Study On Stereo Matching In Image-based Modeling

With the development of computer performance and computer technology, more and more realistic 3-D models appear in all kinds of software and become an important way to represent the real world. Compared with traditional methods to construct the 3-D model, Image-based modeling (IBM) has the advantages of low cost, simple operation and realistic feeling; it turns into a hot spot of researches in computer graphic and computer vision gradually. Stereo matching, which obtains the depth or distance from the pixels'disparity and provides useful information for 3-D reconstruction, robot navigation, and so on, is one of the fundamental problems of computer vision and image-based modeling. However, stereo matching is a difficult problem under the influence of deformations, distortions and occlusions. This dissertation studies relevant theory and approaches of stereo matching and proposes some improved algorithms and experiments.The main research results of this dissertation are summarized as follows:There are many less-textured areas in the real world, so improving the ability of matching less-textured area can enhance the performance of stereo matching algorithm. To get better disparity result of the less-textured area of image, this dissertation proposes an area-based stereo matching strategy. First, the image is divided into several less-textured or well-textured areas. Then, the disparity of less-textured area is obtained through the matching based on regions. Compared with pixel, a region contains more information and appears less repeatedly, so it could reduce the probability of matching errors.On the basis of the area-based stereo matching strategy, this dissertation proposes a basic algorithm. It describes the image texture with gray level co-occurrence matrix (GLCM) and segments and matches the less-textured area by the contrast, entropy and correlation features obtained from the GLCM. This dissertation also introduces the concept of key point into the calculation of the less-textured area's disparity. It solves the problem aroused by the difference of the size and the shape of the matched less-textured areas because of the tiny distinction in the images'texture. The experiment on the international standard image data shows that the proposed algorithm is effective.Segmentation and matching of the less-textured area are the critical steps in the area-based stereo matching strategy. According to the characters of less-textured area, this dissertation provides a novel approach for the segmentation of less-textured area. It describes the texture with Laws'masks and gets the less-textured area by histogram-based segmentation. The best result is obtained for various scene images by comparing various combinations of Laws'masks. Experimental results on the international standard image data show that the novel algorithm has better recognition rate of less-textured area and better robustness of choosing the dividing threshold than the previous method which describes and segments the less-textured area with GLCM. This new algorithm is helpful to increase the accuracy and usability of region-based stereo matching algorithm.The changing of brightness in image can influence the texture description based on GLCM, which is bad for the segmentation and matching of the less-textured area. To solve the problem, this dissertation presents a method based on LBP/C to search the less-textured area. LBP/C has resistance to the changing of brightness. So the novel method distinguish the less-textured pixel by the local contrast which is near by 0 firstly, and match the less-textured area by the distribution of LBP. Experiments show that the novel method can segment and match the less-textured area effectively.The appearance of objects will change dramatically in various scales, which decreases the accuracy of stereo matching algorithm. To reduce the influence, this dissertation proposes a fractal based multi-scale stereo matching algorithm. First, it describes the feature of the local texture with local fractal dimension based on double blanket, and then gets the feature of the image area by the distribution of local fractal dimension. The experiment results show that fractal based multi-scale stereo matching algorithm leads to better result on matching image area with various scale than GLCM based stereo matching algorithm. The novel algorithm can improve the performance of the area based stereo matching algorithm.Finally, the dissertation is concluded. Some problems as well as further work are also given.