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Parallel implementation of the multi-view image segmentation algorithm using the Hough transform
Goshin Ye.V., Kotov A.P.

Samara National Research University, Samara, Russia ,
Image Processing Systems Institute оf RAS – Branch of the FSRC “Crystallography and Photonics” RAS, Samara, Russia

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DOI: 10.18287/2412-6179-2017-41-588-591

Pages: 588-591.

We report on the parallel implementation of a multi-view image segmentation algorithm via segmenting the corresponding three-dimensional scene. The algorithm includes the reconstruction of a three-dimensional scene model in the form of a point cloud, and the segmentation of the resulting point cloud in three-dimensional space using the Hough space. The developed parallel algorithm was implemented on graphics processing units using CUDA technology. Experiments were performed to evaluate the speedup and efficiency of the proposed algorithm. The developed parallel program was tested on modelled scenes.

segmentation; three-dimensional model; Hough transform; CUDA.

Goshin YeV, Kotov AP. Parallel implementation of a multi-view image segmentation algorithm using the Hough transform. Computer Optics 2017; 41(4): 588-591. DOI: 10.18287/2412-6179-2017-41-4-


  1. Pollefeys M, Nistér D, Frahm J-M, Akbarzadeh A, Mordohai P, Clipp B, Engels C, Gallup D, Kim S-J, Merrell P, Salmi C, Sinha S, Talton B, Wang L, Yang Q, Stewénius H, Yang R, Welch G, Towles H. Detailed real-time urban 3D reconstruction from video. International Journal of Computer Vision 2008; 78(2-3): 143-167. DOI: 10.1007/s11263-007-0086-4.
  2. Baillard C, Maître H. 3-D reconstruction of urban scenes from aerial stereo imagery: A focusing strategy. Computer Vision and Image Understanding 1999; 76(3): 244-258. DOI: 10.1006/cviu.1999.0793.
  3. Pollefeys M, Koch R, Van Gool L. Self-calibration and metric reconstruction in spite of varying and unknown intrinsic camera parameters. International Journal of Computer Vision 1999; 32(1): 7-25. DOI: 10.1023/A:1008109111715.
  4. Eisert P, Steinbach E, Girod B. Automatic reconstruction of stationary 3-D objects from multiple uncalibrated camera views. IEEE Transactions on Circuits and Systems for Video Technology 2000; 10(2): 261-277. DOI: 10.1109/76.825726.
  5. Reitberger J, Schnörr C, Krzystek P, Stilla U. 3D segmentation of single trees exploiting full waveform LIDAR data. ISPRS Journal of Photogrammetry and Remote Sensing 2009; 64(6): 561-574. DOI: 10.1016/j.isprsjprs.2009.04.002.
  6. Tarsha-Kurdi F, Landes T, Grussenmeyer P. Hough-transform and extended RANSAC algorithms for automatic detection of 3D building roof planes from lidar data. Proceedings of the ISPRS Workshop on Laser Scanning 2007; 36(3): 407-412.
  7. Zhang J, Lin X, Ning X. SVM-based classification of segmented airborne LiDAR point clouds in urban areas. Remote Sensing 2013; 5(8): 3749-3775. DOI: 10.3390/rs5083749.
  8. Borrmann D, Elseberg J, Lingemann K, Nüchter A. The 3D Hough Transform for plane detection in point clouds: A review and a new accumulator design. 3D Research 2011; 2(2): 02003. DOI: 10.1007/3DRes.02(2011)3.
  9. Goshin YeV, Loshkareva GE. Segmentation of stereo images with the use of the 3D Hough transform. CEUR Workshop Proceedings 2016; 1638: 340-347. DOI: 10.18287/1613-0073-2016-1638-340-347.
  10. Goshin, YeV, Fursov VA. 3D scene reconstruction from stereo images with unknown extrinsic parameters. Computer Optics 2015; 39(5): 770-776. DOI: 10.18287/0134-2452-2015-39-5-770-776.
  11. Lucas BD, Kanade T. An iterative image registration technique with an application to stereo vision. IJCAI 1981; 81: 674-679.
  12. Hartley RI, Sturm P. Triangulation. Computer Vision and Image Understanding 1997; 68(2): 146-157.
  13. Fursov VA, Bibikov SA, Yakimov PYu. Localization of objects contours with different scales in images using Hough transform. Computer Optics 2013, 37(4): 496-502.
  14. Van Den Braak G-J, Nugteren C, Mesman B, Corporaal H. GPU-vote: A framework for accelerating voting algorithms on GPU. Euro-Par 2012 Parallel Processing 2012; 945-956. DOI: 10.1007/978-3-642-32820-6_92.
  15. NVIDIA Corporation. NVIDIA CUDA C Programming Guide: Version 8.0; January 2017. Source: <http://docs.nvidia.com/cuda/pdf/CUDA_C_Programming_Guide.pdf>.

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