Edge detection of highly distorted images suffering from impulsive noise

AEU - International Journal of Electronics and Communications

Volumn 58, Issue 6, 2004, Pages 413-419

  Civicioglu P ^a   Alci M ^a  

^a ERCIYES UNIVERSITY   (Turkey)

Author keywords

Edge detection; Impulsive noise

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTER SIMULATION; ERROR ANALYSIS; ESTIMATION; IMAGE PROCESSING; IMPULSE NOISE; PROBLEM SOLVING; REMOTE SENSING;

COMPUTATIONAL ALGORITHMS; COMPUTATIONAL STRUCTURES; EDGE DETECTORS; IMAGE PIXELS;

EDGE DETECTION;

EID: 10044227487     PISSN: 14348411     EISSN: None     Source Type: Journal    
DOI: 10.1078/1434-8411-54100262     Document Type: Article

References (22)

1. Russo, F.: Edge detection in noisy images using fuzzy reasoning. IEEE Transactions on Instrumentation and Measurement 47 (1998), 1102-1105.
2. Pratt, W.: Digital image processing. California: John Wiley and Sons, 1991.
3. El-Kham, S.; Ghaleb, I.; El-Yamany, N.: Fuzzy edge detection with minimum fuzzy entropy criterion. IEEE MELECON 2002, Egypt, 2002.
4. Russo, F.: Edge detection in noisy images using fuzzy reasoning. IEEE Instrumentation and Measurement Technology Conference, Minnesota, USA, 1998. 369-372.
5. Sonka, M; Hlavac, V.; Boyle, R.: Image processing, analysis and machine vision. Pacific Grove, California: Brooks/Cole Publishing Company, 1998.
6. Umbaugh, S.: Computer vision and image processing. USA: Prentice Hall, Inc., 1998.
7. Schalkof, R.: Digital image processing and computer vision. Canada: John-Wiley and Sons Inc., 1989.
8. Hongjian, S.; Ward, R.: Canny edge based image expansion. ISCAS-2002, IEEE International Symposium on Circuits and Systems, Scottsdale, Arizona, USA, 2002. 785-788.
9. Cai, J.; Yang, J.; Ding, R.: Fuzzy iteration edge detector. The 2000 IEEE Asia-Pacific Conference on Circuits and Systems, Tianjin, China, 2000. 132-135.
10. Suzuki, K.; Horiba, I.; Sugie, N.: Edge detection from noisy images using a neural edge detector. IEEE Workshop on Neural Networks for Signal Processing X. Sydney, Australia, 2000. 487-496.
11. Haseyama, M.; Kitajima, H.; Uchiyama, Y.: Hopfield neural networks for edge detection. The 2001 IEEE International Symposium on Circuits and Systems, ISCAS 2001, Sydney, Australia, 2001. 608-611.
12. Siyal, M.; Zhengquan, H.: Edge detection with bp neural networks. Fourth International Conference on Signal Processing Proceedings, ICSP '98, Beijing, China, 1998. 1382-1384.
13. Frei, W.; Chen, C.: Fast boundary dedection: A genaralization and a new algorithm. IEEE Transactions on Computers 26 (1977), 988-998.
14. Huertas, A.; Medioni, G.: Detection of intensity changes with subpixel accuracy using laplacian-gaussian masks. IEEE Transactions on Pattern Analysis and Machine Intelligence 8 (1986), 651-664.
15. Chen, T.; Wu, H.R.: Space variant median filters for the restoration of impulse noise corrupted images. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 48 (2001), 784-789.
16. Christopher, L.; Abdelhak, M.: Testing for impulsive behavior: A bootstrap approach. Digital Signal Processing 11 (2001), 120-132.
17. Çivicioǧlu, P.; Alçi, M.: Impulsive noise suppression from highly distorted images with triangular interpolants. Accepted to be published in AEU International Journal of Electronics and Communications (2004).
18. El-Kwae, E.; Gudmundsson, M.; Kabuka, M.: Edge detection in medical images using a genetic algorithm. IEEE Transactions on Medical Imaging 17 (1998), 469-474.
19. Yitzhaky, Y.; Peli, E.: A method for objective edge detection evaluation and detector parameter selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (2003), 1027-1033.
20. Bezdek, J.; Chandrasekhar, R.; Attikiouzel, Y.: A geometric approach to edge detection. IEEE Transactions on Fuzzy Systems 6 (1998), 52-75.
21. Elmabrouk, A.; Aggoun, A.: Edge detection using local histogram analysis. Electronics Letters 34 (1998), 1216-1217.
22. Brejl, M.; Sonka, M.: Object localization and border detection criteria design in edge-based image segmentation: Automated learning from examples. IEEE Transactions on Medical Imaging 19 (2000), 973-985.