• Àüü
  • ÀüÀÚ/Àü±â
  • Åë½Å
  • ÄÄÇ»ÅÍ
´Ý±â

»çÀÌÆ®¸Ê

Loading..

Please wait....

Çмú´ëȸ ÇÁ·Î½Ãµù

Ȩ Ȩ > ¿¬±¸¹®Çå > Çмú´ëȸ ÇÁ·Î½Ãµù > IPIU (¿µ»óó¸® ¹× ÀÌÇØ¿¡ °üÇÑ ¿öÅ©¼¥) > IPIU 1998 (Á¦10ȸ ¿µ»óó¸® ¹× ÀÌÇØ¿¡ °üÇÑ ¿öÅ©¼¥)

IPIU 1998 (Á¦10ȸ ¿µ»óó¸® ¹× ÀÌÇØ¿¡ °üÇÑ ¿öÅ©¼¥)

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) ÆÛÁö ¸â¹ö°ªÀ» ÀÌ¿ëÇÑ ¿¡Áö °ËÃâÀÇ ÆĶó¹ÌÅÍ Æò°¡
¿µ¹®Á¦¸ñ(English Title) Evaluation of Edge Detection Parameters Using Fuzzy Membership
ÀúÀÚ(Author) ±èÅ¿렠 ÇÑÁØÈñ   Tae Yong Kim   Joon H. Han  
¿ø¹®¼ö·Ïó(Citation) VOL 10 NO. 01 PP. 0240 ~ 0247 (1998. 01)
Çѱ۳»¿ë
(Korean Abstract)
¿µ¹®³»¿ë
(English Abstract)
Most edge detection methods have parameters(threshold values or standard deviation of Gaussian operator for smoothing) to be set, and these parameters make much influence on the outputs of the detectors. In this paper we propose a method of parameter evaluation and selection in edge detection methods. We evaluate parameters based on the edge ambiguity of existence, location and formation. The existence and location ambiguity are derived from comparing fuzzy memberships of edgeness with detected edges, and the formation ambiguity assesses the connectness and the quantity of edges. The parameters which produce the least ambiguous edges of a detection method for an image are selected as significant ones. The method does not need iterative visual interaction or prior knowledge of edges. The effectiveness of the method is demonstrated by applying the method to synthetic and real images.
Å°¿öµå(Keyword) edge detection   Parameter evaluation   Fuzzy membership  
ÆÄÀÏ÷ºÎ PDF ´Ù¿î·Îµå