Image matching using relational graph representation

• Main Author: Lai, Chui Yen
• Format: Thesis
• Language: English
• Published: 2005
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/4179/1/LaiChuiYenMFSKSM2005.pdf; http://eprints.utm.my/id/eprint/4179/

Image matching is a process to establish correspondence between primitives of two or more images that capturing a scene from different viewing position. Various image matching techniques using image features have been known in literature. Feature-based matching algorithm cannot tackle the problem of matching ambiguities easily. This study presents an image matching technique using the structural descriptions of image. Structural descriptions are consist of lines and interline relationship in the line-extracted image. Three conditions of inter-line relationship, namely ordering, intersection and co-linearity, were defined and derived in this study. The method involves the representation of the structural descriptions of image in relational graph and the matching between relational graphs to perform image matching. The methodology is consists of six steps: (1) input image, (2) line segment extraction from the image, (3) the interpretation and derivation of structural descriptions from the line-extracted image, (4) the construction of relational graph to represent the structural descriptions, (5) the derivation of association graph from relational graphs to perform relational graph matching, and (6) the searching of the largest maximal clique in the association graph to determine the best matching. Hence, image matching is transformed as a relational graph matching problem in this study. Experiments are carried out to evaluate the applicability of incorporating structural information into the image matching algorithm. The data is consisting of 14 pairs of stereo images. From the result obtained, it was found that the usage of structural information of image is only plausible for the matching of images of simple scene. The matching accuracy of images of complicated scene remains low even after the incorporation of inter-line descriptions into the image matching algorithm.