Please wait a minute...
ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2000, Vol. 1 Issue (4): 398-401    DOI: 10.1631/jzus.2000.0398
Science & Engineering     
TRAINING A NEURAL NETWORK FOR MOMENT BASED IMAGE EDGE DETECTION
WANG Hong-yu, LI Hong-dong, YE Xiu-qing, GU Wei-kang
Department of Information and Electronics, Zhejiang University, Hangzhou, 310027, China
Download:     PDF (0 KB)     
Export: BibTeX | EndNote (RIS)      

Abstract  Edge detection is a crucial step to computer vision. Currently, there is not a single edge detector that has both efficiency and reliability. Traditional differential filter-based algorithms have the advantage of theoretical strictness , but require excessive post-processing. This paper introduces a neural network edge detector that takes advantage of moments features. It functions as a neural pattern classifier that directly estimates the posterior probability from the training data set. Two subsystems can be distinguished and different kinds of learning rules are used. For the end-user, it works as a black box that directly transforms raw images into the edge maps so no complicated postprocessing is required. Tests on both simulated and real images showed the proposed neural network edge detector is superior to traditional operators.

Key words： neural network      edge detection      image processing     
Received: 06 April 1999     
CLC:  TP391.41  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
WANG Hong-yu
LI Hong-dong
YE Xiu-qing
GU Wei-kang
Cite this article:

WANG Hong-yu, LI Hong-dong, YE Xiu-qing, GU Wei-kang. TRAINING A NEURAL NETWORK FOR MOMENT BASED IMAGE EDGE DETECTION. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2000, 1(4): 398-401.

URL:

http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.2000.0398     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2000/V1/I4/398

[1] Long Yan, Qing-xiang Meng, Wei-ya Xu, Huan-ling Wang, Qiang Zhang, Jiu-chang Zhang, Ru-bin Wang. A numerical method for analyzing the permeability of heterogeneous geomaterials based on digital image processing[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2017, 18(2): 124-137.
[2] Hossein Rezaei, Ramli Nazir, Ehsan Momeni. Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2016, 17(4): 273-285.
[3] Yun-luo Yu, Wei Li, De-ren Sheng, Jian-hong Chen. A hybrid short-term load forecasting method based on improved ensemble empirical mode decomposition and back propagation neural network[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2016, 17(2): 101-114.
[4] Pijush Samui, Dookie Kim, Bhairevi G. Aiyer. Pullout capacity of small ground anchor: a least square support vector machine approach[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2015, 16(4): 295-301.
[5] José D. Martínez-Morales, Elvia R. Palacios-Hernández, Gerardo A. Velázquez-Carrillo. Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2013, 14(9): 657-670.
[6] Arturo Garcia-Perez, Juan P. Amezquita-Sanchez, Aurelio Dominguez-Gonzalez, Ramin Sedaghati, Roque Osornio-Rios, Rene J. Romero-Troncoso. Fused empirical mode decomposition and wavelets for locating combined damage in a truss-type structure through vibration analysis[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2013, 14(9): 615-630.
[7] Peng-hui Zhang, Jin-liang Zhang, Wei-wei Ren, Jun Xie, Ming Li, Jing-zhe Li, Fang Ding, Jin-kai Wang, Zi-rui Dong. Identification of waterflooded zones and the impact of waterflooding on reservoir properties of the Funing Formation in the Subei Basin, China[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2013, 14(2): 147-154.
[8] Ya-fei Zhou, Mao Liu, Qiong Wu. Water quality improvement of a lagoon containing mixed chemical industrial wastewater by micro-electrolysis-contact oxidization[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2011, 12(5): 390-398.
[9] Yi-qun DENG, Pei-ming WANG. Predicting the shrinkage of thermal insulation mortar by probabilistic neural networks[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2010, 11(3): 212-222.
[10] Abdelhakim Deboucha, Zahari Taha. Identification and control of a small-scale helicopter[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2010, 11(12): 978-985.
[11] Peng Chen, Yong-zai Lu. Memetic algorithms-based neural network learning for basic oxygen furnace endpoint prediction[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2010, 11(11): 841-848.
[12] Hong-li QI, Hui ZHAO, Wei-wen LIU, Hai-bo ZHANG. Parameters optimization and nonlinearity analysis of grating eddy current displacement sensor using neural network and genetic algorithm[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2009, 10(8): 1205-1212.
[13] Eun-jung HAN, Chee-onn WONG, Kee-chul JUNG, Kyung-ho LEE, Eun-yi KIM. Efficient page layout analysis on small devices[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2009, 10(6): 800-804.
[14] Chun-hua LI, Xin-jian ZHU, Guang-yi CAO, Wan-qi HU, Sheng SUI, Ming-ruo HU. A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2009, 10(2): 263-270.
[15] Tsung-lin LEE, Hung-ming LIN, Yuh-pin LU. Assessment of highway slope failure using neural networks[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2009, 10(1): 101-108.