Efficient 3D mesh salient region detection using local homogeneity measure

Efficient 3D mesh salient region detection using local homogeneity measure

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Visual saliency is defined by the perceptual information that makes possible to detect specific areas which attract to guide the human visual attention. In this study, the authors present an efficient method for salient regions detection on three-dimensional (3D) meshes using weighted graphs representation. To do so, the authors propose a novel 3D surface descriptor based on a local homogeneity measure. Then, they define the similarity measure between vertices using normal deviation similarities, a two-dimensional projection height map, and the mean curvature. The saliency of a vertex is then evaluated as its degree measure based on the local patch descriptor and a height map. In addition, the authors introduce a custom version of hill climbing algorithm in order to segment the 3D mesh regions according to the saliency degree. Furthermore, they show the robustness of their proposed method through different experimental results. Finally, the authors present the stability and robustness of their method with respect to noise.


    1. 1)
      • 1. Gal, R., Cohen-Or, D.: ‘Salient geometric features for partial shape matching and similarity’, ACM Trans. Graph, 2006, 25, (1), pp. 130150.
    2. 2)
      • 2. Jinho, L., Baback, M., Hanspeter, P., et al: ‘Finding optimal views for 3D face shape modeling’. Proc. Int. Conf. on Automatic Face and Gesture Recognition, Seoul, South Korea, May 2004, pp. 3136.
    3. 3)
      • 3. Shilane, P., Funkhouser, T.: ‘Distinctive regions of 3D surfaces’, ACM Trans. Graph., 2007, 26, (2), p. 7.
    4. 4)
      • 4. Wu, J., Shen, X., Zhu, W., et al: ‘Mesh saliency with global rarity’, Graph. Models, 2013, 75, (5), pp. 255264.
    5. 5)
      • 5. Nouri, A., Charrier, C., Lézoray, O.: ‘Multi-scale mesh saliency with local adaptive patches for view point selection’, Signal Process., Image Commun., 2015, 38, pp. 151166.
    6. 6)
      • 6. Lee, C., Ha, A., Varshney, D., et al: ‘Mesh saliency’, ACM Trans. Graph. (TOG), 2005, 24, (3), pp. 659666.
    7. 7)
      • 7. Lavoué, G.: ‘A local roughness measure for 3D meshes and its application to visual masking’, ACM Trans. Appl. Percept. (TAP), 2009, 5, (4), pp. 121.
    8. 8)
      • 8. Song, R., Liu, Y., Martin, R.R.., et al: ‘Mesh saliency via spectral processing’, ACM Trans. Graph. (TOG), 2014, 33, (1), p. 6.
    9. 9)
      • 9. Tal, A., Shtrom, E., Leifman, G.: ‘Fast-match: fast affine template matching surface regions of interest for view-point selection’. IEEE Conf. on Computer Vision and Pattern Recognition, 2012, pp. 414421.
    10. 10)
      • 10. Tao, P., Cao, J., Li, S., et al: ‘Mesh saliency via ranking unsalient patches in a descriptor space’, Comput. Graph., 2015, 46, (1), pp. 264274.
    11. 11)
      • 11. Zhao, Y., Liu, Y., Song, R., et al: ‘A saliency detection based method for 3d surface simplification’. In: IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Kyoto, Japan, 2012, pp. 889892.
    12. 12)
      • 12. Zhao, Y., Liu, Y.: ‘Patch based saliency detection method for 3D surface simplification’. Proc. of the 21st Int. Conf. on Pattern Recognition, Tsukuba, Japan, November2012, pp. 845848.
    13. 13)
      • 13. Zhao, Y., Liu, Y., Zeng, Z.: ‘Using region-based saliency for 3d interest points detection’. Int. Conf. on Computer Analysis of Images and Patterns, Berlin, Heidelberg, 2013, pp. 108116.
    14. 14)
      • 14. Elad, M.: ‘Retinex by two bilateral filters’. Proc. of the 5th Int. Conf. on Scale Space and PDE Methods in Computer Vision, 2005 April, Vol. 3459, pp. 217229.
    15. 15)
      • 15. Liu, X., Tao, P., Cao, J., et al: ‘Mesh saliency detection via double absorbing Markov chain in feature space’, Visual Compute, 2016, 32, (9), pp. 11211132.
    16. 16)
      • 16. Boissonnat, J.D., Geiger, B.: ‘Three dimensional reconstruction of complex shapes based on the Delaunay triangulation’. Biomedical Image Processing and Biomedical Visualization, PhD dissertation, INRIA, 1993, 1905, pp. 964–975.
    17. 17)
      • 17. Berkmann, J., Caelli, T.: ‘Computation of surface geometry and segmentation using covariance techniques’, IEEE Trans. Pattern. Anal. Mach., 1994, 16, (11), pp. 11141116.
    18. 18)
      • 18. Julie, D., Raphaelle, C., Sebastien, V.: ‘Self-similarity for accurate compression of point-sampled surfaces’, Euro Graph., 2014, 33, (2), pp. 155164.
    19. 19)
      • 19. Lozes, F., Elmoataz, A., Lézoray, O.: ‘Nonlocal processing of 3d colored point clouds’. Pattern Recognition (ICPR), Tsukuba, Japan, 2012 November, pp. 19681971.
    20. 20)
      • 20. El Chakik A., , Elmoataz, A., Desquesnes, X.: ‘Mean curvature flow on graphs for image and manifold restoration and enhancement’, Signal Process., 2014, 105, pp. 449463.
    21. 21)
      • 21. Gilboa, G., Osher, S.: ‘Nonlocal operators with applications to image processing’, Multiscale Model. Simul, 2008, 7, (3), pp. 10051028.
    22. 22)
      • 22. Chen, X., Saparov, A., Pang, B., et al: ‘Schelling points on 3d surface meshes’, ACM Trans. Graph, 2012, 31, (4), p. 29.
    23. 23)
      • 23. Song, R., Liu, Y., Zhao, Y., et al: ‘Conditional random field-based mesh saliency’. 19th IEEE Int. Conf. on Image Processing, 2012 September, pp. 637640.
    24. 24)
      • 24. Korman, S., Reichman, D., Tsur, G., et al: ‘Fast-match: fast affine template matching’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2013, pp. 23312338.
    25. 25)
      • 25. Ohashi, T., Aghbari, Z., Makinouchi, A.: ‘Hill-climbing algorithm for efficient color-based image segmentation’. IASTED Int. Conf. On Signal Processing, Pattern Recognition, and Applications, 2003 June, pp. 1722.

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