Graph Matching Algorithms and Their Applications in Computer Vision
Abstract：Feature correspondence is a fundamental problem in computer vision, which lays the foundations for many important tasks, such as object recognition, 3D reconstruction. It can be well defined, and effectively solved by graph matching, which is attracting more research interests, driven by more powerful computation and storage abilities of modern computers and some other factors. Aiming at some key problems in graph matching algorithms and their applications, in this project we plan to carry out research on two levels, with the first one to extend our previous work, and the second one to explore new research directions. Specifically, the first one consists of three studies, which are respectively adaptive graph matching, adjacency tensor based matching between hyper-graphs with different sizes, and graph matching based lunar surface image processing. And the second one consists of the exploration on relations between gradient based optimization and spectral decomposition based optimization, and the exploration on time/space effective solutions for the matching between huge size graphs. In this project, we pay great attentions to both theoretical deductions and realistic applications, where the above lunar surface image processing and huge size graph matching problems both belong to application driven research.
Keywords: graph matching; computer vision; combinatorial optimization
Contact:
YANG Xu
E-mail: xu.yang@ia.ac.cn
The State Key Laboratory of Management and Control for Complex Systems