2008 |
23 | EE | Richard Szeliski,
Ramin Zabih,
Daniel Scharstein,
Olga Veksler,
Vladimir Kolmogorov,
Aseem Agarwala,
Marshall F. Tappen,
Carsten Rother:
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors.
IEEE Trans. Pattern Anal. Mach. Intell. 30(6): 1068-1080 (2008) |
2007 |
22 | EE | Heiko Hirschmüller,
Daniel Scharstein:
Evaluation of Cost Functions for Stereo Matching.
CVPR 2007 |
21 | EE | Daniel Scharstein,
Chris Pal:
Learning Conditional Random Fields for Stereo.
CVPR 2007 |
20 | EE | Simon Baker,
Daniel Scharstein,
J. P. Lewis,
Stefan Roth,
Michael J. Black,
Richard Szeliski:
A Database and Evaluation Methodology for Optical Flow.
ICCV 2007: 1-8 |
2006 |
19 | EE | Steven M. Seitz,
Brian Curless,
James Diebel,
Daniel Scharstein,
Richard Szeliski:
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms.
CVPR (1) 2006: 519-528 |
18 | EE | Richard Szeliski,
Ramin Zabih,
Daniel Scharstein,
Olga Veksler,
Vladimir Kolmogorov,
Aseem Agarwala,
Marshall F. Tappen,
Carsten Rother:
A Comparative Study of Energy Minimization Methods for Markov Random Fields.
ECCV (2) 2006: 16-29 |
17 | | Amy J. Briggs,
Yunpeng Li,
Daniel Scharstein,
Matt Wilder:
Robot Navigation using 1D Panoramic Images.
ICRA 2006: 2679-2685 |
16 | EE | Amy J. Briggs,
Carrick Detweiler,
Yunpeng Li,
Peter C. Mullen,
Daniel Scharstein:
Matching scale-space features in 1D panoramas.
Computer Vision and Image Understanding 103(3): 184-195 (2006) |
2004 |
15 | EE | Amy J. Briggs,
Carrick Detweiler,
Daniel Scharstein,
Alexander Vandenberg-Rodes:
Expected Shortest Paths for Landmark-Based Robot Navigation.
I. J. Robotic Res. 23(7-8): 717-728 (2004) |
2003 |
14 | EE | Daniel Scharstein,
Richard Szeliski:
High-Accuracy Stereo Depth Maps Using Structured Light.
CVPR (1) 2003: 195-202 |
13 | EE | Richard Szeliski,
Daniel Scharstein:
Sampling the Disparity Space Image.
IEEE Trans. Pattern Anal. Mach. Intell. 26(3): 419-425 (2003) |
2002 |
12 | EE | Richard Szeliski,
Daniel Scharstein:
Symmetric Sub-Pixel Stereo Matching.
ECCV (2) 2002: 525-540 |
11 | | Daniel Scharstein,
Richard Szeliski:
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms.
International Journal of Computer Vision 47(1-3): 7-42 (2002) |
2001 |
10 | EE | Daniel Scharstein,
Amy J. Briggs:
Real-time recognition of self-similar landmarks.
Image Vision Comput. 19(11): 763-772 (2001) |
2000 |
9 | | Amy J. Briggs,
Daniel Scharstein,
Darius Braziunas,
Cristian Dima,
Peter Wall:
Mobile Robot Navigation using Self-Similar Landmarks.
ICRA 2000: 1428-1434 |
1999 |
8 | | Daniel Scharstein:
View Synthesis Using Stereo Vision
Springer 1999 |
1998 |
7 | | Matthew Dickerson,
Daniel Scharstein:
Optimal placement of convex polygons to maximize point containment.
Comput. Geom. 11(1): 1-16 (1998) |
6 | EE | Daniel Scharstein,
Richard Szeliski:
Stereo Matching with Nonlinear Diffusion.
International Journal of Computer Vision 28(2): 155-174 (1998) |
1996 |
5 | EE | Daniel Scharstein,
Richard Szeliski:
Stereo Matching with Non-Linear Diffusion.
CVPR 1996: 343-350 |
4 | EE | Daniel Scharstein:
Stereo Vision for View Synthesis.
CVPR 1996: 852- |
3 | | Matthew Dickerson,
Daniel Scharstein:
Optimal Placement of Convex Polygons to Maximize Point Containment.
SODA 1996: 114-121 |
2 | EE | Matthew Dickerson,
Daniel Scharstein:
The Rotation Diagram and Optimal Containing Placements of a Convex Polygon.
Symposium on Computational Geometry 1996: V-9-V-10 |
1993 |
1 | | Alberto Maria Segre,
Daniel Scharstein:
Bounded-Overhead Caching for Definite-Clause Theorem Proving.
J. Autom. Reasoning 11(1): 83-113 (1993) |