Wide baseline stereo matching based on scale invariant feature transformation with hybrid geometric constraints
Wide baseline stereo matching is a challenging task because of the presence of significant geometric deformations and illumination changes within the images. Based on the scale invariant feature transformation (SIFT) algorithm, this study proposes a new hybrid matching scheme that uses both the feat...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-12-01
|
| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2013.0265 |
| _version_ | 1797684546620096512 |
|---|---|
| author | Huachao Yang Mei Yu Shubi Zhang |
| author_facet | Huachao Yang Mei Yu Shubi Zhang |
| author_sort | Huachao Yang |
| collection | DOAJ |
| description | Wide baseline stereo matching is a challenging task because of the presence of significant geometric deformations and illumination changes within the images. Based on the scale invariant feature transformation (SIFT) algorithm, this study proposes a new hybrid matching scheme that uses both the feature‐based and the area‐based methods to find reliable matches from sparse to dense under different geometric constraints. Firstly, the authors propose a SIFT‐based robust weighted least squares matching (LSM) method modelled by a two‐dimensional (2D) projective transformation to establish the initial correspondences and their local homographies. In this method, a normalised cross correlation metric modified with an adaptive scale and an orientation of the SIFT features (SIFT‐NCC) is proposed to find a good initial alignment for the SIFT‐LSM. Secondly, a robust matching propagation using the SIFT‐NCC starts from the initial matches under an epipolar geometry and the local homography constraints; geometrical consistency checking is used simultaneously to identify the false matches. Thirdly, they use an improved, feature‐based SIFT matching method to find the correspondences from the points that are not coplanar in the 3D space under an epipolar constraint only. A bidirectional selection strategy is used to remove the error matches. |
| first_indexed | 2024-03-12T00:31:20Z |
| format | Article |
| id | doaj.art-7e6c85be83104bedbc072eaccd88b9ea |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:31:20Z |
| publishDate | 2014-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-7e6c85be83104bedbc072eaccd88b9ea2023-09-15T10:15:58ZengWileyIET Computer Vision1751-96321751-96402014-12-018661161910.1049/iet-cvi.2013.0265Wide baseline stereo matching based on scale invariant feature transformation with hybrid geometric constraintsHuachao Yang0Mei Yu1Shubi Zhang2School of Environmental and Spatial InformaticsChina University of Mining and TechnologyXuzhouPeople's Republic of ChinaSchool of Environmental and Spatial InformaticsChina University of Mining and TechnologyXuzhouPeople's Republic of ChinaSchool of Environmental and Spatial InformaticsChina University of Mining and TechnologyXuzhouPeople's Republic of ChinaWide baseline stereo matching is a challenging task because of the presence of significant geometric deformations and illumination changes within the images. Based on the scale invariant feature transformation (SIFT) algorithm, this study proposes a new hybrid matching scheme that uses both the feature‐based and the area‐based methods to find reliable matches from sparse to dense under different geometric constraints. Firstly, the authors propose a SIFT‐based robust weighted least squares matching (LSM) method modelled by a two‐dimensional (2D) projective transformation to establish the initial correspondences and their local homographies. In this method, a normalised cross correlation metric modified with an adaptive scale and an orientation of the SIFT features (SIFT‐NCC) is proposed to find a good initial alignment for the SIFT‐LSM. Secondly, a robust matching propagation using the SIFT‐NCC starts from the initial matches under an epipolar geometry and the local homography constraints; geometrical consistency checking is used simultaneously to identify the false matches. Thirdly, they use an improved, feature‐based SIFT matching method to find the correspondences from the points that are not coplanar in the 3D space under an epipolar constraint only. A bidirectional selection strategy is used to remove the error matches.https://doi.org/10.1049/iet-cvi.2013.0265wide baseline stereo matchinghybrid geometric constraintsgeometric deformationsillumination changesscale invariant feature transformation algorithmSIFT algorithm |
| spellingShingle | Huachao Yang Mei Yu Shubi Zhang Wide baseline stereo matching based on scale invariant feature transformation with hybrid geometric constraints IET Computer Vision wide baseline stereo matching hybrid geometric constraints geometric deformations illumination changes scale invariant feature transformation algorithm SIFT algorithm |
| title | Wide baseline stereo matching based on scale invariant feature transformation with hybrid geometric constraints |
| title_full | Wide baseline stereo matching based on scale invariant feature transformation with hybrid geometric constraints |
| title_fullStr | Wide baseline stereo matching based on scale invariant feature transformation with hybrid geometric constraints |
| title_full_unstemmed | Wide baseline stereo matching based on scale invariant feature transformation with hybrid geometric constraints |
| title_short | Wide baseline stereo matching based on scale invariant feature transformation with hybrid geometric constraints |
| title_sort | wide baseline stereo matching based on scale invariant feature transformation with hybrid geometric constraints |
| topic | wide baseline stereo matching hybrid geometric constraints geometric deformations illumination changes scale invariant feature transformation algorithm SIFT algorithm |
| url | https://doi.org/10.1049/iet-cvi.2013.0265 |
| work_keys_str_mv | AT huachaoyang widebaselinestereomatchingbasedonscaleinvariantfeaturetransformationwithhybridgeometricconstraints AT meiyu widebaselinestereomatchingbasedonscaleinvariantfeaturetransformationwithhybridgeometricconstraints AT shubizhang widebaselinestereomatchingbasedonscaleinvariantfeaturetransformationwithhybridgeometricconstraints |