Research of Key Technology for Stereo Matching
|School||Nanjing University of Technology and Engineering|
|Course||Pattern Recognition and Intelligent Systems|
|Keywords||Computer vision Stereo correspondence Disparity gradient Adapted windows Rank transformations Specular reflections Graph cuts Phase congruency|
Stereo matching has been one of the most researched areas of machine vision. It can bring critical advantages to a very wide spectrum of visual application domains, such as 3-D reconstruction, robot vision, automation land vehicle navigation, and so on. But stereo matching is an ill-posed problem with the influence of distortions, occlusions and low texture, obtaining exactly disparity still faces challenge. In computer vision, binocular vision is similar to the mechanism of human binocular vision, and easy to achieve in practical applications. The relevant theories and approaches of binocular stereo matching have been studied in this thesis, and some progressive achievements have been made.A central problem in stereo matching by computing correlation or sum of squared differences (SSD) lies in selecting an appropriate window size. The window size must be large enough to include enough intensity variation for reliable matching, but small enough to avoid the effects of projective distortion. If the window is too small and does not cover enough intensity variation, it gives a poor disparity estimate, because the signal to noise ratio is low. If, on the other hand, the window is too large and covers a region in which the depth of scene points (i.e., disparity) varies, then the position of maximum correlation or minimum SSD may not represent correct matching due to different projective distortions in the left and right images. For this reason, a window size must be selected adaptively. We have researched multiple windows and adapted windows which find a best matching windows for area-based stereo matching.Image transformation is widely and effectively used in image processing. According to the principle of Rank transformation and Census constraint and color difference gradient constraint, the paper presented a color image matching algorithm based on Rank transformation. The experiment results show that the disparity of Rank transformation image is more precise than that of intensity image. At the same time, the matching result is more robust by noise influence to a certain extent. In addition a new non-parametric transform founded on neighboring region disparity for stereo matching is proposed in the paper, the proposed algorithm is the more precise matching invariance to certain types of image noise than Rank transform and Census transform.Traditional stereo correspondence algorithms rely heavily on the lambertian model of diffuse reflectance. While this diffuse assumption is generally valid for much of an image, processing of regions that contain specular reflections can result in severe matching errors. In this paper, We address the problem of binocular stereo dense matching in the presence of specular reflections by introducing a novel correspondence measurement which is robust to the specular reflections. Accurate depth can be estimated for both diffuse and specular regions. Unlike the previous works which seek to eliminate or avoid specular reflections using image preprocessing or multibaseline stereo, our approach works in its presence.Some recent stereo matching algorithms are based on graph cuts, they transform the matching problem to a minimisation of a global energy function. The minimisation can be done by finding out an optimal cut in a special graph. Different methods were proposed to construct the graph, But all of them, consider for each pixel, all possible disparities between minimum and maximum values. In this article, a new method is proposed:only some potential values in the disparity range are selected for each pixel, These values can be found using disparity gradient and fuzzy logic. This method allows us to make wider the disparity range,and at the same time to limit the volume of the graph, and therefore to reduce the computation time.Infrared images have higher noise and lower resolution than visible images. This makes it more difficult to achieve a better disparity image in infrared images by using the method based on region matching. After analyzing the phase congruency transformed image, a sparse depth field may be obtained that can be interpolated to produce a dense depth field. In our proposed technique the sparse disparity map is produced by matching the stable features, extracted from the phase congruency model. A set of Log-Gabor wavelet coefficients is used to analyze and describe the extracted features for matching. The resulted sparse disparity map is then refined by triangular and epipolar geometrical constraints. In this work, we present a stereo matching algorithm based on belief propagation (BP). The algorithm is designed to work on sparse images originating from image content adaptive mesh representation techniques. There, an image is approximated with a mesh. The nodes of the mesh are the non-uniform samples which are the ones that form the sparse image. The key issue in the proposed method is to formulate BP such that it matches a sparse left stereo image with a dense right image to obtain a sparse depth map. Moreover, we propose a simple method that recovers the dense disparity map of the scene from the sparse one using the approximating mesh of the image.