Refinement of matching costs for stereo disparities using recurrent neural networks

Basit Öğe Kaydı
dc.authoridpeker, murat/0000-0001-9877-5493
dc.authoridEMLEK, Alper/0000-0001-8161-7181
dc.contributor.authorEmlek, Alper
dc.contributor.authorPeker, Murat
dc.date.accessioned2024-11-07T13:25:06Z
dc.date.available2024-11-07T13:25:06Z
dc.date.issued2021
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractDepth is essential information for autonomous robotics applications that need environmental depth values. The depth could be acquired by finding the matching pixels between stereo image pairs. Depth information is an inference from a matching cost volume that is composed of the distances between the possible pixel points on the pre-aligned horizontal axis of stereo images. Most approaches use matching costs to identify matches between stereo images and obtain depth information. Recently, researchers have been using convolutional neural network-based solutions to handle this matching problem. In this paper, a novel method has been proposed for the refinement of matching costs by using recurrent neural networks. Our motivation is to enhance the depth values obtained from matching costs. For this purpose, to attain an enhanced disparity map by utilizing the sequential information of matching costs in the horizontal space, recurrent neural networks are used. Exploiting this sequential information, we aimed to determine the position of the correct matching point by using recurrent neural networks, as in the case of speech processing problems. We used existing stereo algorithms to obtain the initial matching costs and then improved the results by utilizing recurrent neural networks. The results are evaluated on the KITTI 2012 and KITTI 2015 datasets. The results show that the matching cost three-pixel error is decreased by an average of 14.5% in both datasets.
dc.description.sponsorshipNigde Omer Halisdemir University Research Project Unit [MMT 2019/7-BAGEP]
dc.description.sponsorshipThis work is supported by Nigde Omer Halisdemir University Research Project Unit under the research grant of MMT 2019/7-BAGEP.
dc.identifier.doi10.1186/s13640-021-00551-9
dc.identifier.issn1687-5176
dc.identifier.issn1687-5281
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85104010665
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1186/s13640-021-00551-9
dc.identifier.urihttps://hdl.handle.net/11480/14513
dc.identifier.volume2021
dc.identifier.wosWOS:000637497300001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofEurasip Journal on Image and Video Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectComputer vision
dc.subjectMulti-layer neural networks
dc.subjectRecurrent neural networks
dc.subjectStereo image processing
dc.titleRefinement of matching costs for stereo disparities using recurrent neural networks
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu