Research Of Local Matching Algorithms Of Stereo Vision

Computer vision is an important branch of artificial intelligence.By simulating biological vision,computers have the ability to perceive three-dimensional environmental information similar to human vision.Binocular stereo vision is one of the important fields of computer vision research,which is widely used in driverless,intelligent navigation,industrial detection,virtual reality and other fields.The essence of stereo matching is to find corresponding matching points from different viewpoints of two images,and obtain depth information by calculating parallax.However,due to the existence of factors such as lighting,occlusion,and texture,it challenges stereo matching and makes stereo matching a core part of binocular vision.The matching accuracy and running time of the algorithm directly determine the performance of the binocular vision system in the actual scene.This article introduces the basic principles of binocular stereo vision in detail,and conducts in-depth research and improvement on stereo matching algorithm technology.To solve the problem of disparity accuracy of local stereo matching,this paper first proposes the concept of image multi-scale,using the Gaussian pyramid cross-scale framework to simulate the stereo vision matching on multiple scales at the cost aggregation stage,and fuses the matches of different scales through cross-scale regular constraints.The matching cost of coarse scale will modify the matching cost of the finest scale and improve the stereo matching the accuracy of matching algorithm;Secondly,a twice guided filtering model is proposed,which is applied to the local stereo matching algorithm.The twice guided filtering model overcomes the deficiency of guided filtering and further suppresses the noise because the result of the first-guided image filtering is used as original guiding image of the secondguided filtering.In the cost aggregation phase,the algorithm with a twice guided filtering model further improves the matching accuracy because the cross-scale framework is used to aggregate the matching cost volume of different scales.The experimental results demonstrate that the proposed algorithm has higher accuracy on the detection of standard stereo image pairs on the Middlebury benchmark.The computational complexity is independent of the filtering kernel size.The proposed algorithm achieves good performance in speed and accuracy.The idea of twice guided filtering has potential applications in stereo matching.In this paper,according to the binocular vision model,choose the appropriate camera to build the binocular stereo vision experiment platform.Camera calibration was performed on a self-made calibration board using Matlab software to obtain the internal and external parameters of the binocular camera required for the experiment.In the programming environment of VS2013 combined with Opencv,the camera parameters obtained from calibration are imported for real scene image rectification.The improved algorithm in this paper is used for stereo matching on the rectified image to calculate the 3D information of the object surface and the distance between the object and the camera.The experimental results show that the improved algorithm can also obtain high-quality disparity maps in real scenes,which can well realize 3D information measurement of real scenes.