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Study On The Camera Calibration Approach And Algorithm Of Edge Detection And Contour Tracking

Posted on:2003-11-10Degree:DoctorType:Dissertation
Country:ChinaCandidate:Y YuanFull Text:PDF
GTID:1118360065956244Subject:Mechanical Manufacturing and Automation
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Computer vision is an active and challenging research field, its research area and applications area spread widely. Based on developing of vision recognition system of Micro-Robot World Cup Soccer Tournament, this dissertation is devoted to investigating some related techniques in computer vision, which include edge detection and contour tracking, conventional calibration techniques, self-calibration techniques, implicit calibration based on artificial neural networks.Edge detection and contour tracking are very important in computer vision. Because the single pixel edges are needed in computer vision, an algorithm of edge detection and contour tracking is proposed using the good local character and multi-scale character of wavelet transform in the dissertation. The fuzzy algorithm is applied to pick the model maximum points, so that the single pixel edge can be obtained. Edge image was tracked through using the image information sufficiently. Through finding local peak in a little line adjacent which is perpendicular to the edge direction, the missing edge can be compensated. The experimental results show that the single pixel edge can be obtained, and continuous contours can be extracted.In computer stereo vision, the primary problem to solve is the relations between the 3D points and the 2D image points. So the camera calibration is the premise and the basic problem. There are conventional calibration and self-calibration in camera calibration techniques. Since the cameras lens used in computer vision sustain a lot of nonlinear distortion, recent research efforts has been concentrated on the distortion correction techniques. The optimization algorithms in conventional calibration field have the shortcoming that the computing quantity is huge. In order to improve it, a single adaptive neuron algorithm was developed in conventional camera calibration in this dissertation, and a calibration algorithm considering radial and tangent distortion based on single adaptive neuron is proposed. Compared with the ordinary optimization algorithm of calibration, this algorithm gains simplicity, less computing quantity, and also keeping high accuracy .Camera self-calibration is becoming the important field of calibration research. Camera self-calibration based on active vision makes the problem simplified taking advantage of controlling camera to do known movement. The existed self-calibration algorithms based on translation motion have the limit in constraining the translation too strictly and can not get the external parameters. A linear camera self-calibration approach is proposed in this dissertation. The intrinsic and external parameters can be calibrated linearly by controlling the camera to undergo 3 translations or more which are not co-planar. Based on this algorithm a self-calibration approach taking account of camera two-degree radial distortion is proposed. The five intrinsic parameters and two-degree radial distortion coefficients can be calibrated by controlling the camera to undergo more than 4 translation motion which are not co-planar.inCompared with other methods of self-calibration, these algorithm gains simplicity, strong robustness and high accuracy.The equations derived are more complicated if more precise model is employed for high accuracy. The technique, which doesn't need to have an explicit model - calibration based on neural networks implicit vision model, is more effective. Since BP neural network can implement any nonlinear relationship from input to output and needn't to model, and the classical stereo vision approach based on explicit model are very complicated, an algorithm of stereo vision based on BP neural networks implicit vision model is proposed. This algorithm gains simplicity and convenience. The correction of camera distortion is a main part of camera calibration. An algorithm of camera distortion correction based on BP neural networks is proposed in this dissertation. In the special applications that the 3D points are coplanar, a 2D plane calibration algorithm b...
Keywords/Search Tags:computer vision, edge detection and contour tracking, camera calibration, single adaptive neuron, neural networks, active vision, color model, MIROSOT
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