DocumentCode :
3516700
Title :
Effective edge-corner detection method for defected images
Author :
Lai, Kwok-Kei ; Wu, Paul S Y
Author_Institution :
Dept. of Manuf. Eng., City Univ. of Hong Kong, Kowloon, Hong Kong
Volume :
2
fYear :
1996
fDate :
14-18 Oct 1996
Firstpage :
1151
Abstract :
The locations of junctions, parallelism of lines and inclined angles of lines are difficult to be ascertained but essential in the geometrical transformation into objects from images. A fast and effective edge-corner detection method, named the cellular vectorization method (CVM), is proposed. This method is particularly effective in hand line drawings containing imperfections, such as unattached edges, misaligned junctions, overshoot junctions, and roughly parallel lines. All such defects can be detected and corrected. When in use, the CVM interprets hand line drawings directly and does away with filtering and thinning processes. Thus, the CVM is both a time-saving and versatile method for image detection based on edges and corners data. Experimental results are given to show the correctness and effectiveness of the proposed method
Keywords :
edge detection; feature extraction; image recognition; cellular vectorization method; defective images; edge-corner detection method; experimental results; feature extraction; feature modification; geometrical transformation; hand line drawings; image detection; inclined angles; junction locations; line parallelism; misaligned junctions; objects; overshoot junctions; pattern recognition; roughly parallel lines; unattached edges; Feature extraction; Filtering; Image edge detection; Image processing; Image recognition; Information analysis; Labeling; Manufacturing; Pattern recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing, 1996., 3rd International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-2912-0
Type :
conf
DOI :
10.1109/ICSIGP.1996.566326
Filename :
566326
Link To Document :
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