DocumentCode :
288884
Title :
Contribution of Canny-Deriche filter and artificial neural networks to image segmentation
Author :
Koffi, R. ; Solaiman, B. ; Mouchot, M.C.
Author_Institution :
Ecole Nat. Superieure des Telecommun. de Bretagne, Brest, France
Volume :
6
fYear :
1994
fDate :
27 Jun- 2 Jul 1994
Firstpage :
4044
Abstract :
In this paper, an image segmentation method based on an edge detection view is presented. This method uses the contribution of two approaches: the optimal edge detector proposed by J. Canny, and then extended to the optimal recursive filter by R. Deriche; and the artificial neural network approach. Combining these two methods, the hysteresis stage needed in the technique developed by Deriche is avoided without damaging the segmentation result. Therefore, thresholds required in hysteresis phase and, which are usually quite difficult to find are no more needed. Experimental results show the validity of this method
Keywords :
edge detection; image segmentation; neural nets; recursive filters; Canny-Deriche filter; artificial neural networks; edge detection; image segmentation; optimal edge detector; optimal recursive filter; Artificial neural networks; Detectors; Filtering; Finite impulse response filter; Humans; Hysteresis; IIR filters; Image edge detection; Image segmentation; Signal to noise ratio;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
Type :
conf
DOI :
10.1109/ICNN.1994.374861
Filename :
374861
Link To Document :
بازگشت