Title :
A Clustering-based Algorithm for Extracting the Centerlines of 2D and 3D Objects
Author :
Ferchichi, Seifeddine ; Wang, Shengrui
Author_Institution :
Sherbrooke Univ., Que.
Abstract :
This paper presents a new algorithm for extracting the centerlines of 2D and 3D objects, based on clustering. The algorithm computes the centerline from all points of the object in order to remain faithful to the structure of the shape. The idea is to cluster a data set constituted of the points composing the object and their relative distance transforms. The centerline is derived from the set of computed clusters. The proposed method is accurate and robust to noisy boundaries
Keywords :
feature extraction; object detection; pattern clustering; transforms; 2D object; 3D object; centerlines extraction; clustering-based algorithm; distance transform; Biomedical imaging; Clustering algorithms; Computer vision; Data mining; Noise shaping; Object recognition; Robustness; Shape; Skeleton; Solid modeling;
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2521-0
DOI :
10.1109/ICPR.2006.44
