УДК: 004.932

Investigating surface-segmentation methods when images are being compared in three-dimensional space

Publication in Journal of Optical Technology

For Russian citation (Opticheskii Zhurnal):

Пономарев С.В. Исследование методов сегментации поверхностей при сопоставлении изображений в трехмерном пространстве // Оптический журнал. 2015. Т. 82. № 8. С. 76–83.

Ponomarev S.V. Investigating surface-segmentation methods when images are being compared in three-dimensional space [in Russian] // Opticheskii Zhurnal. 2015. V. 82. № 8. P. 76–83.

For citation (Journal of Optical Technology):

S. V. Ponomarev, "Investigating surface-segmentation methods when images are being compared in three-dimensional space," Journal of Optical Technology. 82(8), 551-556 (2015). https://doi.org/10.1364/JOT.82.000551

Abstract:

This paper discusses surface-segmentation methods in three-dimensional space in terms of a solution of the problem of structural comparison of images. An algorithm is presented for comparing images in three-dimensional space, and methods are considered for making the developed algorithm more stable against the picture-taking conditions. A quantitative estimate is given for the segmentation accuracy and the response rate of the methods being studied. Based of the results of the investigation, a modification of the image-comparison algorithm is proposed.

Keywords:

structural comparison, segmentation methods, three-dimensional space

Acknowledgements:

This work was carried out with the support of the Ministry of Education and Science of the Russian Federation and with the partial state support of the leading universities of the Russian Federation (Subsidy 074-U01).

OCIS codes: 150.1135

References:

1. V. R. Lutsiv, I. A. Malyshev, and A. S. Potapov, “Hierarchical structural matching algorithms for registration of aerospace images,” Proc. SPIE 5238, 164 (2003).
2. V. S. Andreev, A. F. Gubkin, A. S. Iljashenko, A. B. Kadykov, N. N. Lapina, V. R. Lutsiv, I. A. Malyshev, T. A. Novikova, and A. S. Potapov, “Algorithms for automatically processing and analyzing aerospace pictures,” J. Opt. Technol. 74, 307 (2007) [Opt. Zh. 74, No. 5, 12 (2007)].
3. R. Krishnapuram and S. Gupta, “Morphological methods for detection and classification of edges in range images,” J. Math. Imaging Vis. 2, 351 (1992).
4. F. Arman, B. Sabata, and J. K. Aggarwal, “Segmentation of 3D range images using pyramidal data structures,” CVGIP Image Understanding 57, No. 3, 373 (1993).
5. P. J. Besl and R. C. Jain, “Segmentation through variable order surface fitting,” IEEE Trans. Pattern Anal. Mach. Intell. 10, No. 2, 167 (1988).
6. A. Gupta, A. Leonardis, and R. Bajcsy, “Segmentation of range images as the search for geometric parametric models,” Int. J. Comput. Vis. 14, No. 3, 253 (1995).
7. R. L. Hoffman and A. K. Jain, “Segmentation and classification of range images,” IEEE Trans. Pattern Anal. Mach. Intell. 9, 608 (1987).
8. P. J. Flynn and A. K. Jain, “Surface classification: hypothesis testing and parameter estimation,” in Proceedings of Computer Vision and Pattern Recognition, 1988, pp. 261–267.
9. D. H. Ballard, “Generalized Hough transform to detect arbitrary shapes,” Pattern Recog. 13, No. 2, 111 (1981).
10. D. Holz and S. Behnke, “Fast range image segmentation and smoothing using approximate surface reconstruction and region growing,” Adv. Intell. Syst. Comput. 194, 61 (2013).
11. R. Adams and L. Bischof, “Seeded region growing,” IEEE Trans. Pattern Anal. Mach. Intell. 16, 641 (1994).
12. M. Fischler and R. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24, 381 (1981).
13. M. Y. Yang and W. Foerstner, “Plane detection in point cloud data,” Tech. Rep. (Department of Photogrammetry, University of Bonn, 2010).
14. A. S. Potapov, “Principle of representational minimum description length in image analysis and pattern recognition,” Pattern Recog. Image Anal. 22, No. 1, 82 (2012).