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Research on Target Recognition Technology Based on Genetic Algorithm and Fuzzy Clustering

Author: ZhaoGaoPan
Tutor: ZhouDan
School: Shenyang University of Technology
Course: Detection Technology and Automation
Keywords: Target recognition Genetic Algorithms Fuzzy equivalence relation Invariant moments
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 77
Quote: 2
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Abstract


Robot vision system is currently the focus of robotics research, the system relies on the camera and access to outside information to respond to changes in the environment, and target recognition in robot vision system is the key technology. This paper mainly studied in the indoor environment some common goals image recognition technology, including image preprocessing, feature extraction techniques, feature selection techniques to optimize and target recognition technology. In this paper, the robot slow motion state sample image acquisition target object. First, the target image preprocessing operation, the collected convert color images to grayscale image and median filtering, edge detection technique to extract recycling targets edge features, and uses based on mathematical morphology filter edge image processing method Detection of a small amount left over after the background noise. Secondly, feature extraction operation, since the image acquisition process target object and the distance between the robot, orientation is constantly changing, so choose the same moment as the target feature extraction method. In the traditional seven invariant moments are derived on the basis of an additional three high-order invariant moments to extract the target details, while these invariant moments is improved so that the discrete state can still maintain a translation , rotation and scale invariant feature, is more suitable for the computer image of the target feature extraction. Then, optimize operating characteristics, the genetic algorithm improved 9 invariant moments to optimize selection, removing redundant features, compression of the feature space, reducing the amount of computation. Finally, this paper studied based on fuzzy equivalence relation target recognition method, using F statistic to obtain the optimal classification threshold target through the threshold number of categories of clustering, and the center of the class for each feature vector a standard feature with the target vector comparison, thereby completing the target identification. Based on the theoretical study, this paper similar statistics for different target identification done experiments show that the method is feasible and effective; followed by analysis and comparison of the characteristics of the target recognition results before and after optimization, genetic algorithm described the need to optimize characteristics; Finally, the target of the dynamic recognition experiments show that the recognition algorithm is unknown in the case of target categories efficiently identify the target.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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