Research on Computer Vision Stereo Correspondence Theory and Algorithms
|School||Nanjing University of Technology and Engineering|
|Course||Pattern Recognition and Intelligent Systems|
|Keywords||Computer vision Stereo correspondence Disparity gradient Area correlation Rank transformations Phase congruency Edge detection Obstacle detection|
Computer vision mainly studies how to realize human visual function with computers. The main idea is to perceive, identify and understand the three-dimensional scene from two-dimensional projective images. Stereo matching is one of the key issues in computer vision and non-contact measurement. The information of depth or distance can be obtained from disparity of the points. It is useful in three-dimensional reconstruction, robot navigation, vehicle navigation, and so on. Stereo matching is an ill-posed problem with the influence of distortions and occulsions. 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.Area correlation is traditional approach in stereo correspondence. But its wide application is affected by the heavy computational burden. To reduce computation and enhance algorithm speed, the paper presents a variable search region area-matching approach based on disparity gradient, according to relationship between disparity gradient and search region. The core of this method is that the search region of each point in one image can be ascertained by itself disparity gradient and its former point’s disparity. Not all points need matching in the largest search region under the present algorithm. So, the algorithm can decrease redundant search, shorten the matching time and improve accuracy of 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 presents 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, census constraint and color difference gradient constraint further reduce outlier and further enhance right matching ratio.Infrared images have higher noise and lower resolution than visible images. These features makes area matching base on intensity more difficult to obtain a good disparity image. After analyzing the phase congruency transformed image, the paper presents a novel area matching method. By this way, noise in images is restrained and the images features are more distinct. The result of correspondence based on transformation image is better.Obstacle detection is an important research subject in ALV, robot navigation and driver assistant system. The vision-based approach is the most commonly method in obstacle detection. The paper present an obstacle detection algorithm based on color image. The image is segmented under HSV of color image first. And the obstacle locations are obtained with stereo correspondence. The approach is good for detection of path, vehicle and building and can restrain the nature background.