An Edge Detection Method Based on Adaptive Differential Operator

Abstract:

An edge detection method based on adaptive differential operator is proposed in this paper. Firstly, standard local edge models are built. And these edge models are described with four-bit-binary code (FBBC) which is obtained from weighted mean values in four directions. Then, based on weighted gray values in four directions, different differential operator templates are defined. And FBBC is used to build the matching between differential operator templates and edge models. Experiments show that this edge detection method with adaptive differential operator can smooth noise and has satisfactory edge detection result.

• Periodical: Applied Mechanics and Materials (Volumes 713-715)
• Pages: 415-419
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.713-715.415
• Online since: January 2015
• Authors: Rong Fen Gong*, Mao Xiang Chu
• Keywords: Adaptive Differential Operator, Edge Detection, FBBC, Local Edge Model

References

[1] Qiong Zhou, Qi An. Edge detection of steel plates at high temperature using image measurement [J]. Frontiers of Mechanical Engineering in China, 2009, 4(1) 77-82.

DOI: 10.1007/s11465-009-0013-1

[2] Shichun Peng, Jian Liu, Guoping Yang. Medical image edge detection based on EMD method [J]. Wuhan University Journal of Natural Sciences, 2006, 11(5) 1287-1291.

DOI: 10.1007/bf02829253

[3] Wei Fu, Guangzhong Xing, Lantian Hou, Qiming Qin, Wenjun Wang. An applied research on approach of dyadic wavelet transform for Remote Sensing image edge detection [J]. Journal of Electronics (China), 2006, 23(4) 535-538.

DOI: 10.1007/s11767-004-0193-0

[4] Seizaburo Niitsuma, Koshin Tokunaga. Edge detection and curvature calculation in the visual system [J]. Artificial Life and Robotics, 2005, 9(3) 135-138.

DOI: 10.1007/s10015-004-0326-4

[5] Bicheng Li, Tianqiang Peng, Bo Peng. Intelligent image processing technology [M]. Beijing: Publishing House of Electronics Industry, 2004. (In Chinese).

[6] Pratt W K. Digital image processing: PIKS inside, 3rd edition [M]. New York: Wiley-Interscience, (2001).

[7] Haralick R. Digital step edges from zero crossing of second directional derivatives [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(1) 58-68.

DOI: 10.1109/tpami.1984.4767475

[8] Ziou D, Tabbone S. Edge detection techniques - an overview [J]. International Journal of Pattern Recognition and Image Analysis, 1998, 8(4): 537-559.

[9] Pellegrino F A, Vanzella W. Edge detection revisited [J]. IEEE Transactions on Systems, Man and Cybernetics, 2004, 34(3) 1500-1518.

DOI: 10.1109/tsmcb.2004.824147

[10] Maoxiang Chu, Anna Wang and Rongfen Gong. An image filtering algorithm for removing mixed noise [C]. Applied Mechanics and Materials, 2013, 433-435, 383-388.

DOI: 10.4028/www.scientific.net/amm.433-435.383

[11] Mu Kang, Qinggong Xu, Baoshu Wang. A roberts' adaptive edge detection method [J]. Journal of Xi'an Jiaotong University, 2008, 42(10) 1240-1244. (In Chinese).