Learning the probability of correspondences without ground truth

Proceedings of the IEEE International Conference on Computer Vision

Volumn II, Issue , 2005, Pages 1140-1147

  Yang, Qingxiong ^a   Steele, R Matt ^a   Nistér, David ^a   Jaynes, Christopher ^a  

^a UNIVERSITY OF KENTUCKY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ACQUISITION; ESTIMATION; EVALUATION; IMAGE PROCESSING; MANUAL CONTROL; ONLINE SYSTEMS; PERFORMANCE; PROBABILITY DISTRIBUTIONS; SYSTEMS ANALYSIS; VECTORS;

BLACK BOXES; CORRESPONDENCE EXTRACTION SCHEMES; IMAGE PATCHES; LOCAL MOTION VECTORS;

QUALITY CONTROL;

EID: 33745917092     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. S. Baker and Shree K. Nayar, Global Measures of Coherence for Edge Detector Evaluation, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Volume 2, Pages 373-379, Fort Collins, Colorado, June, 1999.
2. D. Claus and A. Fitzgibbon, Reliable Fiducial Detection in Natural Scenes European Conference on Computer Vision, Prague, Czech Republic, 2004.
3. A. Heyden, K. Rohr, Evaluation of Corner Extraction Schemes Using Invariance Methods, ICPR96, A94.3.
4. K. Mikolajczk and C. Schmid, A performance evaluation of local descriptors. IEEE Conference on Computer Vision and Pattern Recognition, June 2003.
5. D. Scharstein and R. Szeliski, A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, International Journal of Computer Vision, 47(1):7-42, May 2002.
6. C. Schmid, R. Mohr and C. Bauckhage, Evaluation of Interest Point Detectors, In International Journal of Computer Vision, 37(2), 151-172, 2000.
7. Z. Tu, X. Chen, A.L. Yuille and S.C. Zhu. Image Parsing: Unifying Segmentation, Detection, and Recognition. International Conference on Computer Vision, 2003.
8. J.M. Coughlan and A.L. Yuille, Manhattan World: Compass Direction from a Single Image by Bayesian Inference. International Conference on Computer Vision, Corfu, Greece, 1999.
9. Y. Wexler, A. Fitzgibbon and A. Zisserman, Learning Epipolar Geometry from Image Sequences, IEEE Conference on Computer Vision and Pattern Recognition, Volume 2, pp. 209-216, 2003.
10. W. Förstner, 10 Pros and Cons Against Performance Characterization of Vision Algorithms, ECCV Workshop on Performance Characteristics of Vision Algorithms, 1996.
11. P. Rosin and E. Ioannidis, Evaluation of global image thresholding for change detection, Pattern Recogn. Lett., Elsevier Science Inc. 24(14):2345-2356, 2003.
12. K. Bowyer and P. J. Phillips, Empirical Evaluation Techniques in Computer Vision, IEEE Computer Society Press, Los Alamitos, CA, 1998.