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Unsupervised TOF Image Segmentation through Spectral Clustering and Region Merging
DOI:
https://doi.org/10.24215/16666038.18.e11Keywords:
Spectral Clustering, TOF images, Unsupervised image segmentationAbstract
Time of Flight (TOF) cameras generate two simultaneous images, one of intensity and one of range. This allows to tackle segmentation problems in which the separate use of intensity or range information is not enough to extract objects of interest from the 3D scene. In turn, range information allows to obtain a normal vector estimation of each point of the captured surfaces. This article presents a semi-supervised spectral clustering method which combines intensity and range information as well as normal vector orientations to improve segmentation results. The main contribution of this article consists in the use of a statistical region merging as a final step of the segmentation method. The region merging process combines adjacent regions which satisfy a similarity criterion. The performance of the proposed method was evaluated over real images. The use of this final step presents preliminary improvements in the metrics evaluated.
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References
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[2] R. Wu, Z. y Leahy, “An optimal graph theoretic approach to data clustering: theory and its application to image segmentation,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 15, pp. 1101–1113, Nov 1993.
[3] L. Lorenti and J. Giacomantone, “Time of flight image segmentation through co-regularized spectral clustering,” in XX Congreso Argentino de Ciencias de la Computaci´on (CACIC 2014), pp. 101–110, 2014.
[4] S. Holzer, R. B. Rusu,M. Dixon, S. Gedikli, and N. Navab, “Adaptive neighborhood selection for real-time surface normal estimation from organized point cloud data using integral images,” in Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, pp. 2684–2689, IEEE, 2012.
[5] D. Holz and S. Behnke, “Fast range image segmentation and smoothing using approximate surface reconstruction and region growing,” Intelligent autonomous systems 12, pp. 61–73, 2013.
[6] M. Cazorla, D. Viejo, and C. Pomares, “Study of the sr 4000 camera,” in X I Workshop de Agentes F´ısicos, pp. 88–97, 2004.
[7] L. Lorenti and J. Giacomantone, “Segmentaci´on espectral de im´agenes utilizando c´amaras de tiempo de vuelo,” in XVIII Congreso Argentino de Ciencias de la Computaci´on, pp. 430–439, 2013.
[8] U. Von Luxburg, “A tutorial on spectral clustering,” Statistics and computing, vol. 17, no. 4, pp. 395–416, 2007.
[9] L. Lorenti, J. Giacomantone, O. N. Bria, and A. E. De Giusti, “Fusi ´on de informaci ´on de geometr´ıa e intensidad para segmentaci ´on de im´agenes tof,” in XXIII Congreso Argentino de Ciencias de la Computaci´on (La Plata, 2017)., pp. 508–517, 2017.
[10] M. A. Hasnat, O. Alata, and A. Tr´emeau, “Joint color-spatial-directional clustering and region merging (jcsd-rm) for unsupervised rgb-d image segmentation,” IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 11, pp. 2255–2268, 2016.
[11] J. Pont-Tuset and F. Marques, “Measures and meta-measures for the supervised evaluation of image segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2131–2138, 2013.
[12] A. Y. Ng, M. I. Jordan, and Y. Weiss, “On spectral clustering: Analysis and an algorithm,” in ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS, pp. 849–856, MIT Press, 2001.
[13] J. Shi and J. Malik, “Normalized cuts and image segmentation,” in Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on, pp. 731–737, Jun 1997.
[14] C. Fowlkes, S. Belongie, F. Chung, and J.Malik, “Spectral grouping using the nystr ¨om method,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, pp. 214–225, Feburary 2004.
[15] A. Kumar, P. Rai, and H. Daume, “Coregularized multi-view spectral clustering,” in Advances in Neural Information Processing Systems 24 (J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger, eds.), pp. 1413–1421, Curran Associates, Inc., 2011.
[16] G. Pagnutti and P. Zanuttigh, “Joint color and depth segmentation based on region merging and surface fitting.,” in VISIGRAPP (4: VISAPP), pp. 93–100, 2016.
[17] M. A. Hasnat, O. Alata, and A. Tr´emeau, “Unsupervised clustering of depth images using watson mixture model,” in Pattern Recognition (ICPR), 2014 22nd International Conference on, pp. 214–219, IEEE, 2014.
[18] B. Peng, L. Zhang, and D. Zhang, “Automatic image segmentation by dynamic region merging,” IEEE Transactions on image processing, vol. 20, no. 12, pp. 3592–3605, 2011.
[19] L. Zelnik-Manor and P. Perona, “Self-tuning spectral clustering,” in Advances in neural information processing systems, pp. 1601–1608, 2005.
[20] D. R. Martin, C. C. Fowlkes, and J. Malik, “Learning to detect natural image boundaries using local brightness, color, and texture cues,” IEEE transactions on pattern analysis and machine intelligence, vol. 26, no. 5, pp. 530–549, 2004.
[21] P. Arbelaez,M.Maire, C. Fowlkes, and J.Malik, “Contour detection and hierarchical image segmentation,” IEEE transactions on pattern analysis and machine intelligence, vol. 33, no. 5, pp. 898–916, 2011.
[22] M. Meila, “Comparing clusterings: an axiomatic view,” in Proceedings of the 22nd international conference on Machine learning, pp. 577–584, ACM, 2005.
[23] S. Sra and D. Karp, “The multivariate watson distribution: Maximum-likelihood estimation and other aspects,” Journal of Multivariate Analysis, vol. 114, pp. 256–269, 2013.
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Published
2018-10-04
How to Cite
Lorenti, L., Giacomantone, J., & Bria, O. (2018). Unsupervised TOF Image Segmentation through Spectral Clustering and Region Merging. Journal of Computer Science and Technology, 18(02), e11. https://doi.org/10.24215/16666038.18.e11
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