Binocular Stereo Matching Research Based On Pattern Recognition

Along with the rapid development of science and technology, applications related to machine vision play more and more important roles in the national life. Such as security guard monitoring, robot navigation, three-dimensional digital virtual reality and so on. At the same time, people put forward higher demand for machine vision. At the present stage, a large number of machine vision applications moved from two-dimensional to three-dimensional visualization techniques. As the most close to human visual characteristic three-dimensional system, binocular stereo vision system is a research hotspot in machine vision. Binocular stereo vision system can simulate human vision system. It restored the three-dimensional graphics by their left and right images. Stereo matching technology is a very important and very difficult critical process. The essence of stereo matching is to consult one image, then search its corresponding homonymous points in another image. The image of the natural world is very complex. Some images of special scenes even cause human who has the highest intelligence visual system to produce an illusion. It is a very challenging task to require the computer to analyze various complex images correctly and to search corresponding homonymous points. The work of this article is around the binocular stereo matching task.Studies of binocular stereo matching has been for decades, from the initial feature search based on limit constraint, then the application of optimization theory in Markov random field, to the application of image segmentation algorithm, at last to present the latest parallel algorithm is introduced. The research of the binocular stereo matching follows the visual theoretical framework introduced by Marr that from the bottom processing to the top image understanding. The weak texture and occlusion areas in the image are the difficult questions in binocular stereo matching all the time. To make the binocular stereo matching technology to be used in production and all aspects of life, it must be able to handle a variety of complex natural scenes and guarantee the computational efficiency. The traditional image processing methods had met bottleneck to deal with these problems. In order to get a new breakthrough, it must go to the top of image understanding. The artificial intelligence and pattern recognition methods should be used in stereo matching. The theories and methods in pattern recognition area can be introduced into binocular stereo matching, and that is the focus of the research work done in this article.The main research work and innovative results of this article are as follows: (1) Chapter one in this article compares and discusses the current five kinds of mainstream algorithms in stereo matching fields respectively. The problems and difficulties existing in the current stereo matching algorithms are also discussed. Chapter two introduces the classification for the binocular stereo vision principle and stereo matching algorithm. It also discussed the relationship between some theory in pattern recognition and the matching in binocular stereo vision in detail.(2) The development of image acquisition device is very rapid in recent years. As a result, people can get high resolution images and videos easily. But those high resolution images bring great challenge to computer processing. Several years ago, some of stereo matching algorithms based on Markov theory took more than ten minutes to calculate a450x375resolution image. In nowadays, an ordinary camera can obtain the image resolution of up to6000x4000pixels. The calculation time is too long to lose practical value by traditional stereo matching algorithms. Chapter three in this article make a depth research around this issue and propose a stereo matching method based on the constraint by affine invariant triangle convergence. This method can gain high accuracy matching points in outdoor scene. For some complex scene whose disparity change drastically, the areas can be identified and matched by the point grouping. It can handle the occlusion problem effectively. The method is based on the constraint by affine invariant geometric and has nothing to do with the pixel itself attribute. So it has very high speed to the order of millisecond.(3) The high reliable matching points also can be called generalized ground control points (GGCP), play a very important role in stereo matching. How to get the high reliable GGCP automatically is the research content in the first section in chapter four. In that chapter, a K-means clustering algorithm based on area element in different depth has been proposed. It can be used to screen and eliminate the initial SURF matched feature points and preserve matched feature points conformed to the conditions. Compared with the traditional feature points matching algorithm, this method take a full account of the characteristics of target depth constraint to the space object and cluster two-dimensional points robustly. It can be used to obtain high reliability GGCP.(4) Compared with other algorithm, the stereo matching algorithm based on image segmentation has many good qualities for weak texture images. But the algorithm depends on the segmentation effect greatly and the error brought by the segment method is very difficult to be corrected in the following matching step. For this issue, the second section in the fourth chapter carries on the detailed research. The fuzzy theory in pattern recognition has been introduced, and the single "meaningless" pixel can be attributed to some relative rich semantic line and area elements. The area matching can be used in a whole area element and it can overcome the adverse effects by the weak texture area in the image. What’s more, the line and area elements can be corrected in the latter matching step and avoid some errors difficult to be corrected caused by element extract and matching.(5) Dense stereo matching usually take a long time. It found that the reason for the long operation time is caused by parallax polling disparity for every pixel. The third section in the fourth chapter makes use of the idea of syntax pattern recognition, and the hierarchical relationship model is built in the complex scene image. The complex data structure is expressed by multidimensional matrix. Point and line jump disparity transmission algorithm is proposed, and the dynamic programming theory is also introduced to create disparity map step by step. Compared with the traditional method, it has some intelligence. At first, the complex scene is recognized as element rich of semantic information, then matching them by their features. This method has high computation speed and good adaptability to different scenes.(6) The shadow recognition and elimination for the moving object is always difficult in moving object tracking and position. The chapter five makes use of binocular stereo system and its theory to eliminate the cast shadow for the moving object outdoor and indoor separately and a good effect is obtained.All of the matching algorithms proposed in this article follow the idea of artificial intelligence and pattern recognition. It has very strict requirement in computing speed in order to ensure that the proposed algorithm is practical.