by Haeusler, R and Klette, R
Abstract:
Current research in stereo image analysis focuses on improving matching algorithms in terms of accuracy, computational costs, and robustness towards real-time applicability for complex image data and 3D scenes. Interestingly, performance testing takes place for a huge number of algorithms, but, typically, on very small sets of image data only. Even worse, there is little reasoning whether data as commonly applied is actually suitable to prove robustness or even correctness of a particular algorithm. We argue for the need of testing stereo algorithms on a much broader variety of image data then done so far by proposing a simple measure for putting image stereo data of different quality into relation to each other. Potential applications include purpose-directed decisions for the selection of image stereo data for testing the applicability of matching techniques under particular situations, or for realtime estimation of stereo performance (without any need for providing ground truth) in cases where techniques should be selected depending on the given situation. © 2010 Springer-Verlag.
Reference:
Benchmarking stereo data (not the matching algorithms) (Haeusler, R and Klette, R), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 6376 LNCS, 2010.
Bibtex Entry:
@inproceedings{haeusler2010benchmarkingalgorithms,
author = "Haeusler, R and Klette, R",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "383--392",
title = "Benchmarking stereo data (not the matching algorithms)",
volume = "6376 LNCS",
year = "2010",
abstract = "Current research in stereo image analysis focuses on improving matching algorithms in terms of accuracy, computational costs, and robustness towards real-time applicability for complex image data and 3D scenes. Interestingly, performance testing takes place for a huge number of algorithms, but, typically, on very small sets of image data only. Even worse, there is little reasoning whether data as commonly applied is actually suitable to prove robustness or even correctness of a particular algorithm. We argue for the need of testing stereo algorithms on a much broader variety of image data then done so far by proposing a simple measure for putting image stereo data of different quality into relation to each other. Potential applications include purpose-directed decisions for the selection of image stereo data for testing the applicability of matching techniques under particular situations, or for realtime estimation of stereo performance (without any need for providing ground truth) in cases where techniques should be selected depending on the given situation. © 2010 Springer-Verlag.",
doi = "10.1007/978-3-642-15986-2_39",
isbn = "3642159850",
isbn = "9783642159855",
issn = "0302-9743",
eissn = "1611-3349",
language = "eng",
}