Image Segmentation Theory And Algorithm Based On Gap Statistics

Image processing and pattern recognition are the stratosphere of an interdisciplinary field. It is well known that edge detection, image segmentation and object description are the mainly research subjects in such field. In addition image segmentation plays a very important role in the field of image processing, pattern recognition, and it is classical difficult problem of the lower level computer vision as well as the fundamental step for image understanding. Based on the projects named "Research of IBR based simulate image synthesis theory and techniques ", "Real-time digital scene generation and analysis system" and "Research of the virtual environment generation in the battlefield based on image rendering ", this dissertation focuses on the research on the theory and application of Gap statistic for image segmentation. A systematic study is made for the theory and algorithms of image segmentation based on Gap statistic. The primary works and creative contribution of the paper are:(1) The paper takes a systematic research on the random statistical model, geometrical analysis model and neural network model, etc. The Gap statistic is firstly introduced to study the theory and algorithms of image segmentation.(2) A new conception of "function gap" is originally proposed in this paper. Based on the different feature and criteria of edge in image, several types of function gap are defined and the relationships among them are discussed. Furthermore, utilizing the theory of function gap, we creatively take an investigation on the Gap statistical properties of images. Finally, the gap statistical models for edge detection are proposed.(3) After presenting the new conception of "point gap", we study the Gap features of the edges in image and develop a formalism of generalized step edges and roof edges. A point Gap model of multi-scale edge detection is presented and the corresponding algorithm is advanced. Furthermore, we put our study forward on the theory and application of another Gap named "order rank sum Gap".(4) Using the long range and short range dependency among image pixels respectively, we propose the conception of weighted Gap and present a new multi-scale weighted neighborhood based edge detection model and corresponding algorithms. The difference between Gap operator and Sobel operator is also analyzed.(5) Several new conceptions including function feature, total Gap and full Gap are proposed and the Gap statistical model for image segmentation is established and the detail...