{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Jiang Y"],"funding":["Guangdong Province Science and Technology Program","Science, Technology and Innovation Commission of Shen-zhen Municipality"],"pagination":["3427"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10098972"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["23(7)"],"pubmed_abstract":["Stereo matching in binocular endoscopic scenarios is difficult due to the radiometric distortion caused by restricted light conditions. Traditional matching algorithms suffer from poor performance in challenging areas, while deep learning ones are limited by their generalizability and complexity. We introduce a non-deep learning cost volume generation method whose performance is close to a deep learning algorithm, but with far less computation. To deal with the radiometric distortion problem, the initial cost volume is constructed using two radiometric invariant cost metrics, the histogram of gradient angle and amplitude descriptors. Then we propose a new cross-scale propagation framework to improve the matching reliability in small homogenous regions without increasing the running time. T"],"journal":["Sensors (Basel, Switzerland)"],"pubmed_title":["Robust Cost Volume Generation Method for Dense Stereo Matching in Endoscopic Scenarios."],"pmcid":["PMC10098972"],"funding_grant_id":["2019B010143003","WDZC20200820160650001"],"pubmed_authors":["Jiang Y","Mai S","Dong Z"],"additional_accession":[]},"is_claimable":false,"name":"Robust Cost Volume Generation Method for Dense Stereo Matching in Endoscopic Scenarios.","description":"Stereo matching in binocular endoscopic scenarios is difficult due to the radiometric distortion caused by restricted light conditions. Traditional matching algorithms suffer from poor performance in challenging areas, while deep learning ones are limited by their generalizability and complexity. We introduce a non-deep learning cost volume generation method whose performance is close to a deep learning algorithm, but with far less computation. To deal with the radiometric distortion problem, the initial cost volume is constructed using two radiometric invariant cost metrics, the histogram of gradient angle and amplitude descriptors. Then we propose a new cross-scale propagation framework to improve the matching reliability in small homogenous regions without increasing the running time. T","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Mar","modification":"2025-05-29T19:44:26.427Z","creation":"2025-04-04T12:15:18.462Z"},"accession":"S-EPMC10098972","cross_references":{"pubmed":["37050489"],"doi":["10.3390/s23073427"]}}