Title: Study on image feature recognition algorithm and its application in public security management

Authors: Xiaoyi Yang; Qian Wu; Xinmei Deng

Addresses: School of Education, Chongqing Normal University, No. 37 Daxuecheng Middle Road, Shapingba District, Chongqing, 401331, China ' School of Computer Science and Engineering, Chongqing University of Technology, No. 69 Hongguang Avenue, Banan District, Chongqing, 400054, China ' Library, Chongqing University of Science and Technology, No. 20 Daxuecheng East Road, Shapingba District, Chongqing, 401331, China

Abstract: Public security is the topic of common concern of the government and the common people. In order to solve the puzzle of image distortion, being complex in algorithm and being difficult to take into account of the overall structure and details of the image in the image recognition algorithm of public security management system, the paper presented a fusion algorithm of texture consistency measure based on bi-orthogonal wavelet transform. By means of the orthogonal wavelet transform, the wavelet transform is used to decompose the source image, and then the low frequency and high frequency wavelet coefficient matrix of the fused image is determined according to a certain proportion and texture measure, thus the fusion image is obtained. The experimental results show that the algorithm can not only distinguish the false edges of the image, but also enrich the details of the image and take into account the overall visual image, so it can better improve the recognition effect of the image in the public security management system.

Keywords: public security; security management system; public security management; government concern; image distortion; overall structure; details information; orthogonal wavelet transform; wavelet coefficient matrix; texture measure; false edges; enriching details.

DOI: 10.1504/IJITM.2019.099814

International Journal of Information Technology and Management, 2019 Vol.18 No.2/3, pp.284 - 296

Received: 19 Jul 2017
Accepted: 17 Oct 2017
Published online: 23 May 2019 *

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