@inbook {IOPORT.05972790,
    author       = {Wekel, Tilman and Hellwich, Olaf},
    title        = {A robust approach to multi-feature based mesh segmentation using adaptive density estimation.},
    year         = {2011},
    booktitle    = {Computer analysis of images and patterns. 14th international conference, CAIP 2011, Seville, Spain, August 29--31, 2011. Proceedings, Part I},
    isbn         = {978-3-642-23671-6},
    pages        = {244-252},
    publisher    = {Berlin: Springer},
    doi          = {10.1007/978-3-642-23672-3_30},
    abstract     = {Summary: In this paper, a new and robust approach to mesh segmentation is presented. There are various algorithms which deliver satisfying results on clean 3D models. However, many reverse-engineering applications in computer vision such as 3D reconstruction produce extremely noisy or even incomplete data. The presented segmentation algorithm copes with this challenge by a robust semi-global clustering scheme and a cost-function that is based on a probabilistic model. Vision based reconstruction methods are able to generate colored meshes and it is shown, how the vertex color can be used as a supportive feature. A probabilistic framework allows the algorithm to be easily extended by other user defined features. The segmentation scheme is a local iterative optimization embedded in a hierarchical clustering technique. The presented method has been successfully tested on various real world examples.},
    identifier   = {05972790},
}