Stereo Matching Method With Color Image Based On Neural Network For Micro Stereo Measurement

Stereo vision system is a visual system composed of a number of industrialcameras and PC. It can reconstruct the three-dimensional shape of the object byobserving it from more than one viewpoint. The Micro stereo vision system is anadvanced stereo vision system developed in the1990s. Its main component isMicroscope and it is widely used in Microscopic field such as micro-operations, microassembly, biological medicine etc. Stereo matching is an important part of Microstereo vision because it affects the reconstruction accuracy directly. Now it hasbecome a very active research area, many scholars are involved in the research, butthere is still not a qualitative approach that can solve this problem perfectly. In orderto improve the accuracy of the micro-vision stereo matching, a new method integratedof a neural network application was raised in this paper, which obviously improvesthe accuracy of stereo matching. This paper has more study on micro-vision systemmechanism, the microscopic color image pre-processing and micro stereo matching.Firstly, the mechanism of micro-vision system was analyzed and built a visionmodel for the later three-dimensional reconstruction; then constructed themicro-vision system calibration method based on LMS statistical calibration method;at last analyzed the microscopic color image preprocessing by using the existing colorimage pre-processing methods to do image filtering, enhancement and segmentationaccording to the characteristics of the microscopic image. The image filtering methodis combined with vector median filter and vector directional filter; the imageenhancement method is based on gray-scale transformation; the image segmentationused methods of threshold segmentation, regional growth, as well as interactive imagesegmentation, at the last of this chapter, we get the advantage and disadvantage of allthe methods by doing experiments, and at last we know which is the best method forour system.Secondly, the existing regional matching algorithms were analyzed, and then thecolor similarity in a RGB color image was defined. Considering the hamming distancebased on the Rank transform, combined with color similarity to form two new colorimage matching algorithms based on ZNCC algorithm and ZSAD algorithmseparately. The accuracy of the two new algorithms was verified by experiments at theend of this chapter and a comparison of experimental results was made at last.Furthermore, DHNN and CHNN were described and its stability was proved.Then study the constraints of stereo matching of micro color image, build the optimal stereo matching relations by using the color similarity combining with Rank transformand the ZNCC algorithm. At last, build the Energy function of Hopfield network withall these relations combined with the inherent constraints of computer stereo vision.Finally, by doing the experiments of the new method mentioned in the forthchapter based on the platform of VC++6.0, we got the results that show the accuracyof stereo matching and noise immunity are partially improved by using Hopfieldnetwork algorithm compared to ZNCC algorithm.