DocumentCode :
1623044
Title :
Adaptive edge detecting approach based on scale-space theory
Author :
Changsheng, Xu ; SongDe, Ma
Author_Institution :
Nat. Lab. of Pattern Recognition, Acad. Sinica, Beijing, China
Volume :
1
fYear :
1997
Firstpage :
130
Abstract :
The main problem existing in many edge detecting approaches is that they are sensitive to the noise. There is a conflict between the precision of edge detection and the effect of the noise removal. The scale-space theory on the basis of the optimal edge detecting theory is introduced in this paper. Each specific edge corresponds to an optimal filter scale (OFS) and the value of the OFS of each edge point is calculated based on the optimal edge detecting theory. An automatic threshold is introduced in the process of edge detection in order to eliminate the ragged edge further. Based on the optimal scale and the automatic threshold, a fast adaptive edge detecting algorithm is proposed. Experiment results demonstrate that this algorithm can get either the noise removing effect of low-resolution filter or the edge detecting precision of high-resolution filter and make a better compromise between the precision of edge detection and the effect of the noise removal
Keywords :
Gaussian processes; adaptive systems; edge detection; filtering theory; interference suppression; adaptive edge detecting algorithm; automatic threshold; edge detecting; high-resolution filter; low-resolution filter; noise removal; optimal edge detecting theory; optimal filter scale; scale-space theory; Adaptive filters; Automation; Detectors; Equations; Filtering theory; Gaussian noise; Image edge detection; Signal to noise ratio; Stochastic processes; White noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Instrumentation and Measurement Technology Conference, 1997. IMTC/97. Proceedings. Sensing, Processing, Networking., IEEE
Conference_Location :
Ottawa, Ont.
ISSN :
1091-5281
Print_ISBN :
0-7803-3747-6
Type :
conf
DOI :
10.1109/IMTC.1997.603929
Filename :
603929
Link To Document :
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