Dr. JiChun Yang Profile

Dr. JiChun Yang

at Chinese Academy of Medical Sciences

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Proceedings Article | 28 November 2007 Paper

Fast intellective recognition of autocar tire character based on canny operator

Zhan-hua Huang, Ji-chun Yang, Zheng Liu, Tian-hong Zhao

Proceedings Volume 6833, 683310 (2007) https://doi.org/10.1117/12.754027

KEYWORDS: Image processing, Image filtering, Optical character recognition, Detection and tracking algorithms, Feature extraction, Pattern recognition, Edge roughness, Aluminum, Zoom lenses, Signal to noise ratio

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To relieve the inefficient of traditional method on auto car tire character recognition based on hand-copying, in this paper, we present an intellective method of fast recognition. Several algorithms were involved in this method, including polar transformation, improved canny operator and distance fast cluster. The procedure was consisted of several steps. In first step, tire image was rotated by polar transformation and mosaic to form a rectangle area by Bilinear Interpolation algorithm. Compared to other methods this part can reduce system time expense greatly. Next, Histogram Equalization algorithm was used to improve the gray distribution and image contrast, which always low on the foreground and background of image for the curing process. Then, the real edge was extracted by improved canny operator, which has advantages of smoothing image, binarizing image and suppressing the big noise caused by rough surface. Subsequently, based on foregoing results, tire image was divided into several small regions, and the judgment of each region whether belongs to real character region was processed by utilizing the continued similarity and the ratio of black pixel number to white pixel number. Furthermore distance fast cluster was utilized to filter noise and split image, and class merging was also used to complete character split. Finally, character feature vectors were extracted and character pattern recognition was completed using them. This method was tested on a series of experiments, the result shows small time expenses and high recognition ratio, which demonstrated that this method can satisfy the special requirement of fast and effective recognition of autocar tire character.

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