Accurate and robust image change detection and motion segmentation has been of substantial interest in the image processing and computer vision communities. To date no single motion detection algorithm has been universally superior while biological vision systems are so adept at it. In this paper, we analyze image sequences using phase plots generated from sequential image frames and demonstrate that the changes in pixel amplitudes due to the motion of objects in an image sequence result in phase space behaviour resembling a chaotic signal. Recent research in neural signals have shown biological neural systems are highly responsive to chaos-like signals resulting from aperiodic forcing functions caused by external stimuli. We then hypothesize an alternative physics-based motion algorithm from the traditional optical flow algorithm. Rather than modeling the motion of objects in an image as a flow of grayscale values as in optical flow, we propose to model moving objects in an image scene as aperiodic forcing functions, impacting the imaging sensor, be it biological or silicon-based. We explore the applicability of some popular measures for detecting chaotic phenomena in the frame-wise phase plots generated from sequential image pairs and demonstrate their effectiveness on detecting motion while robustly ignoring illumination change.
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