Real time area-based stereo matching algorithm for multimedia video devices

• Autorzy: Hachaj T. , Ogiela M. R.

Identyfikatory

DOI

10.2478/s11772-013-0107-5

• Języki publikacji: EN

Abstrakty

EN

In this paper we investigate stereovision algorithms that are suitable for multimedia video devices. The main novel contribution of this article is detailed analysis of modern graphical processing unit (GPU)-based dense local stereovision matching algorithm for real time multimedia applications. We considered two GPU-based implementations and one CPU implementation (as the baseline). The results (in terms of frame per second, fps) were measured twenty times per algorithm configuration and, then averaged (the standard deviation was below 5%). The disparity range was [0,20], [0,40], [0,60], [0,80], [0,100] and [0,120]. We also have used three different matching window sizes (3×3, 5×5 and 7×7) and three stereo pair image resolutions 320×240, 640×480 and 1024×768. We developed our algorithm under assumption that it should process data with the same speed as it arrives from captures’ devices. Because most popular of the shelf video cameras (multimedia video devices) capture data with the frequency of 30Hz, this frequency was threshold to consider implementation of our algorithm to be “real time”. We have proved that our GPU algorithm that uses only global memory can be used successfully in that kind of tasks. It is very important because that kind of implementation is more hardware-independent than algorithms that operate on shared memory. Knowing that we might avoid the algorithms failure while moving the multimedia application between machines operating different hardware. From our knowledge this type of research has not been yet reported.

Słowa kluczowe

EN

stereovision; GPU algorithm; local methods; dense methods; CUDA

• Wydawca: Stowarzyszenie Elektryków Polskich ; Polska Akademia Nauk ; Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
• Czasopismo: Opto-Electronics Review
• Rocznik: 2013
• Tom: Vol. 21, No. 4
• Strony: 367--375
• Opis fizyczny: Bibliogr. 33 poz., tab., wykr.

Twórcy

autor

Hachaj T.

• Pedagogical University of Krakow, Institute of Computer Science and Computer Methods, 2 Podchorążych Ave, 30-084 Krakow, Poland

autor

Ogiela M. R.

• AGH University of Science and Technology 30 Mickiewicza Ave, 30-059 Krakow, Poland

Bibliografia

• 1. S. Lefebvre, S. Ambellouis, and F. Cabestaing, “A 1D approach to correlation-based stereo matching”, Image Vision Comput. 29, 580-593 (2011).
• 2. J-J. Orteu, “3-D computer vision in experimental mechanics”, Opt. Laser. Eng. 47, 282-291 (2009).
• 3. D. Garcia, J. J. Orteu, and L. Penazzi, “A combined temporal tracking and stereo-correlation technique for accurate measurement of 3D displacements: application to sheet metal forming”, J. Mater. Process. Tech. 125-126, 736-742 (2002).
• 4. J. Xavier, A. M. P. de Jesus, J. J. L. Morais, and J. M. T. Pinto, “Stereovision measurements on evaluating the modulus of elasticity of wood by compression tests parallel to the grain”, Constr. Build. Mater. 26, 207-215 (2012).
• 5. M. Uematsu, N. Suzuki, A. Hattori, Y. Otake, S. Suzuki, M. Hayashibe, S. Kobayashi, and A. Uchiyama, “A real-time data fusion system updating 3D organ shapes using colour information from multi-directional cameras”, ICS 1268, Proc. Computer Assisted Radiology and Surgery, 741-746 (2004).
• 6. L-M. Su, B. P. Vagvolgyi, R. Agarwal, C. E. Reiley, R. H. Taylor, and G. D. Hager, “Augmented reality during robot-assisted laparoscopic partial nephrectomy: toward real-time 3D-CT to stereoscopic video registration”, Urology 73, 896-900 (2009).
• 7. Q. Yu, H. Araújo, and H. Wang, “A stereovision method for obstacle detection and tracking in non-flat urban environments”, Auton. Robot. 19, 141-157 (2005).
• 8. R. Labayrade, C. Royere, D. Gruyer, and D. Aubert, “Cooperative fusion for multi-obstacles detection with use of stereovision and laser scanner”, Auton. Robot. 19, 117-140 (2005).
• 9. K. Kohara, N. Suganuma, T. Negishi and Takuya Nanri, “Obstacle detection based on occupancy grid maps using stereovision system”, Int. J. Intell. Transport. Syst. Research 8, 85-95 (2009).
• 10. G. Pajares and J. M. de la Cruz, “A probabilistic neural network for attribute selection in stereovision matching”, Neural Comput. Appl., 83-89 (2002).
• 11. H. Halawana, H. Hamdan, and M. Hamdan, “Dense stereovision using mono-CCD color cameras”, Artif. Life Robot. 15, 508-511 (2010).
• 12. A. S. Ogale and Y. Aloimonos, “Shape and the stereo correspondence problem”, Int. J. Comput. Vision 65, 147-162 (2005).
• 13. G. Pajares and J. M. de la Cruz, “Fuzzy cognitive maps for stereovision matching”, Pattern recogn. 39, 2101-2114 (2006).
• 14. G. Pajares, J. M. de la Cruz, and J. A. López-Orozco, “Relaxation labelling in stereo image matching”, Pattern Recogn. 33, 53-68 (2000).
• 15. L. Di Stefano, M. Marchionni, and S. Mattocci, “A fast area-based stereo matching algorithm”, Image Vision Comput. 22, 983-1005 (2004).
• 16. Q. Zhu, Y. Jiang, W. Deng and L. Tang, “Crowdedness estimation approach based on stereovision for bus passengers”, J. Shanghai University 14, 17-23 (2010).
• 17. G. Pajares and J. M. de la Cruz, “Stereovision matching through support vector machines”, Pattern Recogn. Lett. 24, 2575-2583 (2003).
• 18. J. Delon and B. Rougé, “Small baseline stereovision”, J. Math. Imaging Vision 28, 209-223 (2007).
• 19. M. J. P. M. Lemmens, “A survey on stereo matching techniques”, Proc. 16 th ISPRSC. In ASPRS 27/B8, pp.11-23, Kyoto, 1988.
• 20. D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms”, Int. J. Comput. Vision 47 Issue 1-3, 7-42 (2002).
• 21. N. Lazarosa, G. C. Sirakoulisb, and A. Gasteratosa, “Review of stereo vision algorithms: from software to hardware”, Int. J. Optomechatr. 2, 435-462 (2008).
• 22. A. H. J. Koning, K. J. Zuidervelda, and M. A. Viergevera, “Volume visualization on shared memory architectures”, Parallel Comput. 23, 915-925 (1997).
• 23. T. Hachaj and M. R. Ogiela, “Framework for cognitive analysis of dynamic perfusion computed tomography with visualization of large volumetric data”, J. Electron. Imaging. 21, (2012), doi: 10.1117/1.JEI.21.4.043017.
• 24. T. Hachaj and M. R. Ogiela, “Visualization of perfusion abnormalities with GPU-based volume rendering”, Comput. Graphics 36, 163-169 (2012).
• 25. Y. Allusse, P. Horain, A. Agarwal, and C. Saipriyadarshan, “GpuCV: A GPU-accelerated framework for image processing and computer vision”, Lect. Notes Comput. 5359, 430-439 (2008).
• 26. D. Castańo-Díez, D. Moser, A. Schoenegger, S. Pruggnaller, and A. S Frangakis, “Performance evaluation of image processing algorithms on the GPU”, J. Struct. Biol. 164, 153-160 (2008).
• 27. R. Di Salvo and C. Pino, “Image and video processing on CUDA: state of the art and future directions”, MACMESE’11 Proc. 13th WSEAS Int. Conf. on Mathematical and Computational Methods in Science and Engineering, pp. 60-66, 2011.
• 28. W. Lik Dennis Lui, R. Jarvis, Eye-Full Tower, “A GPU-based variable multibaseline omnidirectional stereovision system with automatic baseline selection for outdoor mobile robot navigation”, Robot. Auton. Syst. 58, 747-761 (2010).
• 29. S. Prehn, “GPU stereo vision”, http://www.planetswebdesign.de/fileadmin/pdfs/Prehn%20Sebastian%20-%20GPU%20Stereo%20Vision%20-%202007-12-06.pdf
• 30. R. Fujiki, H. Yoshimoto, D. Arita, and R. Taniguchi, “Real-time model-based hand shape estimation with stereo vision”, Proc. of Korea-Japan Joint Workshop on Frontiers of Computer Vision, pp. 225-230, 2005.
• 31. NVIDIA CUDA Compute Unified Device Architecture, Programming Guide, Version 2.0, http://developer.download.nvidia.com/compute/cuda/2_0/docs/NVIDIA_CUDA_Programming_Guide_2.0.pdf, (2008).
• 32. M. R. Ogiela and S. Bodzioch, “Computer analysis of gallbladder ultrasonic images towards recognition of pathological lesions”, Opto-Electron. Rev. 19, 155-168 (2011).
• 33. S. Bodzioch and M. R. Ogiela, “New approach to gallbladder ultrasonic images analysis and lesions recognition”, Computerized Medical Imaging and Graphics. 33, 154-170 (2009).