Graph-Cut versus Belief-Propagation Stereo on Real-World Images
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Benchmarking stereo data (not the matching algorithms)
Proceedings of the 32nd DAGM conference on Pattern recognition
Ground truth evaluation of stereo algorithms for real world applications
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Illumination invariant cost functions in semi-global matching
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Evaluation of a new coarse-to-fine strategy for fast semi-global stereo matching
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Real-World stereo-analysis evaluation
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Pyramid transform and scale-space analysis in image analysis
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Optimality in combinations of confidence measures for stereo vision
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Hi-index | 0.00 |
Prediction errors are commonly used when analyzing the performance of a multi-camera stereo system using at least three cameras. This paper discusses this methodology for performance evaluation for the first time on long stereo sequences (in the context of vision-based driver assistance systems). Three cameras are calibrated in an ego-vehicle, and prediction error analysis is performed on recorded stereo sequences. They are evaluated using various common stereo matching algorithms, such as belief propagation, dynamic programming, semi-global matching, or graph cut. Performance is evaluated on both synthetic and real data.