Research On Binocular Stereo Vision And Reverse Engineering

In this paper, a different passive binocular stereo vision system to get 3D surface reconstruction is presented. An in-depth research is done in vision system calibration, image matching, dense point cloud reconstruction, subdivision surface fitting and so on. Moreover, there is also some useful work on rapid computation of convergence criterion of subdivision rules. Finally, some examples are given by applying the binocular stereo vision system to face recognition.The main research achievements are as follows in detail:Based on complex wavelet, a phase correlation algorithm is put forward to match images intensively. Considering order matching constraint, continuity constraint and correlation constraint conditions, multiresolution image regional matching is done through peak fitting and iteration of sub-pixel displacement factor, which leads to sub-pixel real-time matching results for corresponding points. This method makes it feasible to overcome boundary jumping and other problems caused by the periodicity of traditional Fourier transform. Meanwhile, it has coarse-to-fine multiresolution capacity to match and search, which Gabor transform don't have. In addition, it has higher robust for light changing and could reconstruct 3D point cloud information densely. Finally, subdivision surface fitting technique is applied to get object-level local refinement of the surface reconstruction.A passive binocular stereo vision system with adaptive baseline is introduced. Here, "adaptive" means the length of baseline can be drived adaptively by SCM (Single Chip Micyoco) according to sample distance and required accuracy. Since adjusting baseline and shakable shooting may change the stereo calibrated parameters, a new semi-automatic calibration technology is put forward, which can gain new real-time calibration accurately. Furthermore, in order to improve the synchronization sampled by traditional dual-camera, a new passive binocular stereo vision system based on single camera is presented, as well as corresponding calibration algorithm, which solves the auto-calibration problem of non-pinhole model.There is also some work on the convergence analysis of subdivision method. It is well-known that the p-norm joint spectral radius is defined by a bounded collection of square matrices with complex entries and of the same size, which is used as convergence criterion of subdivision rules. The p-norm joint spectral radius for integers is investigated here, as well as some basic formulas and a simple proof of Berger-Wang's relation concerning the∞-norm joint spectral radius. In addition, estimate of the eigenvalues of subdivision stochastic matrices is studied here, as well as some search algorithms from graph theory.At last, an improved 3D face recognition method as well as a new binocular stereo vision system based on single camera are proposed in this paper. Under the assumption that face is symmetrical, the point cloud is optimized automatically by filling holes and correcting. Then, simplified CANDIDE-3 model is used as initial subdivision controlling mesh, refined locally and levelly fitted. Meanwhile, Geodesic mapping technique is applied to normalize different expressions and face database is built respectively. Furthermore, pyramid structure is employed to compare and recognize 3D faces, which is also suitable for reverse seeking 3D face information. Experiments show that the new stereo vision system not only improves reconstruction accuracy, but also avoids robust decreasing caused by non synchronous shooting of two cameras. Moreover, subdivision surfaces used as storage can save space and provide theoretical support for comparison. Considering its low cost, the system is feasible to spread in many fields.