基于双边缘检测的目标定位技术

doi:10.3969/j.issn.1006-2475.2016.08.012

计算机与现代化

基于双边缘检测的目标定位技术

(1.中国航天科工集团第二研究院研究生院，北京 100854; 2.北京计算机技术及应用研究所，北京 100854)

收稿日期:2016-02-18 出版日期:2016-08-18 发布日期:2016-08-11
作者简介:杜默(1991-)，女，内蒙古赤峰人，中国航天科工集团第二研究院研究生院硕士研究生，研究方向：模式识别; 王清理(1969-)，男，北京计算机技术及应用研究所研究员，硕士生导师，硕士，研究方向：嵌入式计算机。

Target Location Based on Double-edge Detection

(1. Graduate School, Second Research Academy of CASIC, Beijing 100854, China;
2. Institute 706, Second Research Academy of CASIC, Beijing 100854, China)

Received:2016-02-18 Online:2016-08-18 Published:2016-08-11

摘要/Abstract

摘要： 为了提高目标定位的准确性，在传统的高斯差分边缘检测和Sobel算子边缘检测的基础上，提出将二者相结合的双边缘检测算法。对原始图像进行2次标准差不同的高斯模糊求高斯差分来代替原本边缘检测图像预处理中的高斯平滑过程；对实验结果较好的高斯差分参数，进行Sobel边缘检测并进行目标定位。实验结果表明，在算法时间开销增加较少的情况下，使用高斯差分算子与Sobel算子相结合进行边缘检测的结果优于Sobel算子单独检测，检测效果较好。

关键词: 双边缘检测, 高斯差分(DoG), Sobel算子, 目标定位, 目标检测

Abstract: In order to improve the accuracy of target location, based on the traditional difference of Gaussian edge detection and Sobel operator edge detection, this paper puts forward a double edge detection algorithm which combines the two. We use the difference of Gaussian to replace the Gaussian smoothing in image preprocessing, choose better results of difference of Gaussian parameters, use Sobel edge detection after the difference of Gaussian process, and find the target location. The experimental results show that combining difference of Gaussian with Sobel operator do not increase much running time overhead, and have better detection result than the Sobel test alone.

Key words: double-edge detection, difference of Gaussian (DoG), Sobel operator, target location, target detection

中图分类号:

TP391.4

杜默1,2，王清理2. 基于双边缘检测的目标定位技术[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2016.08.012.

DU Mo1,2, WANG Qing-li2. Target Location Based on Double-edge Detection[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2016.08.012.

参考文献

[1] Marr D, Hildreth E. Theory of edge detection[J]. Proceedings of the Royal Society of London, Series B, Biological Sciences, 1980,207(1167):187-217.

[2] Marr D, Ullman S, Poggio T. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information[M]. San Francisco: The MIT Press, 2010.

[3] Birch P, Mitra B, Bangalore N M, et al. Approximate bandpass and frequency response models of the difference of Gaussian filter[J]. Optics Communications, 2010,283(24):4942-4948.

[4] Kamalakannan S, Gururajan A, Sari-Sarraf H, et al. Double-edge detection of radiographic lumbar vertebrae images using pressurized open DGVF snakes[J]. IEEE Transactions on Bio-medical Engineering, 2010,57(6):1325-1334.

[5] Iwabuchi S, Kakazu Y, Koh J-Y, et al. Evaluation of the effectiveness of Gaussian filtering in distinguishing punctate synaptic signals from background noise during image analysis[J]. Journal of Neuroscience Methods, 2014,223:92-113.

[6] Wang Shoujia, Li Wenhui, Wang Ying, et al. An improved difference of Gaussian filter in face recognition[J]. Journal of Multimedia, 2012,7(6):429-433.

[7] Gonzalez C I, Melin P, Castro J R, et al. An improved Sobel edge detection method based on generalized type-2 fuzzy logic[J/OL]. Soft Computing, 2014-12-16.

[8] Elmahdy S I. Change detection and mapping of mangrove using multi-temporal remote sensing data; a case study of Abu Dhabi, UAE[J]. Journal of Geomatics, 2013,7(1):41-46.

[9] Lavanya K B, Ramana Reddy K V, Yellampalli S S. Comparative analysis of different optimization technique for Sobel edge detection on FPGA[C]// Proceedings of the 2014 International Conference on Advances in Electronics, Computers and Communications (ICAECC). 2014.

[10] Sun Jing. Image edge detection based on relative degree of grey incidence and Sobel operator[C]// Proceedings of the 2012 International Conference on Artificial Intelligence and Computational Intelligence. 2012:762-768.

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基于双边缘检测的目标定位技术

Target Location Based on Double-edge Detection

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