A PCA-Fuzzy Clustering Algorithm for Contours Analysis
Principal component analysis (PCA) is a usefully tool for data compression 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 identify the non-linear relations and to obtain morphological information of the data. The proposed PCA-Fuzzy algorithm is performed on the neighborhood of the cluster 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.
KeywordsPrincipal Component Analysis Fuzzy Cluster Fuzzy Cluster Algorithm Fuzzy Support Vector Machine Robust Principal Component Analysis
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- 2.Daniels, J.D., Ha, L., Ochotta, T., Silva, C.T.: Robust smooth feature extraction from point clouds. In: IEEE International Conference on Shape Modeling and Applications 2007 (SMI 2007), Lyon, France, pp. 123–136 (2007)Google Scholar
- 4.Hubert, M., Engelen, S.: Bioinformatics 20, 1728–1736 (2004)Google Scholar
- 9.Pauly, M., Gross, M., Kobbelt, L.P.: Efficient simplification of point-sampled surfaces. In: VIS 2002: Proceedings of the Conference on Visualization 2002, pp. 163–170. IEEE Computer Society, Boston (2002)Google Scholar
- 12.Sârbu, C., Pop, H.F.: Fuzzy soft-computing methods and their applications in chemistry. In: Lipkowitz, K.B., Boyd, D.B., Cundari, T.R. (eds.) Reviews in Computational Chemistry, ch. 5, pp. 249–332. Wiley–VCH, Weinheim (2004)Google Scholar
- 13.Tong, W.-S., Tang, C.-K., Mordohai, P., Medioni, G.: First order augmentation to tensor voting for boundary inference and multiscale analysis in 3D. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5), 294–611 (2004)Google Scholar
- 14.Weingarten, J., Gruener, G., Siegwart, R.: A fast and robust 3d feature extraction algorithm for structured environment reconstruction. In: Proceedings of 11th International Conference on Advanced Robotics (ICAR), Portugal (2003)Google Scholar