Real-time stereo vision: Making more out of dynamic programming

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5702 LNCS, Issue , 2009, Pages 1096-1103

  Salmen, Jan ^a   Schlipsing, Marc ^a   Edelbrunner, Johann ^a   Hegemann, Stefan ^b   Lüke, Stefan ^b  

^a RUHR UNIVERSITY BOCHUM   (Germany)

^b Division Chassis and Safety ^*  (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATA; DISPARITY MAP; EFFICIENT METHOD; MULTI-PATH; OFFLINE; REAL TIME CONSTRAINTS; REAL TIME STEREO; REAL-WORLD APPLICATION; REFERENCE IMPLEMENTATION; STEREO PROCESSING; STEREO-IMAGE;

DYNAMIC POSITIONING; DYNAMIC PROGRAMMING; EVOLUTIONARY ALGORITHMS; IMAGE ANALYSIS; PERCOLATION (SOLID STATE); SYSTEMS ENGINEERING;

STEREO VISION;

EID: 70349306708     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-03767-2_133     Document Type: Conference Paper

References (15)

1. Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47, 7-42 (2002)
2. Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 195-202 (2003)
3. Ohta, Y., Kanade, T.: Stereo by intra- and inter-scanline search using dynamic programming. IEEE Transactions on Pattern Analysis and Machine Intelligence 7, 139-154 (1985)
4. Geiger, D., Ladendorf, B., Yuille, A.L.: Occlusions and binocular stereo. In: Sandini, G. (ed.) ECCV 1992. LNCS, vol. 588, pp. 425-433. Springer, Heidelberg (1992)
5. Birchfield, S., Tomasi, C.: Depth discontinuities by pixel-to-pixel stereo. International Journal of Computer Vision 35, 1073-1080 (1999)
6. Forstmann, S., Kanou, Y., Ohya, J., Thuering, S., Schmitt, A.: Real-time stereo by using dynamic programming. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop, vol. 3, p. 29 (2004)
7. Gong, M., Yang, Y.H.: Near real-time reliable stereo matching using programmable graphics hardware. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 924-931 (2005)
8. Kim, J.C., Lee, K.M., Choi, B.T., Lee, S.U.: A dense stereo matching using two-pass dynamic programming with generalized ground control points. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1075-1082 (2005)
9. Wang, L., Liao, M., Gong, M., Yang, R., Nister, D.: High-quality real-time stereo using adaptive cost aggregation and dynamic programming. In: Proceedings of the International Symposium on 3D Data Processing, Visualization and Transmission, pp. 798-805 (2006)
10. Veksler, O.: Stereo correspondence by dynamic programming on a tree. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 384-390 (2005)
11. Bobick, A.F., Intille, S.S.: Large occlusion stereo. International Journal of Computer Vision 33(3), 181-200 (1999)
12. Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159-195 (2001)
13. Beyer, H.G.: Evolution strategies. Scholarpedia 2(8), 1965 (2007)
14. Igel, C., Glasmachers, T., Heidrich-Meisner, V.: Shark. Journal of Machine Learning Research 9, 993-996 (2008)
15. Brockers, R., Hund, M., Mertsching, B.: Stereo vision using cost-relaxation with 3D support regions. In: Image and Vision Computing, New Zealand (2005)