Exploiting Multi-Fractal and Chaotic Phenomena of Motion in Image Sequences: Foundations

Abstract

Accurate and robust image motion detection has been of substantial interest in the image processing and computer vision communities. Unfortunately, no single motion detection algorithm has been universally superior; while biological vision systems are adept at motion detection. Recent research in neural signals have shown biological neural systems are highly responsive to chaotic signals. In this paper, we analyze image sequences using frame-wise phase plots and demonstrate that the changes in pixel amplitudes due to the motion of objects in an image sequence, results in apparently chaotic behavior in phase space. We explore these chaotic phenomena in a variety of image datasets to show their repeatability, to validate the assumption of ergodicity, and to demonstrate their uniqueness from the changes due to illumination, particularly spatio-temporally varying illumination.