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The Study of Artificial Target Detection Based on Fractal Feature

Author: ZhangFeng
Tutor: ZouHuanXin
School: National University of Defense Science and Technology
Course: Information and Communication Engineering
Keywords: Artificial target detection Fractal dimension Fractal characteristics
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 16
Quote: 0
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Abstract


Artificial target detection is an important research topic in image processing and computer vision is the basis of the target tracking and recognition , and has a wide range of applications in intelligent transportation, urban road detection , and military target detection . The study artificial target detection has great significance , and fractal better distinction between man-made targets and natural background . In this paper, for high-resolution visible light image to carry out artificial target detection method based on fractal characteristics . This paper systematically summarizes the basic theory of fractal , six typical sub Viti -fetch algorithms to be verified by experiment , analysis and comparison , better differential box-counting method and Carpet analysis for subsequent experiments . Four different fractal characteristics were extracted based on the above two methods , and compare the effect of four different fractal characteristics of the artificial target detection . In summary and analysis of four different features based on a new combination of fractal characteristics , the features without increasing the computational complexity based on significantly enhancing the natural background and artificial goal difference in the characteristics . The classic two-parameter CFAR detection algorithm based on a quick CFAR detection algorithm , compared to the original algorithm, the algorithm can significantly improve the detection speed . A combination of fractal characteristics of artificial target fast detection algorithm . Firstly, the image SUSAN detection , and then extract the combination of fractal characteristics of the test results area and the surrounding Finally, the extracted features the fast constant false alarm rate (CFAR) , and that test results . Experiments show that the algorithm can effectively detect the target has a higher detection rate and low false alarm rate and faster detection speed .

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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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