Two-Frame Stereo Matching Algorithms—A Review

REFERENCES

• ST Barnard & M A Fischler, Computational stereo, ACM Computing Surveys, vol 14, no 4, pp 553–572, 1982.
   Google Scholar
• X J Jiang & H Bunke, Line segment based axial motion stereo. Pattern Recognition, vol 28, no 4, pp 553–562, 1995.
   Google Scholar
• R Dhond & J K Aggarwal, Structure from stereo-A review, IEEE Transactions on Systems, Man and Cybernetics, vol 19, no 6, pp 1499–1510. 1989.
   Google Scholar
• http://www.middlebury.edu/stereo/taxonomy-IJCV.pdf
   Google Scholar
• P Aschwanden & W Guggenbuehl, Experimental results from a comparative study on correlation-type registration algorithms, Robust Computer Vision, Foerstner and Ruwiedel eds., pp. 268–289, Wichmann, 1993.
   Google Scholar
• ST Barnard. Stereo matching, Academic Press, New York, NY, 1993, pp 245–271. (Chapter 10).
   Google Scholar
• M Svedlow, C D McGillem & P A Anuta, Image registration: Similarity measure and preprocessing method comparisons, IEEE Transactions on Aerospace and Electronics systems, vol 14, no 1, pp 141–149, 1978.
   Google Scholar
• M Okutomi & T Kanade, A locally adaptive window for signal matching, International Journal of Computer vision, vol 17, no 2, pp 143–162, 1992.
   Google Scholar
• L G Brown, A survey of image registration techniques, Computing surveys, vol 24, no 4, pp 325–376, 1992.
   Google Scholar
• T Kanade, Development of a video-rate stereo machine, Image understanding Workshop, pp 549–557, Monterey, CA, 1994, Morgan Kaufmann Publishers.
   Google Scholar
• M D Levine, D A O Handley & G M Yagi, Computer determination of depth maps, Computer Graphics and Image Processing, vol 2, no 4, pp 131–150, 1973.
   Google Scholar
• T Kanade & M Okutomi, A Stereo Matching Algorithm with an Adaptive window: Theory and Experiment, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 16, no 9, pp 920–932, 1994.
   Google Scholar
• Y Boykov, O Veksler & R Zabih, A variable window approach to early vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 20, no 12, pp 1283–1294, 1998.
   Google Scholar
• D N Bhat & S K Nayar, Ordinal measures for image correspondence, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol 20, no 4, pp 415–423, 1998.
   Google Scholar
• N Sebe, M S Lew & D P Huijsmans, Toward Improved Ranking metrics. Transactions on Pattern Analysis and Machine Intelligence, vol 22, no 10, pp 1132–1143, 2000.
   Google Scholar
• A Fusiello & V Roberto, Symmetric stereo with multiple windowing, International Journal of Pattern Recognition and Artificial Intelligence, vol 14, no 8, pp 1053–1066, 2000.
   Google Scholar
• YS Kim, J J Lee & Y H Ha, Stereo matching algorithm based on modified wavelet decomposition process. Pattern Recognition, vol 30, no 6, pp 929–952, 1997.
   Google Scholar
• F Shi. N R Hughes & G N Roberts, SSD Matching using shift invariant wavelet transform, British Machine vision Conference, Manchester, pp 113–122, 2001.
   Google Scholar
• L Tang, C Wu & Z Chen, Image dense matching based on region growth with adaptive window, Pattern Recognition Letters, vol 23, pp 1169–1178, 2002.
   Google Scholar
• S Brichfield & C Tornasi, A pixel dissimilarity measure that is insensitive to image sampling, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 20, pp 401–406, 1998.
   Google Scholar
• R Maas, B M ter Haar Romeny & M A Viergerer, Area- based computation of stereo disparity with model-based window size selection, International Conference on Computer Vision and Pattern Recognition, pp 106–112. 1999.
   Google Scholar
• M Shimizu & M Okutomi, Precise sub-pixel estimation on area-based matching. Proceedings of International Conference on Computer vision, vol II, pp 90–97, 2001.
   Google Scholar
• R Zabih & J Woodfill, Non-parametric local transforms for computing visual correspondence, Proceedings of European Conference on Computer Vision, vol II, pp 151–158, 1994.
   Google Scholar
• P Meer, D Mintz, A Rosenfeld & D Kim, Robust Regression methods for Computer vision: A Review, International Journal of Computer Vision, vol 6, no 1, pp 59–70, 1991.
   Google Scholar
• P Rousseeuw & A Leroy, Robust Regression and outlier detection, New York: Wiley, 1987.
   Google Scholar
• D N Bhat & S K Nayar, Ordinal measures for visual correspondence, Columbia University, Computer Science, Tech Report, CUCS-009–96, 1996.
   Google Scholar
• S S Kumar & B N Chatterji, Stereo Correspondence using Fuzzy Approach, Proceedings of AFSS International Conference on Fuzzy Systems Springer, Calcutta, pp 367–374, 2002.
   Google Scholar
• S S Kumar & B N Chatterji, Stereo Matching Algorithms based on Fuzzy Approach, accepted for publication in International Journal of Pattern Recognition and Artificial Intelligence.
   Google Scholar
• S S Kumar & B N Chatterji, Robust similarity measures for Stereo Correspondence, communicated to Institute of Engineers (India) (Electronics division).
   Google Scholar
• S S Kumar, B N Chatterji & V Ravindranath, Comparative Performance of Rank Correlation Techniques for Stereo Correspondence, Proceedings of International Conference on Recent Trends in Mathematical Sciences, pp 31 1–318, Kharagpur, Narosa, 2000.
   Google Scholar
• D Marr & T Poggio, A theory of human stereo vision, Proc Roy Soc, London, Vol B 204, pp 301–328, 1979.
   Google Scholar
• W E L Grimson, Computational Experiments with a feature based stereo algorithm, IEEE Transactions of Pattern Analysis and Machine Intelligence, vol 7, no I, pp. 17–34, 1985.
   Google Scholar
• S Mallat, Zero-Crossings of a wavelet transform, IEEE Transactions on Information Theory, vol 37, no 4, pp 1019–1034, 1991.
   Google Scholar
• Y Ohta & T Kanade, Stereo by Intra and Inter-scan line search using dynamic programming, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 7, no 2, pp 139–154, 1985.
   Google Scholar
• G Pajares, J M Cruz & J Aranda, Stereo matching based on the self-organizing feature-mapping algorithm, Pattern Recognition Letters, vol 19, pp 319–330, 1998.
   Google Scholar
• G Pajares & J M Cruz, Local stereovision matching through the ADALINE neural network, Pattern Recognition Letters, vol 22, pp 1457–1473, 2001.
   Google Scholar
• G Medioni & R Nevatia, Segment-Based stereo matching, Computer Graphics and Image Processing, vol 31, pp 2–18, 1985.
   Google Scholar
• A Bigand, T Bouwamans & J P Dubus, A new stereo matching algorithm based on linear features and the fuzzy integral, Pattern Recognition Letters, vol 22, pp 133–146, 2001.
   Google Scholar
• NM Nasrabadi & C Y Choo, Hopfield network for stereo vision correspondence, IEEE Transactions on Neural Networks, vol 3, pp 5–13, 1992.
   Google Scholar
• G Pajares, J M Cruz & J Aranda, Relaxation by Hopfield network in stereo matching, Pattern Recognition, vol 31, pp 561–574, 1998.
   Google Scholar
• R K K Yip & W P Ho, A multilevel dynamic programming for stereo line matching, Pattern Recognition Letters, vol 19, pp 839–855, 1998.
   Google Scholar
• J Canny, A Computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 8, no 6, pp 679–698, 1986.
   Google Scholar
• A Kundu, Robust Edge Detection, Pattern Recognition, vol 23, no 5, pp 423–440, 1990.
   Google Scholar
• R Nevatia & K Babu, Linear feature extraction and detection, Computer Graphics and Image Processing, vol 13, pp 257–269,1980.
   Google Scholar
• K Koundinya & B Chand, Detecting lines in gray level images using search techniques, Signal Processing, vol 37, pp 287–299, 1994.
   Google Scholar
• G Medioni & Y Yasumoto, Corner detection and Curve representation using Cubic splines, Computer Vision, Graphics and Image Processing, vol 39, pp 267–278,1987.
   Google Scholar
• M Trajkovic & M Hedley, Fast comer detection, Image and Vision Computing, vol 16, pp 75–87, 1998.
   Google Scholar
• S T Barnard, Stochastic Stereo Matching over scale, International Journal of Computer Vision, vol3, pp 17–32, 1989.
   Google Scholar
• J P Starink & E Backer, Finding point correspondences using Simulated Annealing, Pattern Recognition, vol 28, no 2, pp 231–240, 1995.
   Google Scholar
• I J Cox, S L Hingorani & S B Rao, A maximum likelihood Stereo Algorithm, Computer Vision and Image Understanding, vol 63, no 3, pp 542–567, 1996.
   Google Scholar
• G Q Wei, W Brauer & G Heirzinger, Intensity and Gradient based stereo matching using hierarchical Gaussian basis functions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 20, no 11, pp 1143–1160, 1998.
   Google Scholar
• C Tomase & R Manduchi, Stereo matching as a nearest neighbor problem, IEEE Transactions on Analysis and Machine Intelligence, vol 20, no 11, 1998.
   Google Scholar
• C L Zitnick & T Kanade, A co-operative algorithm for stereo matching and occlusion detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 22, no 7, pp 675–684, 2000.
   Google Scholar
• R Szeliski & P Golland, Stereo matching with transparency and matting, Proceedings of 6th International Conference on Computer Vision, Bombay, Narosa, pp 517–524, 1998.
   Google Scholar
• R D Henkel, Fast Stereovision with sub-pixel precision, Proceedings of International Conference on Computer Vision, Bombay, Narosa, pp 1024–1028, 1998.
   Google Scholar
• D Scharstein & R Szeliski, Stereo matching with nonlinear diffusion, International Journal of Computer Vision, vol 28, no 2, pp 155–174, 1998.
   Google Scholar
• K Phil Han, K W Song, E Y Chung, S J Cho & Y H Ha, Sterod matching using genetic algorithm with adaptive chromosomes. Pattern Recognition, vol 34, pp 1729–1740, 2001.
   Google Scholar
• S Birchfied & C Tornasi, Depth Discontinuties by pixel to pixel stereo, Proceeding of 6th International Conference on Computer Vision, Bombay, pp 1073–1080, Narosa, 1998.
   Google Scholar
• J Weng, N Ahuja & T S Huang, Matching two perspective views, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 14, pp 806–825, 1992.
   Google Scholar
• S D Cochran & G Medioni, 3D surface description from binocular steroe, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 14, pp 981–994, 1992.
   Google Scholar
• W Hoff & N Ahuja, Surface from Stereo: Integrating feature matching, Disparity Estimation and Contour detection; IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 11, pp 121–136, 1989.
   Google Scholar
• M E Ansari, L Masmoudi & L Radouane, A new region matching method for stereoscopic images. Pattern Recongition Letters, vol 21, pp 283–294, 2000.
   Google Scholar
• K P Han, T M Bae & Y H Ha, Hybrid stereo matching with a new relaxation scheme of preserving disparity discontinuity. Pattern Recognition, vol 33, pp 767–785, 2000.
   Google Scholar
• G Fielding & M Kam, Weighted matchings of dense stereo correspondence, Pattern Recognition, vol 33, pp 1511–1524, 2000.
   Google Scholar
• G Fielding & M Kam, Disparity maps for dynamic stereo, Pattern Recognition, vol 34, pp 531–534, 2001.
   Google Scholar
• S S Kumar & B N Chatterji, Stereo Correspondence uzing Zernike Moments, communicated to Pattern Recognition Journal.
   Google Scholar
• C H Teh & R T Chin, On Image Analysis by the Methods of Moments, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 10, no. 4, pp 496–512, 1988.
   Google Scholar