Abstract
The segmentation method proposed in this paper is based on the observation that a single physical reflectance can have many different image values. We call the set of all these values a dominant colour. These variations are caused by shadows, shading and highlights and due to varying object geometry. The main idea is that dominant colours trace connected ridges in the chromatic histogram. To capture them, we propose a new Ridge based Distribution Analysis (RAD) to find the set of ridges representative of the dominant colour. First, a multilocal creaseness technique followed by a ridge extraction algorithm is proposed. Afterwards, a flooding procedure is performed to find the dominant colours in the histogram. Qualitative results illustrate the ability of our method to obtain excellent results in the presence of shadow and highlight edges. Quantitative results obtained on the Berkeley data set show that our method outperforms state-of-the-art segmentation methods at low computational cost.
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References
Skarbek, W., Koschan, A.: Colour image segmentation — a survey. Technical report, Institute for Technical Informatics, Technical University of Berlin (October 1994)
Cheng, H., Jiang, X., Sun, Y., Wang, J.: Color image segmentation:advances and prospects. Pattern Recognition 34(6), 2259–2281 (2001)
Lucchese, L., Mitra, S.: Color image segmentation: A state-of-the-art survey. INSA-A: Proceedings of the Indian National Science Academy, 207–221 (2001)
Agarwal, S., Madasu, S., Hanmandlu, M., Vasikarla, S.: A comparison of some clustering techniques via color segmentation. In: ITCC 2005: Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC 2005), vol. II, pp. 147–153. IEEE Computer Society Press, Washington (2005)
Yang, Y., Wright, J., Sastry, S., Ma, Y.: Unsupervised segmentation of natural images via lossy data compression (2007)
Freixenet, J., Munoz, X., Raba, D., Mart, J., Cuf, X.: Yet another survey on image segmentation: Region and boundary information integration. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 408–422. Springer, Heidelberg (2002)
Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–165 (2004)
Fukunaga, K., Hostetler, L.D.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory 121(1), 32–40 (1975)
Verma, D., Meila, M.: A comparison of spectral clustering algorithms. technical report uw-cse-03-05-01, university of washington
Abd-Almageed, W., Davis, L.: Density Estimation Using Mixtures of Mixtures of Gaussians. In: 9th European Conference on Computer Vision (2006)
Bilmes, J.: A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. International Computer Science Institute 4 (1998)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. In: Proc. Eighth Int’l Conf. Computer Vision, vol. 2, pp. 416–423 (2001)
Shafer, S.A.: Using color to seperate reflection components. COLOR research and application 10(4), 210–218 (1985)
Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.M.: The Amsterdam library of object images. Int. J. Comput. Vision 61(1), 103–112 (2005)
Klinker, G., Shafer, S.: A physical approach to color image understanding. Int. Journal of Computer Vision 4, 7–38 (1990)
López, A.M., Lumbreras, F., Serrat, J., Villanueva, J.J.: Evaluation of methods for ridge and valley detection. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 327–335 (1999)
Wang, L., Pavlidis, T.: Direct gray-scale extraction of features for character recognition. IEEE Trans. Pattern Anal. Mach. Intell. 15(10), 1053–1067 (1993)
Bishnu, A., Bhowmick, P., Dey, S., Bhattacharya, B.B., Kundu, M.K., Murthy, C.A., Acharya, T.: Combinatorial classification of pixels for ridge extraction in a gray-scale fingerprint image. In: ICVGIP (2002)
Vazquez, E., Baldrich, R., Vazquez, J., Vanrell, M.: Topological histogram reduction towards colour segmentation. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4477, pp. 55–62. Springer, Heidelberg (2007)
Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(6), 583–598 (1991)
Gauch, J.M., Pizer, S.M.: Multiresolution analysis of ridges and valleys in grey-scale images. IEEE Trans. Pattern Anal. Mach. Intell. 15(6), 635–646 (1993)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
Christoudias, C., Georgescu, B., Meer, P.: Synergism in low level vision. International Conference on Pattern Recognition 4, 150–155 (2002)
Pantofaru, C., Hebert, M.: A comparison of image segmentation algorithms. Technical Report CMU-RI-TR-05-40, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA (September 2005)
Ge, F., Wang, S., Liu, T.: New benchmark for image segmentation evaluation. Journal of Electronic Imaging 16, 033011 (2007)
Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. Intl. Journal of Computer Vision 59(2) (2004)
Micusık, B., Hanbury, A.: Automatic image segmentation by positioning a seed. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952. Springer, Heidelberg (2006)
Fowlkes, C., Martin, D., Malik, J.: Learning affinity functions for image segmentation: combining patch-based and gradient-based approaches. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, vol. 2 (2003)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
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Vazquez, E., van de Weijer, J., Baldrich, R. (2008). Image Segmentation in the Presence of Shadows and Highlights. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5305. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88693-8_1
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DOI: https://doi.org/10.1007/978-3-540-88693-8_1
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