Title :
A constrained clustering algorithm for shape analysis with multiple features
Author :
Marques, Jorge S. ; Abrantes, Arnaldo J.
Author_Institution :
Inst. de Sistemas e Robotica, Inst. Superior Tecnico, Lisbon, Portugal
Abstract :
This paper extends a class of constrained clustering methods for shape estimation by using the concept of extended features. The extended features consist of edge points and associated image properties, e.g., gradient, texture and color. Experimental results show that the use of extended features improves the performance of the algorithm in the presence of cluttered background
Keywords :
edge detection; feature extraction; image colour analysis; image texture; constrained clustering; edge points; feature extraction; image color; image gradient; image texture; shape estimation; Active contours; Algorithm design and analysis; Clustering algorithms; Clustering methods; Data mining; Feature extraction; Image edge detection; Robustness; Sensor fusion; Shape;
Conference_Titel :
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
0-7695-0750-6
DOI :
10.1109/ICPR.2000.905586
