Studies On Perceptual Completion Models And Algorithms For Subjective Contour

The smooth completion of fragmented curve segments even when sufficientcontrast is lacking or in the presence of occlusions is an intrinsic skill of human visualsystem and subjective contour shows one of the compelling examples. Subjectivecontour can be defined as a visual psychological phenomenon which is to perceive aComplete contour in homogenious areas according to the implicit constrains presented inthe image. Recognizing subjective contour features is an intrinsic capability of humanvision, demonstrating the remarkable ability of the visual system. As an important cueto investigate the perception of contour,shape,depth and visual attention, research workon subjective contour can not only contribute to solve several problems in imageprocessing and computer vision, but also mean a lot to cognition science. The subjectivecontour theory will be proved to have many applications in modern life.This thesis addresses the research work on perceptual completion model ofsubjective contour and related algorithms, which will play a foundamental role forsimulating how the biovision system perceive subjective contours. The maincontributions of the thesis can be summaried as follows:Firstly, a perceptual completion model of subjective contour in the light ofFACADE (Form And Color And Depth) theory is presented. This model contains acomprehensive solution to solve the subjective contour problem, subdividing theprocess to contour feature extraction together with contour organization and contour gapcompletion.Secondly, the corresponding algorithms for contour feature extraction are studied:In the previous model, contour feature extraction consists of oriented edge detection andkey point detection. Through analyzing the similarities between these two parts, weadopt orientation estimation as the basic technology. Then a relatively systematic studyon orientation estimation is carried out: a mathematical definition for orientation isgiven; the guide theory for orientation estimation is extended and proved; we also putforward an orientation estimation algorithm called CLFND (Combined LognormalFilter and Nonlinear Diffusion) and demonstrate its efficiency in experiments.Furthermore, an algorithm for oriented edge detection and another algorithm for keypoint detection are brought forward, which are all based on orientation estimation.Thirdly, with regard to the contour organization and contour gap completion, thethesis presents three research results: one is an improved tensor voting algorithm forsubjective contour extraction. An iterative optimization procedure was developed basedon eigenvalue perturbation analysis to complete the contour gaps and to reduce theblurring effect of the original method. The second one is a computation approach for subjective contour which utilizes the cooperation-competition mechanism of biovisionsystem. The goal is to simplify the neural-computing of biovision process modeled inthe Boundary Contour System of FACADE theory, and to extract subjective contour in.a new way. The procedure consists of DOG filtering, cooperative filtering,post-processing of cooperative cues and regrouping of endpoints, corresponding to theoriented edge filtering by simple cells, cooperative filtering by bipole cells and end-stopeffect formulated by hypercomplex cells in biovision system respectively. The last oneis an algorithm based on connection relation graph, which judges whether two keypoints should be connected according to their positions and orientations and thenconnect the key points that Should be connected to complete the subjective contour.