Illumination Invariant Intensity-Based Image Registration Using Chaos Theory

Accurate and robust registration of image pairs is of interest in many fields that use computer vision such as surveillance and medical diagnostics. In each of these fields the area-based (or voxel-based) approach to image registration is popular, however it is known that these methods are sensitive to illumination change where incorrect results are common. Past work in applying chaos theory to computer vision has demonstrated that the underlying physics of illumination change versus contextual change result in very different behavior when analyzed in phase space. Illumination is deterministic and results in non-fractal phase space behavior, while contextual change is chaos-like and results in complex fractal regions in phase space. A chaos-theoretic approach to image registration is presented with favorable results compared to the traditional and very popular Mutual Information measure.