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ABSTRACT

Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods. Our taxonomy is designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms. We have also produced several new multi-frame stereo data sets with ground truth and are making both the code and data sets available on the Web. Finally, we include a comparative evaluation of a large set of today's best-performing stereo algorithms.

REFERENCES

• 1: Anandan, P. 1989. A computational framework and an algorithm for the measurement of visual motion. IJCV, 2(3):283-310.
• 2: Arnold, R.D. 1983. Automated stereo perception. Technical Report AIM-351, Artificial Intelligence Laboratory, Stanford University.
• 3: Baker, H.H. 1980. Edge based stereo correlation, in Image Understanding Workshop, L.S. Baumann (Ed.). Science Applications International Corporation, pp. 168-175.
• 4: Baker, H. and Binford, T. 1981. Depth from edge and intensity based stereo. In IJCAI, pp. 631-636.
• 5: S. Baker , R. Szeliski , P. Anandan, A Layered Approach to Stereo Reconstruction, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p.434, June 23-25, 1998
• 6: Barnard, S.T. 1989. Stochastic stereo matching over scale. IJCV, 3(1):17-32.
• 7: Stephen T. Barnard , Martin A. Fischler, Computational Stereo, ACM Computing Surveys (CSUR), v.14 n.4, p.553-572, Dec. 1982 [doi>10.1145/356893.356896]
• 8: J. L. Barron , D. J. Fleet , S. S. Beauchemin, Performance of optical flow techniques, International Journal of Computer Vision, v.12 n.1, p.43-77, Feb. 1994 [doi>10.1007/BF01420984]
• 9: Peter N. Belhumeur, A Bayesian approach to binocular stereopsis, International Journal of Computer Vision, v.19 n.3, p.237-260, Aug. 1996 [doi>10.1007/BF00055146]
• 10: Belhumeur, P.N. and Mumford, D. 1992. A Bayesian treatment of the stereo correspondence problem using half-occuluded regions. In CVPR, pp. 506-512.
• 11: James R. Bergen , P. Anandan , Keith J. Hanna , Rajesh Hingorani, Hierarchical Model-Based Motion Estimation, Proceedings of the Second European Conference on Computer Vision, p.237-252, May 19-22, 1992
• 12: Stan Birchfield , Carlo Tomasi, A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.20 n.4, p.401-406, April 1998 [doi>10.1109/34.677269]
• 13: Stan Birchfield , Carlo Tomasi, Depth Discontinuities by Pixel-to-Pixel Stereo, Proceedings of the Sixth International Conference on Computer Vision, p.1073, January 04-07, 1998
• 14: Birchfield, S. and Tomasi, C. 1999. Multiway cut for stereo and motion with slanted surfaces. In ICCV, pp. 489-495.
• 15: Black, M.J. and Anandan, p. 1993. A framework for the robust estimation of optical flow. In ICCV, pp. 231-236.
• 16: Michael J. Black , Anand Rangarajan, On the unification of line processes, outlier rejection, and robust statistics with applications in early vision, International Journal of Computer Vision, v.19 n.1, p.57-91, July 1996 [doi>10.1007/BF00131148]
• 17: Andrew Blake , Andrew Zisserman, Visual reconstruction, MIT Press, Cambridge, MA, 1987
• 18: Aaron F. Bobick , Stephen S. Intille, Large Occlusion Stereo, International Journal of Computer Vision, v.33 n.3, p.181-200, Sept. 1999 [doi>10.1023/A:1008150329890]
• 19: Bolles, R.C., Baker, H.H., and Hannah, M.J. 1993. The JISCT stereo evaluation. In DARPA Image Understanding Workshop, pp.263- 274.
• 20: Bolles, R.C., Baker, H.H., and Marimont, D.H. 1987. Epipolar-plane image analysis: An approach to determining structure from motion. IJCV, 1:7-55.
• 21: Yuri Boykov , Olga Veksler , Ramin Zabih, A New Algorithm for Energy Minimization with Discontinuities, Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, p.205-220, July 26-29, 1999
• 22: Yuri Boykov , Olga Veksler , Ramin Zabith, A Variable Window Approach to Early Vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.20 n.12, p.1283-1294, December 1998 [doi>10.1109/34.735802]
• 23: Yuri Boykov , Olga Veksler , Ramin Zabih, Fast Approximate Energy Minimization via Graph Cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.23 n.11, p.1222-1239, November 2001 [doi>10.1109/34.969114]
• 24: Broadhurst, A., Drummond, T., and Cipolla, R. 2001. A probabilistic framework for space carving. In ICCV, Vol. I, pp. 388-393.
• 25: Lisa Gottesfeld Brown, A survey of image registration techniques, ACM Computing Surveys (CSUR), v.24 n.4, p.325-376, Dec. 1992 [doi>10.1145/146370.146374]
• 26: Burt, P.J. and Adelson, E.H. 1983. The Laplacian pyramid as a compact image code. IEEE Transactions on Communications, COM- 31(4):532-540.
• 27: J Canny, A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.8 n.6, p.679-698, Nov. 1986 [doi>10.1109/TPAMI.1986.4767851]
• 28: Paul B. Chou , Christopher M. Brown, The theory and practice of Bayesian image labeling, International Journal of Computer Vision, v.4 n.3, p.185-210, Jun. 1990 [doi>10.1007/BF00054995]
• 29: Steven D. Cochran , Gérard Medioni, 3-D Surface Description from Binocular Stereo, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.14 n.10, p.981-994, October 1992 [doi>10.1109/34.159902]
• 30: Robert T. Collins, A Space-Sweep Approach to True Multi-Image Matching, Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96), p.358, June 18-20, 1996
• 31: Ingemar J. Cox , Sunita L. Hingorani , Satish B. Rao , Bruce M. Maggs, A maximum likelihood stereo algorithm, Computer Vision and Image Understanding, v.63 n.3, p.542-567, May 1996 [doi>10.1006/cviu.1996.0040]
• 32: I. J. Cox , S. Roy , S. L. Hingorani, Dynamic histogram warping of image pairs for constant image brightness, Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2, p.2366, October 23-26, 1995
• 33: W. Bruce Culbertson , Thomas Malzbender , Gregory G. Slabaugh, Generalized Voxel Coloring, Proceedings of the International Workshop on Vision Algorithms: Theory and Practice, p.100-115, September 21-22, 1999
• 34: De Bonet, J.S. and Viola, P. 1999. Poxels: Probabilistic voxelized volume reconstruction. In ICCV, pp. 418-425.
• 35: R. Deriche, Fast Algorithms for Low-Level Vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.12 n.1, p.78-87, January 1990 [doi>10.1109/34.41386]
• 36: Dev, P. 1974. Segmentation processes in visual perception: A co-operative neural model. University of Massachusetts at Amherst, COINS Technical Report 74C-5.
• 37: Dhond, U.R. and Aggarwal, J.K. 1989. Structure from stereo--a review. IEEE Trans. on Systems, Man, and Cybern., 19(6):1489- 1510.
• 38: Faugeras, O. and Keriven, R. 1998. Variational principles, surface evolution, PDE's, level set methods, and the stereo problem. IEEE Trans. Image Proc., 7(3):336-344.
• 39: Olivier Faugeras , Quang-Tuan Luong , T. Papadopoulou, The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications, MIT Press, Cambridge, MA, 2001
• 40: David J. Fleet , Allan D. Jepson , Michael R. M. Jenkin, Phase-based disparity measurement, CVGIP: Image Understanding, v.53 n.2, p.198-210, March 1991 [doi>10.1016/1049-9660(91)90027-M]
• 41: T. Frö , hlinghaus , J. M. Buhmann, Regularizing Phase-Based Stereo, Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I, p.451, August 25-30, 1996
• 42: Fua, P. 1993. A parallel stereo algorithm that produces dense depth maps and preserves image features. Machine Vision and Applications , 6:35-49.
• 43: P. Fua , Y. G. Leclerc, Object-centered surface reconstruction: combining multi-image stereo and shading, International Journal of Computer Vision, v.16 n.1, p.35-55, Sept. 1995 [doi>10.1007/BF01428192]
• 44: Ed Gamble , Tomaso Poggio, Visual Integration and Detection of Discontinuities: The Key Role of Intensity Edges, Massachusetts Institute of Technology, Cambridge, MA, 1987
• 45: Davi Geiger , Federico Girosi, Parallel and Deterministic Algorithms from MRFs: Surface Reconstruction, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.13 n.5, p.401-412, May 1991 [doi>10.1109/34.134040]
• 46: Davi Geiger , Bruce Ladendorf , Alan L. Yuille, Occlusions and Binocular Stereo, Proceedings of the Second European Conference on Computer Vision, p.425-433, May 19-22, 1992
• 47: Geman, S. and Geman, D. 1984. Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE TPAMI, 6(6):721-741.
• 48: Gennert, M.A. 1988. Brightness-based stereo matching. In ICCV, pp. 139-143.
• 49: Minglun Gong , Yee-Hong Yang, Genetic-Based Stereo Algorithm and Disparity Map Evaluation, International Journal of Computer Vision, v.47 n.1-3, p.63-77, April-June 2002 [doi>10.1023/A:1014529404956]
• 50: Grimson, W.E.L. 1985. Computational experiments with a feature based stereo algorithm. IEEE TPAMI, 7(1): 17-34.
• 51: Marsha Jo Hannah, Computer matching of areas in stereo images., 1974
• 52: Richard Hartley , Andrew Zisserman, Multiple view geometry in computer vision, Cambridge University Press, New York, NY, 2000
• 53: Heiko Hirschmüller , Peter R. Innocent , Jon Garibaldi, Real-Time Correlation-Based Stereo Vision with Reduced Border Errors, International Journal of Computer Vision, v.47 n.1-3, p.229-246, April-June 2002 [doi>10.1023/A:1014554110407]
• 54: Yuan C. Hsieh , David M. McKeown, Jr. , Frederic P. Perlant, Performance Evaluation of Scene Registration and Stereo Matching for Artographic Feature Extraction, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.14 n.2, p.214-238, February 1992 [doi>10.1109/34.121790]
• 55: Hiroshi Ishikawa , Davi Geiger, Occlusions, Discontinuities, and Epipolar Lines in Stereo, Proceedings of the 5th European Conference on Computer Vision-Volume I, p.232-248, June 02-06, 1998
• 56: Michael R. M. Jenkin , Allen D. Jepson , John K. Tsotsos, Techniques for disparity measurement, CVGIP: Image Understanding, v.53 n.1, p.14-30, Jan. 1991 [doi>10.1016/1049-9660(91)90002-7]
• 57: David G. Jones , Jitendra Malik, A Computational Framework for Determining Stereo Correspondence from a Set of Linear Spatial Filters, Proceedings of the Second European Conference on Computer Vision, p.395-410, May 19-22, 1992
• 58: Kanade, T. 1994. Development of a video-rate stereo machine. In Image Understanding Workshop, Monterey, CA, 1994. Morgan Kaufmann Publishers: San Mateo, CA, pp. 549-557.
• 59: T. Kanade , M. Okutomi, A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.16 n.9, p.920-932, September 1994 [doi>10.1109/34.310690]
• 60: Takeo Kanade , Atsushi Yoshida , Kazuo Oda , Hiroshi Kano , Masaya Tanaka, A Stereo Machine for Video-Rate Dense Depth Mapping and Its New Applications, Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96), p.196, June 18-20, 1996
• 61: Kang, S.B., Szeliski, R., and Chai, J. 2001. Handling occlusions in dense multi-view stereo. In CVPR, pp. 103-110.
• 62: Sing Bing Kang , J. A. Webb , C. L. Zitnick , T. Kanade, A multibaseline stereo system with active illumination and real-time image acquisition, Proceedings of the Fifth International Conference on Computer Vision, p.88, June 20-23, 1995
• 63: Kass, M. 1988. Linear image features in stereopsis. IJCV, 1 (4):357- 368.
• 64: Kimura, R. et al. 1999. A convolver-based real-time stereo machine (SAZAN). In CVPR, Vol. 1, pp. 457-463.
• 65: Kolmogorov, V. and Zabih, R. 2001. Computing visual correspondence with occlusions using graph cuts. In ICCV, Vol. II, pp. 508- 515.
• 66: Kiriakos N. Kutulakos, Approximate N-View Stereo, Proceedings of the 6th European Conference on Computer Vision-Part I, p.67-83, June 26-July 01, 2000
• 67: Kiriakos N. Kutulakos , Steven M. Seitz, A Theory of Shape by Space Carving, International Journal of Computer Vision, v.38 n.3, p.199-218, July-August 2000 [doi>10.1023/A:1008191222954]
• 68: Sang Hwa Lee , Yasuaki Kanatsugu , Jong-Il Park, MAP-Based Stochastic Diffusion for Stereo Matching and Line Fields Estimation, International Journal of Computer Vision, v.47 n.1-3, p.195-218, April-June 2002 [doi>10.1023/A:1014550009499]
• 69: Lin, M. and Tomasi, C. Surfaces with occlusions from layered stereo. Technical report, Stanford University. In preparation.
• 70: Loop, C. and Zhang, Z. 1999. Computing rectifying homographies for stereo vision. In CVPR, Vol. I, pp. 125-131.
• 71: Lucas, B.D. and Kanade, T. 1981. An iterative image registration technique with an application in stereo vision. In IJCAI, pp. 674- 679.
• 72: Marr, D. 1982. Vision. Freeman: New York.
• 73: Marr, D. and Poggio, T. 1976. Cooperative computation of stereo disparity. Science, 194:283-287.
• 74: Marr, D.C. and Poggio, T. 1979. A computational theory of human stereo vision. Proceedings of the Royal Society of London, B 204:301-328.
• 75: Marroquin, J.L. 1983. Design of cooperative networks. AI Lab, MIT, Working Paper 253.
• 76: Marroquin, J., Mitter, S., and Poggio, T. 1987. Probabilistic solution of ill-posed problems in computational vision. Journal of the American Statistical Association, 82(397):76-89.
• 77: Matthies, L., Szeliski, R., and Kanade, T. 1989. Kalman filter-based algorithms for estimating depth from image sequences. IJCV, 3:209-236.
• 78: Amar Mitiche , Patrick Bouthemy, Computation and analysis of image motion: a synopsis of current problems and methods, International Journal of Computer Vision, v.19 n.1, p.29-55, July 1996 [doi>10.1007/BF00131147]
• 79: Karsten Mühlmann , Dennis Maier , Jürgen Hesser , Reinhard Männer, Calculating Dense Disparity Maps from Color Stereo Images, an Efficient Implementation, International Journal of Computer Vision, v.47 n.1-3, p.79-88, April-June 2002 [doi>10.1023/A:1014581421794]
• 80: Mulligan, J., Isler, V., and Danulidis, K. 2001. Performance evaluation of stereo for tele-presence. In ICCV, Vol. II, pp. 558-565.
• 81: Yuichi Nakamura , Tomohiko Matsuura , Kiyohide Satoh , Yuichi Ohta, Occlusion Detectable Stereo -- Occlusion Patterns in Camera Matrix, Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96), p.371, June 18-20, 1996
• 82: Nishihara, H.K. 1984. Practical real-time imaging stereo matcher. Optical Engineering, 23(5):536-545.
• 83: Ohta, Y. aud Kanade, T. 1985. Stereo by intra- and interscanline search using dynamic programming. IEEE TPAMI, 7(2):139- 154.
• 84: Masatoshi Okutomi , Takeo Kanade, A locally adaptive window for signal matching, International Journal of Computer Vision, v.7 n.2, p.143-162, Jan. 1992 [doi>10.1007/BF00128133]
• 85: M. Okutomi , T. Kanade, A Multiple-Baseline Stereo, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.15 n.4, p.353-363, April 1993 [doi>10.1109/34.206955]
• 86: M. Otte , H.-H. Nagel, Optical flow estimation: advances and comparisons, Proceedings of the third European conference on Computer vision (vol. 1), p.51-60, June 1994, Stocklholm, Sweden
• 87: Tomaso Poggio , Vincent Torre , Christof Koch, Computational vision and regularization theory, Nature, v.317 n.26, p.314-319, Sept. 1985 [doi>10.1038/317314a0]
• 88: Pollard, S.B., Mayhew, J.E.W., and Frisby, J.P. 1985. PMF: A stereo correspondence algorithm using a disparity gradient limit. Perception , 14:449-470.
• 89: Prazdny, K. 1985. Detection of binocular disparities. Biological Cybernetics . 52(2):93-99.
• 90: Quam, L.H. 1984. Hierarchical warp stereo. In Image Understanding Workshop, New Orleans, Louisiana, 1984. Science Applications International Corporation, pp. 149-155.
• 91: Sébastien Roy, Stereo Without Epipolar Lines: A Maximum-Flow Formulation, International Journal of Computer Vision, v.34 n.2-3, p.147-161, Nov. 1999 [doi>10.1023/A:1008192004934]
• 92: Sébastien Roy , Ingemar J. Cox, A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem, Proceedings of the Sixth International Conference on Computer Vision, p.492, January 04-07, 1998
• 93: Ryan, T.W., Gray, R.T., and Hunt, B.R. 1980. Prediction of correlation errors in stereo-pair images. Optical Engineering, 19(3):312- 322.
• 94: Saito, H. and Kanade, T. 1999. Shape reconstruction in projective grid space from large number of images. In CVPR, Vol. 2, pp. 49-54.
• 95: Scharstein, D. 1994. Matching images by comparing their gradient fields. In ICPR, Vol. 1, pp. 572-575.
• 96: Daniel Scharstein , J. Van Leeuwen , J. Hartmanis , G. Goos, View Synthesis Using Stereo Vision, Springer-Verlag New York, Inc., Secaucus, NJ, 1999
• 97: Daniel Scharstein , Richard Szeliski, Stereo Matching with Nonlinear Diffusion, International Journal of Computer Vision, v.28 n.2, p.155-174, June 1998 [doi>10.1023/A:1008015117424]
• 98: Scharstein, D. and Szeliski, R. 2001. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Microsoft Research, Technical Report MSR-TR-2001-81.
• 99: D. Scharstein , R. Szeliski , R. Zabih, A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms, Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV'01), p.131, December 09-10, 2001
• 100: Seitz, P. 1989. Using local orientation information as image primitive for robust object recognition. In SPIE Visual Communications and Image Processing IV, Vol. 1199, pp. 1630-1639.
• 101: Steven M. Seitz , Charles R. Dyer, Photorealistic Scene Reconstruction by Voxel Coloring, International Journal of Computer Vision, v.35 n.2, p.151-173, Nov.-Dec. 1999 [doi>10.1023/A:1008176507526]
• 102: Jonathan Shade , Steven Gortler , Li-wei He , Richard Szeliski, Layered depth images, Proceedings of the 25th annual conference on Computer graphics and interactive techniques, p.231-242, July 1998 [doi>10.1145/280814.280882]
• 103: Shah, J. 1993. A nonlinear diffusion model for discontinuous disparity and half-occlusion in stereo. In CVPR, pp. 34-40.
• 104: Juliang Shao, Generation of Temporally Consistent Multiple Virtual Camera Views from Stereoscopic Image Sequences, International Journal of Computer Vision, v.47 n.1-3, p.171-180, April-June 2002 [doi>10.1023/A:1014545908590]
• 105: Shimizu, M. and Okutomi, M. 2001. Precise sub-pixel estimation on area-based matching. In ICCV, Vol. I, pp. 90-97.
• 106: Shum, H.-Y. and Szeliski, R. 1999. Stereo reconstruction from multiperspective panoramas. In ICCV, pp. 14-21.
• 107: Simoncelli, E.P., Adelson, E.H., and Heeger, D.J. 1991. Probability distributions of optic flow. In CVPR, pp. 310-315.
• 108: Changming Sun, Fast Stereo Matching Using Rectangular Subregioning and 3D Maximum-Surface Techniques, International Journal of Computer Vision, v.47 n.1-3, p.99-117, April-June 2002 [doi>10.1023/A:1014585622703]
• 109: Jian Sun , Heung-Yeung Shum , Nan-Ning Zheng, Stereo Matching Using Belief Propagation, Proceedings of the 7th European Conference on Computer Vision-Part II, p.510-524, May 28-31, 2002
• 110: Richard Szeliski, Bayesian Modeling of Uncertainty in Low-Level Vision, Kluwer Academic Publishers, Norwell, MA, 1989
• 111: Richard Szeliski, Prediction Error as a Quality Metric for Motion and Stereo, Proceedings of the International Conference on Computer Vision-Volume 2, p.781, September 20-25, 1999
• 112: Richard Szeliski , James Coughlan, Spline-Based Image Registration, International Journal of Computer Vision, v.22 n.3, p.199-218, March/April 1997 [doi>10.1023/A:1007996332012]
• 113: Richard Szeliski , Polina Golland, Stereo Matching with Transparency and Matting, International Journal of Computer Vision, v.32 n.1, p.45-61, Aug. 1. 1999 [doi>10.1023/A:1008192912624]
• 114: Szeliski, R. and Hinton, G. 1985. Solving random-dot stereograms using the heat equation. In CVPR, pp. 284-288.
• 115: Richard Szeliski , Daniel Scharstein, Symmetric Sub-Pixel Stereo Matching, Proceedings of the 7th European Conference on Computer Vision-Part II, p.525-540, May 28-31, 2002
• 116: Richard Szeliski , Ramin Zabih, An Experimental Comparison of Stereo Algorithms, Proceedings of the International Workshop on Vision Algorithms: Theory and Practice, p.1-19, September 21-22, 1999
• 117: Tao, H., Sawhney, H., and Kumar, R. 2001. A global matching framework for stereo computation. In ICCV, Vol. 1, pp. 532-539.
• 118: A. Murat Tekalp, Digital video processing, Prentice-Hall, Inc., Upper Saddle River, NJ, 1995
• 119: Demetri Terzopoulos, Regularization of inverse visual problems involving discontinuities, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.8 n.4, p.413-242, July 1986 [doi>10.1109/TPAMI.1986.4767807]
• 120: Trezopoulos, D. and Fleischer, K. 1988. Deformable models. The Visual Computer, 4(6):306-331.
• 121: Demetri Terzopoulos , Dimitri Metaxas, Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.13 n.7, p.703-714, July 1991 [doi>10.1109/34.85659]
• 122: Qi Tian , Michael N Huhns, Algorithms for subpixel registration, Computer Vision, Graphics, and Image Processing, v.35 n.2, p.220-233, Aug. 1986 [doi>10.1016/0734-189X(86)90028-9]
• 123: Olga Veksler , Ramin Zabih, Efficient graph-based energy minimization methods in computer vision, 1999
• 124: Veksler, O. 2001. Stereo matching by compact windows via minimum ratio cycle. In ICCV, Vol. I, pp. 540-547.
• 125: Wang, J.Y.A. and Adelson, E.H. 1993. Layered representation for motion analysis. In CVPR, pp. 361-366.
• 126: Witkin, A., Terzopoulos, D., and Kass, M. 1987. Signal matching through scale space. IJCV, 1:133-144.
• 127: Yang, Y., Yuille, A., and Lu, J. 1993. Local, global, and multilevel stereo matching. In CVPR, pp. 274-279.
• 128: Yuille, A.L. and Poggio, T. 1984. A generalized ordering constraint for stereo correspondence. AI Lab, MIT, A.I. Memo 777.
• 129: Ramin Zabih , John Woodfill, Non-parametric local transforms for computing visual correspondence, Proceedings of the third European conference on Computer Vision (Vol. II), p.151-158, May 1994, Stockholm, Sweden
• 130: Zhengyou Zhang, Determining the Epipolar Geometry and its Uncertainty: A Review, International Journal of Computer Vision, v.27 n.2, p.161-195, Mar 1998 [doi>10.1023/A:1007941100561]
• 131: Zhengyou Zhang, A Flexible New Technique for Camera Calibration, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.22 n.11, p.1330-1334, November 2000 [doi>10.1109/34.888718]
• 132: C. Lawrence Zitnick , Takeo Kanade, A Cooperative Algorithm for Stereo Matching and Occlusion Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.22 n.7, p.675-684, July 2000 [doi>10.1109/34.865184]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.10 Vision and Scene Understanding
            Subjects: 3D/stereo scene analysis

Additional Classification:
  F. Theory of Computation
  F.2 ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY
      F.2.2 Nonnumerical Algorithms and Problems
          Subjects: Computations on discrete structures

  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.8 Scene Analysis
          Subjects: Stereo

General Terms:
Algorithms, Design, Performance

Keywords:
evaluation of performance, stereo correspondence software, stereo matching survey