標題: Possibilistic Clustering of Generic Shapes Derived from Templates
作者: Wang, Tsaipei
資訊工程學系
Department of Computer Science
公開日期: 2008
摘要: We present in this paper a new type of alternating-optimization based possibilistic c-shell algorithm for clustering template-based shapes. A cluster prototype consists of a copy of the template after translation, seating, rotation, and/or affine transformations. We use a number of two-dimensional data sets, both synthetic and from real-world images, to illustrate the capability of our algorithm in detecting generic template-based shapes in images. We also describe a progressive clustering procedure aimed to relax the requirements of known number of dusters and good initialization.
URI: http://hdl.handle.net/11536/2641
ISBN: 978-1-4244-1818-3
ISSN: 1098-7584
期刊: 2008 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5
起始頁: 1723
結束頁: 1730
Appears in Collections:Conferences Paper