Research On Shape Contour Analysis And Matching Based On Combined Global And Local Information

Shape, as a high-level visual characteristic, has been frequently used to represent the properties of objects in Computer Vision, Image Analysis and Understanding. Shape is a kind of critical and fundamental feature, and shape representation/matching has become one basic problem in the field of Computer Vision. Because of serious intra-class variances among object shapes in our life, there are a lot of difficulties when performing shape matching. However, a huge number of classical shape representation technologies and shape matching algorithms have appeared by hard works of researchers. Shape matching is still one of the leading and hot topics in Computer Vision and Image Analysis so far.In this thesis, the development of shape matching technologies is reviewed, and it is found that none of current descriptors is able to combine local and global features effectively and efficiently. Accordingly, some researches are performed on how to define local and global features, and a framework to naturally combine these two issues is presented, resulting in some novel descriptors with better performances. The main contributions of this thesis are summarized as follows:First, some effective local features are proposed. Local features are fundamental for defining shape descriptors. Local features should give a precise description for shape contours, and remain unchanged under linear transformations (translation, rotation, and scaling). Based on the geometric relationship between contour points, several local features, such as feature triangle and height functions, are defined to represent different aspects of shape contours. These local features are able to represent shapes exactly, and achieve linear transformation invariance. They provide a solid basis for presenting effective shape descriptors.Second, some effective global feature is proposed. Global features are important issues contained in shape descriptors. They directly determine the robustness of shape descriptors to noise and local deformations. Based on the experience of shape matching algorithms, the order sequential information of contours is creatively defined as the global feature. This is the first time for contour order information to be treated as shape feature used in shape descriptors. It is proved that the contour order information can be used not only as constraints in shape matching algorithms but also as shape features in shape descriptors. It is able to help significantly improve the performance of descriptors.Third, a new framework to combine local and global shape features is presented. Shape descriptors will never become not only description precisely but also robust to noise until both local and global features are combined together. It is discovered that after local features are rearranged according to the contour order information, the local and global features are naturally combined. Compared with current multi-scale or multi-aspect descriptors, the proposed methods are able to overcome the problems of usage difficulties and lack of shape information. The proposed descriptors include both local and global shape information, and they are easy to use and compute as well.Fourth, three novel shape descriptors are presented. Based on the local features of feature triangles, shape contexts and height functions, combined with contour order information, three novel shape descriptors are constructed correspondingly. The experimental results show that the proposed methods are all able to achieve excellent shape retrieval rates. Especially for height functions, it is able to get the highest accuracy rate of shape retrieval among all descriptors all over the world. It achieves on the well-known MPEG-7 shape benchmark the best ever high rate of 90.35% only by the descriptor and 96.45% when combing with the graph transduction algorithm. What is more, each of these descriptors has a definite geometric meaning, is efficient to compute, and has a low feature dimension. The general performances of them outperform other widely used shape description algorithms.In this thesis, contour based shape representation is carefully studied and the contour sequential information (a kind of global feature) is regarded as the core and fundamental shape characteristic. Several contour descriptors with clear geometric meaning, high discriminative power, precise representation and moderate computational complexity are presented by combining the sequential information with different local shape features. Some successes are obtained for shape matching, which is one of the key problems in the field of Computer Vision, and these successes are already confirmed by extensive experimentations. Some vision theories and applications can also benefit from the studies and algorithms proposed in this thesis.