Authors
Advisor(s)
Abstract(s)
Principal component analysis (PCA) is a usefully tool for data compres- sion and information extraction. It is often utilized in point cloud processing as it provides an efficient method to approximate local point properties through the examination of the local neighborhoods. This process does sometimes suffer from the assumption that the neighborhood contains only a single surface, when it may contain curved surface or multiple discrete surface entities, as well as relating the properties from PCA to real world attributes. This paper will present a new method that joins the fuzzy clustering algorithm with a local sliding PCA analysis to iden- tify the non-linear relations and to obtain morphological information of the data. The proposed PCA-Fuzzy algorithm is performed on the neighborhood of the clus- ter center and normal approximations in order to estimate a tangent surface and the radius of the curvature that characterizes the trend and curvature of the data points or contour regions.
Description
Keywords
Principal component Analysis Fussy clustering
Citation
Salgado, Paulo; Igrejas, Getúlio (2012). A PCA-fuzzy clustering algorithm for contours analysis. In Eurofuse 2011, Advances in Intelligent and Soft Computing. Peso da Régua. 107, p.305-311
Publisher
Springer Berlin Heidelberg