DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Seungryong | ko |
dc.contributor.author | Min, Dongbo | ko |
dc.contributor.author | Lin, Stephen | ko |
dc.contributor.author | Sohn, Kwanghoon | ko |
dc.date.accessioned | 2024-08-16T02:00:14Z | - |
dc.date.available | 2024-08-16T02:00:14Z | - |
dc.date.created | 2024-08-16 | - |
dc.date.issued | 2021-07 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v.43, no.7, pp.2345 - 2359 | - |
dc.identifier.issn | 0162-8828 | - |
dc.identifier.uri | http://hdl.handle.net/10203/322315 | - |
dc.description.abstract | We present the deep self-correlation (DSC) descriptor for establishing dense correspondences between images taken under different imaging modalities, such as different spectral ranges or lighting conditions. We encode local self-similar structure in a pyramidal manner that yields both more precise localization ability and greater robustness to non-rigid image deformations. Specifically, DSC first computes multiple self-correlation surfaces with randomly sampled patches over a local support window, and then builds pyramidal self-correlation surfaces through average pooling on the surfaces. The feature responses on the self-correlation surfaces are then encoded through spatial pyramid pooling in a log-polar configuration. To better handle geometric variations such as scale and rotation, we additionally propose the geometry-invariant DSC (GI-DSC) that leverages multi-scale self-correlation computation and canonical orientation estimation. In contrast to descriptors based on deep convolutional neural networks (CNNs), DSC and GI-DSC are training-free (i.e., handcrafted descriptors), are robust to cross-modality, and generalize well to various modality variations. Extensive experiments demonstrate the state-of-the-art performance of DSC and GI-DSC on challenging cases of cross-modal image pairs having photometric and/or geometric variations. | - |
dc.language | English | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.title | Dense Cross-Modal Correspondence Estimation With the Deep Self-Correlation Descriptor | - |
dc.type | Article | - |
dc.identifier.wosid | 000692540900013 | - |
dc.identifier.scopusid | 2-s2.0-85108022643 | - |
dc.type.rims | ART | - |
dc.citation.volume | 43 | - |
dc.citation.issue | 7 | - |
dc.citation.beginningpage | 2345 | - |
dc.citation.endingpage | 2359 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | - |
dc.identifier.doi | 10.1109/TPAMI.2020.2965528 | - |
dc.contributor.localauthor | Kim, Seungryong | - |
dc.contributor.nonIdAuthor | Min, Dongbo | - |
dc.contributor.nonIdAuthor | Lin, Stephen | - |
dc.contributor.nonIdAuthor | Sohn, Kwanghoon | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Estimation | - |
dc.subject.keywordAuthor | Benchmark testing | - |
dc.subject.keywordAuthor | Imaging | - |
dc.subject.keywordAuthor | Robustness | - |
dc.subject.keywordAuthor | Strain | - |
dc.subject.keywordAuthor | Lighting | - |
dc.subject.keywordAuthor | Visualization | - |
dc.subject.keywordAuthor | Cross-modal correspondence | - |
dc.subject.keywordAuthor | pyramidal structure | - |
dc.subject.keywordAuthor | self-correlation | - |
dc.subject.keywordAuthor | local self-similarity | - |
dc.subject.keywordAuthor | non-rigid deformation | - |
dc.subject.keywordPlus | REGISTRATION | - |
dc.subject.keywordPlus | IMAGES | - |
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