by Haeusler, R and Klette, R
Abstract:
Confidence measures for stereo analysis are not yet a subject of detailed comparative evaluations. There have been some studies, but still insufficient for estimating the performance of these measures. We comparatively discuss confidence measures whose performance appeared to be ‘promising’ to us, by evaluating their performance on commonly used stereo test data. Those data are either engineered and come with accurate ground truth (for disparities), or they are recorded outdoors and come with approximate ground truth. The performance of confidence measures varies widely between these two types of data. We propose modifications of confidence measures which can improve their performance on outdoor data. © 2012 Springer-Verlag.
Reference:
Disparity confidence measures on engineered and outdoor data (Haeusler, R and Klette, R), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 7441 LNCS, 2012.
Bibtex Entry:
@inproceedings{haeusler2012disparitydata,
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 = "624--631",
title = "Disparity confidence measures on engineered and outdoor data",
volume = "7441 LNCS",
year = "2012",
abstract = "Confidence measures for stereo analysis are not yet a subject of detailed comparative evaluations. There have been some studies, but still insufficient for estimating the performance of these measures. We comparatively discuss confidence measures whose performance appeared to be 'promising' to us, by evaluating their performance on commonly used stereo test data. Those data are either engineered and come with accurate ground truth (for disparities), or they are recorded outdoors and come with approximate ground truth. The performance of confidence measures varies widely between these two types of data. We propose modifications of confidence measures which can improve their performance on outdoor data. © 2012 Springer-Verlag.",
doi = "10.1007/978-3-642-33275-3_77",
isbn = "9783642332746",
issn = "0302-9743",
eissn = "1611-3349",
language = "eng",
}