.
Interestingly, illumination changes in image sequences do not exhibit this chaos-like
behavior, but rather exhibit very deterministic behavior that has very limited excursions
in phase space. Recall systems can also exhibit spatial chaotic behavior and the
amplitude variations across an image under varying textures will exhibit varying
behavior in phase space. Also the traditional methods of analyzing texture through greylevel
co-occurrence matrices is closely related to the reconstructed phase space analysis
methods defined in the previous chapter. Thus chaos theory can provide a unifying
framework for describing interesting temporal and spatial behavior in computer vision
while providing an inherent immunity to even complex illumination changes.
Temporal chaos, spatial chaos, texture, global illumination, spatiotemporal
illumination, Lambertian model, Sparrow-Torrance Model, grey-level
co-occurrence matrices, saturation, gamma correction.
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