Research On The Technic Of Three Dimensional Reconstruction For Binocular Vision Based On Region Growing

The technique of3-D reconstruction for images is an important part in computer vision technology. As a vital branch in computer vision technology, the techniques of object detection and recognition for specific images have gradually been attached great importance for scholars at home and abroad in recent years. With the advantages of high efficiency and low cost, this technique plays an increasingly important role in the field of industrial inspection, security monitoring, visual navigation, biomedical, virtual reality, and so on.This paper mainly focus on the dense matching problem of how to use the diffusion method based on region growing, and then apply the relevant principles and methods to obtain the3-D point cloud data of the object surface. The main work is as follows:1. For the insufficiency of the existing dense matching algorithm based on region growing, an improved3-D reconstruction method based on region growing is proposed in the paper. The7×7rectangular neighboring region of seed points is selected as the initial searching window in the process of spreading, and based on the method of self-adaptive searching window, the rule of polar line distance and directional constraints is put forward in order to remove the untrusted matching candidates in the searching window; Meanwhile, The binary robust scale key points (Brisk) are selected as the seed points in regional spreading, which makes the extraction and matching process meet the requirement of real-time for seed points.2. As the image acquisition process is seriously influenced by the angle and strength of the light, the quality of the image is particularly sensitive to the light. Through the analysis of the texture features of the image, the image is pre-processed by texturing, thereby weakening the impact of the uneven illumination under different perspectives on the image gray value of the local area, and appropriately removing the noise interference of this partial area.3. Experimental methods are utilized to obtain the2-D dense matching points based on the above theories. Simultaneously, the analysis of projection error model is established based on the principle of back projection residual, from which the precision verification is made on the results of the detection of feature points and the results of dense matching, removing those large error points. Finally, reliable3-D point cloud data is obtained which meets the requirement of point accuracy. Experimental data show that the overall reconstruction results, which this paper proposed, achieve the expected target.