Research On Several Problems Of Computer Vision Inspection And Its Application

Computer vision detection is a technology of simulating human vision to classify, identify and detect objects detected by using computer image processing systems. It has advantages of high performance, more parameters, non-attachment, result-objectiveness and etc in detection. With the development of technologies in computer vision, pattern recognition, digital image processing and the promotion in computer hard wares, Computer vision detection techniques are getting more and more widely used in many areas. As application requirements appear continuously,they arise many new study topics for researching in computer vision detection and related theory. This dissertation studies problems about overlapping object separating, dynamic object characteristic description and image matching and their applications in real computer vision detection systems. The chief works are as follows:(1) It proposes an overlapping object separating algorithm based on fuzzy distance transform. Combining fuzzy distance transform and watershed algorithm, it constructs the overlapping object separating algorithm and promotes the separating effect in the case of object edge being fuzzy.(2) It studies proportion invariance of moment invariance in discrete condition,corrects the proof error in related literature, infers the result that in Hu's seven moments,φ2 ~φ7 still keep scale invariance, butφ1 does not in discrete situation. And then, it suggests blurred boundary moment invariance to describe blurred objects and proves its equivalence to that of clear boundary object moment. Using this result, we are not necessary to precisely extract its crisp edge at first when extracting boundary invariable moment from blurred images. This makes it convenient to extract boundary invariable moment features and promotes the robustness of boundary invariable moment feature extracting.(3) It puts forward dynamic object feature extracting method based on project moment. This method firstly overlays the dynamic object images in several frames in the sliding windows onto a projection plane, and then it uses the moment invariance of the projection image to describe the dynamic distribution of the object. Compared with features in using single frame, it increases the discrimination degree of classification and is of higher practice.(4) It improves quantum genetic algorithm using clone selection mechanism and enhances local searching ability. Applying improved quantum genetic algorithm in the matching of a corner based image to be detected and a template image, we perform a detection with face fault.(5) It introduces applications of the above methods in a silkworm Ovum counting system, a ship cabin fire detection system and a printing matter fault detection system.