Image stitching using chaos-inspired dissimilarity measure

Abstract

A novel image-stitching algorithm is proposed that uses the chaos-inspired dissimilarity measure. Previous approaches to image stitching generally employed invariant local features and random sample consensus (RANSAC), to verify image matches. In panoramic stitching, however, RANSAC is highly sensitive to illumination difference between the pair of images stemming from two different exposures, which essentially results in mismatched pairs, leading to poor stitching performance. By applying chaos theory, the contextual change of an image becomes chaos-like, and results in a complex fractal trajectory in phase space. The specific chaos behaviour between a pair of images is exploited, whereby the resulting phase space exhibits strong invariance to illumination changes. Accordingly, the chaos-inspired dissimilarity between images is measured, thereby the matched pairs are robustly found under the presence of illumination difference. Representative performance experiments demonstrate significant improvements over the conventional method.