Hierarchical Stereo Matching in Two-Scale Space for Cyber-Physical System
Dense disparity map estimation from a high-resolution stereo image is a very difficult problem in terms of both matching accuracy and computation efficiency. Thus, an exhaustive disparity search at full resolution is required. In general, examining more pixels in the stereo view results in more ambi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/17/7/1680 |
| _version_ | 1828155246044512256 |
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| author | Eunah Choi Sangyoon Lee Hyunki Hong |
| author_facet | Eunah Choi Sangyoon Lee Hyunki Hong |
| author_sort | Eunah Choi |
| collection | DOAJ |
| description | Dense disparity map estimation from a high-resolution stereo image is a very difficult problem in terms of both matching accuracy and computation efficiency. Thus, an exhaustive disparity search at full resolution is required. In general, examining more pixels in the stereo view results in more ambiguous correspondences. When a high-resolution image is down-sampled, the high-frequency components of the fine-scaled image are at risk of disappearing in the coarse-resolution image. Furthermore, if erroneous disparity estimates caused by missing high-frequency components are propagated across scale space, ultimately, false disparity estimates are obtained. To solve these problems, we introduce an efficient hierarchical stereo matching method in two-scale space. This method applies disparity estimation to the reduced-resolution image, and the disparity result is then up-sampled to the original resolution. The disparity estimation values of the high-frequency (or edge component) regions of the full-resolution image are combined with the up-sampled disparity results. In this study, we extracted the high-frequency areas from the scale-space representation by using difference of Gaussian (DoG) or found edge components, using a Canny operator. Then, edge-aware disparity propagation was used to refine the disparity map. The experimental results show that the proposed algorithm outperforms previous methods. |
| first_indexed | 2024-04-11T22:50:42Z |
| format | Article |
| id | doaj.art-41e5da16047b4c15a2833b9f8d5083ef |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:50:42Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-41e5da16047b4c15a2833b9f8d5083ef2022-12-22T03:58:35ZengMDPI AGSensors1424-82202017-07-01177168010.3390/s17071680s17071680Hierarchical Stereo Matching in Two-Scale Space for Cyber-Physical SystemEunah Choi0Sangyoon Lee1Hyunki Hong2Department of Imaging Science and Arts, GSAIM, Chung-Ang University, 221 Huksuk-dong, Dongjak-ku, Seoul 156-756, KoreaSchool of Integrative Engineering, Chung-Ang University, 221 Huksuk-dong, Dongjak-ku, Seoul 156-756, KoreaSchool of Integrative Engineering, Chung-Ang University, 221 Huksuk-dong, Dongjak-ku, Seoul 156-756, KoreaDense disparity map estimation from a high-resolution stereo image is a very difficult problem in terms of both matching accuracy and computation efficiency. Thus, an exhaustive disparity search at full resolution is required. In general, examining more pixels in the stereo view results in more ambiguous correspondences. When a high-resolution image is down-sampled, the high-frequency components of the fine-scaled image are at risk of disappearing in the coarse-resolution image. Furthermore, if erroneous disparity estimates caused by missing high-frequency components are propagated across scale space, ultimately, false disparity estimates are obtained. To solve these problems, we introduce an efficient hierarchical stereo matching method in two-scale space. This method applies disparity estimation to the reduced-resolution image, and the disparity result is then up-sampled to the original resolution. The disparity estimation values of the high-frequency (or edge component) regions of the full-resolution image are combined with the up-sampled disparity results. In this study, we extracted the high-frequency areas from the scale-space representation by using difference of Gaussian (DoG) or found edge components, using a Canny operator. Then, edge-aware disparity propagation was used to refine the disparity map. The experimental results show that the proposed algorithm outperforms previous methods.https://www.mdpi.com/1424-8220/17/7/1680stereo matchingscale space imagedisparity mapdifference of GaussianCanny edge detectorcost aggregation |
| spellingShingle | Eunah Choi Sangyoon Lee Hyunki Hong Hierarchical Stereo Matching in Two-Scale Space for Cyber-Physical System Sensors stereo matching scale space image disparity map difference of Gaussian Canny edge detector cost aggregation |
| title | Hierarchical Stereo Matching in Two-Scale Space for Cyber-Physical System |
| title_full | Hierarchical Stereo Matching in Two-Scale Space for Cyber-Physical System |
| title_fullStr | Hierarchical Stereo Matching in Two-Scale Space for Cyber-Physical System |
| title_full_unstemmed | Hierarchical Stereo Matching in Two-Scale Space for Cyber-Physical System |
| title_short | Hierarchical Stereo Matching in Two-Scale Space for Cyber-Physical System |
| title_sort | hierarchical stereo matching in two scale space for cyber physical system |
| topic | stereo matching scale space image disparity map difference of Gaussian Canny edge detector cost aggregation |
| url | https://www.mdpi.com/1424-8220/17/7/1680 |
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