Paper Info
Title
Accurate Detection and Characterization of Corner Points Using Circular Statistics and Fuzzy Clustering
Abstract
Accurate detection and characterization of corner points in grey level images is considered as a pattern recognition problem. The method considers circular statistic tests to detect 2D features. A fuzzy clustering algorithm is applied to the edge orientations near the prospective corners to detect and classify them. The method is based on formulating hypotheses about the distribution of these orientations around an edge, corner or other 2-D feature. The method may provide accurate estimates of the direction of the edges that converge in a corner, along with their confidence intervals. Experimental results show the method to be robust enough against noise and contrast changes. Fuzzy membership improves the results of the algorithm and both versions (crisp and fuzzy) give better results than other previously proposed corner detectors.
Year
DOI
Venue
1998
10.1007/BFb0033293
SSPR/SPR
Keywords
Field
DocType
fuzzy clustering,circular statistics,accurate detection,confidence interval,pattern recognition,statistical test
Fuzzy clustering,Computer vision,Discrete mathematics,Statistic,Pattern recognition,Computer science,Pattern analysis,Fuzzy logic,Artificial intelligence,Detector,Pattern recognition problem
Conference
ISBN
Citations 
PageRank 
3-540-64858-5
0
0.34
References 
Authors
5
5
Name
Order
Citations
PageRank
María Elena Díaz1215.69
Guillermo Ayala29516.13
J. Albert311.83
Francese J. Ferri443.00
Juan Domingo53319258.54