Research On Self-organization Reconstruction Of Free-form Surface Based On Image Intensity

The three-dimension reconstruction technique of free-form surfaces has been widely used in many fields such as manufacturing industry, art design, medical treatment and plastic surgery. Shape from shading (SFS), an important branch in computer vision field, is one of reconstruction methods of free-form surface. In order to overcome some disadvantages of traditional SFS methods such as low reconstruction precision, limited compatibility and constrained application under Lambertian reflectance model, this dissertation studies and proposes a novel SFS method based on self-organization principle in system theory. This method takes the intensity image as a target template, treats each pixel of an image as a bion that possesses decision-making capacity, and simulates the self-organization behavior of biotic populations to reconstruct the free-form surfaces guided by the target template.This dissertation uses a novel SFS method based on self-organization principle to reconstruct surface. First the feasibility of this method is studied. The free-form surface to be reconstructed is regarded as a macro-surface which is composed of some micro-surface. By using the mechanism of self-organization system, each micro-surface is treated as a bion that possesses decision-making capacity, and uses its local communication and decision-making capacity to gradually realize the self-organizing reconstruction for the free-form macro-surface guided by the transition rule. Since self-organization system has the ability of evolution from simplicity to complexity, and from roughness to fineness, and it has antinoise capacity, this method can eliminate some error caused by inaccurate micro-surface.Then based on self-organization principle, three SFS methods under idea Lambertian reflectance model are presented, namely self-organization freeform reconstruction method based on triangulation, self-organization freeform reconstruction method based on quadric surface, and self-organization freeform reconstruction method based on adjusting needle map. In self-organization freeform reconstruction method based on triangulation, each pixel of an image is taken as a bion of self-organization system, and each bion and its two neighbors constitute a triangular surface patch, then this bion adjusts its state in order that the triangle element satisfies the Lambertian reflectance model. All bion can parallelly operate and cooperate with its neighbors, and gradually reconstruct the free-form surface.In self-organization freeform reconstruction method based on quadric surface, the surface to be reconstructed can be divided into a union of micro-surface. According to the intensity target template of image, each micro-surface is approximated by a quadric surface under Lambertian reflectance map. Then each approximated micro-surface is treated as a vital self-organization unit, and all units reconstruct the macro-surface under the internal machanism of self-organization system.The reconstructed results are expressed as surface normal in self-organization freeform reconstruction method based on adjusting needle map. In self-organization system, each surface normal is treated as a bion. At first all surface normals are initialed, then guided by the image template, surface normals are parallelly and gradually adjusted by a procedure which includes three constraints: smooth constraint, intensity gradient constraint and intensity constraint. And then micro-surface is approximated by a quadric surface from each surface normal and its neighbors, in the same way, the macro-surface can be reconstructed from the known micro-surface.In order to improve the compatibility of SFS, the presented SFS technique is extended to general reflectance model. This study develops a measuring apparatus for the reflection model parameters, and then uses the parameters to separate reflection components from grayscale image, and then reconstructs free-form surface from the diffuse component.The experiment results demonstrate this SFS approach has better precision and compatibility, and can be used under general reflectance model. This method introduces self-organization principle to SFS method for the first time, and extends its application to general reflection model, so it has estimable theory value. After being further improved, the results of this dissertation can be applied to many fields, such as aerial survey of relief of Earth's or Moon's surface, reconstruction of 3D head skeleton or breast from image in medical aesthetics, automatic navigation of mobile military robot, so it can be applied to military, arts, medical treatments, space exploration and so on, and this method is valuable for academic research and application.