Daniel Scharstein - Publications

Books and book chapters

• D. Scharstein. View Synthesis Using Stereo Vision. Lecture Notes in Computer Science (LNCS), volume 1583. Springer Verlag, 1999.

• A. M. Segre, C. Elkan, D. Scharstein, G. Gordon, and A. Russell. Adaptive inference. In A. L. Meyrowitz and S. Chipman, editors, Foundations of Knowledge Acquisition: Machine Learning, volume 2 of The Kluwer International Series in Engineering and Computer Science, chapter 2, pages 43-81. Kluwer Academic, Norwell, MA, 1993.

Journal articles

• R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. Tappen, and C. Rother. A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(6):1068-1080, June 2008. See also the Middlebury MRF Page.
• A. Briggs, C. Detweiler, Y. Li, P. Mullen, and D. Scharstein. Matching scale-space features in 1D panoramas. Computer Vision and Image Understanding, 103(3):184-195, September 2006.
• A. Briggs, C. Detweiler, D. Scharstein, and A. Vandenberg-Rodes. Expected shortest paths for landmark-based robot navigation. International Journal of Robotics Research (IJRR), 23(7-8):717-728, July-August 2004.
• R. Szeliski and D. Scharstein. Sampling the disparity space image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3):419-425, March 2004.
• D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1/2/3):7-42, April-June 2002. See also the Middlebury Stereo Vision Page.
• D. Scharstein and A. Briggs. Real-time recognition of self-similar landmarks. Image and Vision Computing, 19(11):763-772, September 2001.
• D. Scharstein and R. Szeliski. Stereo matching with nonlinear diffusion. International Journal of Computer Vision, 28(2):155-174, June/July 1998.
• M. Dickerson and D. Scharstein. Optimal placement of convex polygons to maximize point containment. Computational Geometry: Theory and Applications, 11(1):1-16, August 1998.
• A. M. Segre and D. Scharstein. Bounded-overhead caching for definite-clause theorem proving. Journal of Automated Reasoning, 11:83-113, November 1993.

Refereed conference articles

• S. Baker, D. Scharstein, J.P. Lewis, S. Roth, M. Black, and R. Szeliski. A database and evaluation methodology for optical flow. In IEEE International Conference on Computer Vision (ICCV 2007), Rio de Janeiro, Brazil, October 2007.
• D. Scharstein and C. Pal. Learning conditional random fields for stereo. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), Minneapolis, MN, June 2007.
• H. Hirschmüller and D. Scharstein. Evaluation of cost functions for stereo matching. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), Minneapolis, MN, June 2007.
• S. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski. A comparison and evaluation of multi-view stereo reconstruction algorithms. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), volume 1, pages 519-526, New York, NY, June 2006. See also the Middlebury Multi View Page.
• R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. Tappen, and C. Rother. A comparative study of energy minimization methods for Markov random fields. In Ninth European Conference on Computer Vision (ECCV 2006), volume 2, pages 19-26, Graz, Austria, May 2006. See also the Middlebury MRF Page.
• A. Briggs, Y. Li, D. Scharstein, and M. Wilder. Robot navigation using 1D panoramic images. In International Conference on Robotics and Automation (ICRA 2006), pages 2679-2685, Orlando, FL, May 2006.
• A. Briggs, Y. Li, and D. Scharstein. Feature matching across 1D panoramas. In Omnivis 2005, the sixth Workshop on Omnidirectional Vision (in conjunction with ICCV 2005), Beijing, China, October 2005.
• A. Briggs, C. Detweiler, P. Mullen, and D. Scharstein. Scale-space features in 1D omnidirectional images. In Omnivis 2004, the fifth Workshop on Omnidirectional Vision (in conjunction with ECCV 2004), pages 115-126, Prague, Czech Republic, May 2004.
• D. Scharstein and R. Szeliski. High-accuracy stereo depth maps using structured light. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003), volume 1, pages 195-202, Madison, WI, June 2003.
• A. Briggs, C. Detweiler, D. Scharstein, and A. Vandenberg-Rodes. Expected shortest paths for landmark-based robot navigation. In Fifth International Workshop on Algorithmic Foundations of Robotics (WAFR 2002), Nice, France, December 2002.
• R. Szeliski and D. Scharstein. Symmetric subpixel stereo matching. In Seventh European Conference on Computer Vision (ECCV 2002), volume 2, pages 525-540, Copenhagen, Denmark, May 2002.
• D. Scharstein, R. Szeliski, and R. Zabih. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. In Workshop on Stereo and Multi-Baseline Vision (in conjunction with IEEE CVPR 2001), pages 131-140, Kauai, Hawaii, December 2001.
• A. Briggs, D. Scharstein, and S. Abbott. Reliable mobile robot navigation from unreliable visual cues. In Fourth International Workshop on Algorithmic Foundations of Robotics (WAFR 2000), pages 349-362, Hanover, NH, March 2000.
• A. Briggs, D. Scharstein, D. Braziunas, C. Dima, and P. Wall. Mobile robot navigation using self-similar landmarks. In International Conference on Robotics and Automation (ICRA 2000), pages 1428-1434, San Francisco, CA, April 2000.
• D. Scharstein and A. Briggs. Fast recognition of self-similar landmarks. In Workshop on Perception for Mobile Agents (in conjunction with IEEE CVPR'99), pages 74-81, Fort Collins, CO, June 1999.
• D. Scharstein. Stereo vision for view synthesis. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'96), pages 852-858, San Francisco, CA, June 1996. Abstract available.
• D. Scharstein and R. Szeliski. Stereo matching with non-linear diffusion. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'96), pages 343-350, San Francisco, CA, June 1996. Abstract available. Techreport version.
• M. Dickerson and D. Scharstein. Optimal placement of convex polygons to maximize point containment. In 7th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA'96), pages 114-121, Atlanta, GA, February 1996. Abstract available. On-line Java demo available.
• M. Dickerson and D. Scharstein. The rotation diagram and optimal containing placements of a convex polygon. In 5th Video Review of Computational Geometry (with the 12th ACM Symposium on Computational Geometry), pages V9-V10, Philadelphia, PA, May 1996.
• D. Scharstein. Matching images by comparing their gradient fields. In 12th International Conference on Pattern Recognition (ICPR'94), volume 1, pages 572-575, Jerusalem, Israel, October 1994. Techreport version.

PhD Thesis

• D. Scharstein. View Synthesis Using Stereo Vision. PhD thesis, Cornell University, January 1997. Available as Technical Report CORNELLCS:TR96-1604.

Patents:

• Digital sundial, U.S. patent 5,590,093, issued 12/1996; German patent 4431817, issued 7/1998.