TY - GEN
T1 - A comparative evaluation of competitive learning algorithms for edge detection enhancement
AU - Sirin, Tuba
AU - Saglam, Mehmet Izzet
AU - Erer, Isin
AU - Gokmen, Muhittin
AU - Ersoy, Okan
PY - 2005
Y1 - 2005
N2 - Most edge detection algorithms include three main stages: smoothing, differentiation, and labeling. In this paper, we evaluate the performance of algorithms in which competitive learning is applied first to enhance edges, followed by an edge detector to locate the edges. In this way, more detailed and relatively more unbroken edges can be found as compared to the results when an edge detector is applied alone. The algorithms compared are K-Means, SOM and SOGR for clustering, and Canny and GED for edge detection. Perceptionally, best results were obtained with the GED-SOGR algorithm. The SOGR is also considerably simpler and faster than the SOM algorithm.
AB - Most edge detection algorithms include three main stages: smoothing, differentiation, and labeling. In this paper, we evaluate the performance of algorithms in which competitive learning is applied first to enhance edges, followed by an edge detector to locate the edges. In this way, more detailed and relatively more unbroken edges can be found as compared to the results when an edge detector is applied alone. The algorithms compared are K-Means, SOM and SOGR for clustering, and Canny and GED for edge detection. Perceptionally, best results were obtained with the GED-SOGR algorithm. The SOGR is also considerably simpler and faster than the SOM algorithm.
UR - https://www.scopus.com/pages/publications/84863647202
M3 - Conference contribution
AN - SCOPUS:84863647202
SN - 1604238216
SN - 9781604238211
T3 - 13th European Signal Processing Conference, EUSIPCO 2005
SP - 850
EP - 853
BT - 13th European Signal Processing Conference, EUSIPCO 2005
T2 - 13th European Signal Processing Conference, EUSIPCO 2005
Y2 - 4 September 2005 through 8 September 2005
ER -