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Research target for detection and localization of directional sub-pixel technology based

Author: ZhouLinZuo
Tutor: JiangChunHua
School: University of Electronic Science and Technology
Course: Applied Computer Technology
Keywords: image recognition subpixel corner detected BP Neural Network
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 70
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Abstract


With the development of video technology,more and more high-quality image is used,the requirement for detecting is increasing too.The target in a complex image and other image are often interfere with each other.The image and the actual test often have a bias, the method of sub-pixel edge algorithm can detect these features and improve image accuracy.Sub-pixel edge detection algorithm based mainly on Zernike orthogonal,a improvement is used to eliminate the template effect of the original algorithm,also reduce the redundancy and improve the efficiency of edge detection and the detection accuracy.To make the image more clear for detection.The preprocessing for image is developed.A more effective algorithm for noise reducing is used.The image is more smooth and clear.With histogram equalization method to enhance the contrast of the image regions of different colors to improve the accuracy of the image local segmentation.Hough image recognition algorithm using the improved phase ellipse detection algorithm, the edge sub-pixel accurate positioning algorithm is used to more accurately find the center of the ellipse and the length of the shaft, and through the parameters of elliptical target of target location and orientation.Target for the feature detection using Harris-SIFT algorithm, by extracting the same deflection characteristics of the target point,the corner points, and calculate the area near the corner feature vector. And feature vector has been simplified. Sift features using a simplified value as the input of neural network to detect the target number.With the result of finally program,the pratical and the efficient of the algorithm is proved.

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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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