Application of Chaos and Fractals to Computer Vision

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This book provides a thorough investigation of the application of chaos theory and fractal analysis to computer vision. The field of chaos theory has been studied in dynamical physical systems, and ...
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Applications to Attentive Vision - Chaotic Basins of Attraction for Motion and Contextual Change Segmentation

Pp. 186-201 (16)

Michael Edward Farmer

Abstract

Attentive vision relates to the isolation of objects of interest to be able to advance them to yet higher level vision systems such as object tracking and object recognition. Since the task is related to object segmentation from the background it will require the application of local measures of fractality in phase space. These local measures will be used to identify the regions of phase space that correspond to interesting behavior such as a moving object or contextual change. Once these regions in phase space are identified the vision system will use the mapping from phase space to the original image space to identify the pixels that correspond to the object of interest. This is markedly different from traditional methods such as Optical Flow and Gaussian Mixture Models where the decisions of important change are made in the grayscale space of the original images, yet this space is corrupted by spatio-temporal illumination changes. The performance of the chaos-based approach is demonstrated both for motion-based segmentation and also contextual change-based segmentation.

Keywords:

Attentive vision, motion segmentation, change-based segmentation, optical flow, orbit compaction.

Affiliation:

Department of Computer Science, Engineering and Physics 207 Murchie Science Building 303 E. Kearsley Street Flint, Michigan USA.