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3D Mesh Segmentation Based on Unsupervised Clustering

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016 (AISI 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 533))

Abstract

3D mesh segmentation is considered an important process in the field of computer graphics. It is a fundamental process in different applications such as shape reconstruction in reverse engineering, 3D models retrieval, and CAD/CAM applications, etc. It consists of subdividing a polygonal surface into patches of uniform properties either from a geometrical point of view or from a perceptual/semantic point of view. In this paper, unsupervised clustering techniques for the 3D mesh segmentation problem are introduced. The K-means and the Fuzzy C-means (FCM) clustering techniques are selected for the development of the proposed clustering-based 3D mesh segmentation techniques. Since the mesh faces are considered the main element, the clustering technique is applied to the dual mesh. The 3D Euclidean distance is used as the distance measure to compute matching between mesh elements. Based on empirical results on a benchmark dataset of 3D mesh models, the FCM-based mesh segmentation technique outperforms the K-means-based one in terms of accuracy and consistency with human segmentations.

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Author information

Authors and Affiliations

  1. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt

    Dina Khattab, Hala M. Ebeid & Mohamed F. Tolba

  2. Faculty of Computer Studies, Arab Open University, Headquarters, 13033, Kuwait City, Kuwait

    Ashraf S. Hussein

Authors
  1. Dina Khattab
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  2. Hala M. Ebeid
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  3. Ashraf S. Hussein
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  4. Mohamed F. Tolba
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Corresponding author

Correspondence to Dina Khattab .

Editor information

Editors and Affiliations

  1. Faculty of Computers & Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University, Dubai, United Arab Emirates

    Khaled Shaalan

  3. CS Dept. Faculty of Computers and Inform, Suez Canal University CS Dept. Faculty of Computers and Inform, Ismailia, Egypt

    Tarek Gaber

  4. Ahmed Orabi Square , Menouf, Egypt

    Ahmad Taher Azar

  5. Faculty of Computer & Information Scienc, Ain Shams University Faculty of Computer & Information Scienc, Cairo, Egypt

    M. F. Tolba

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Khattab, D., Ebeid, H.M., Hussein, A.S., Tolba, M.F. (2017). 3D Mesh Segmentation Based on Unsupervised Clustering. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_57

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  • DOI: https://doi.org/10.1007/978-3-319-48308-5_57

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48307-8

  • Online ISBN: 978-3-319-48308-5

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