Open AccessImage stitching using chaos‐inspired dissimilarity measure
Author(s) -
Song Taeyup,
Jeon Changwon,
Ko Hanseok
Publication year - 2015
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2014.0981
Subject(s) - image stitching , ransac , artificial intelligence , computer vision , chaos (operating system) , mathematics , invariant (physics) , measure (data warehouse) , computer science , image (mathematics) , pattern recognition (psychology) , data mining , computer security , mathematical physics
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.