Depth Estimation Based on Pyramid Normalized Cross-Correlation Algorithm for Vergence Control
A depth estimation algorithm based on vergence vision using a mechanical joint attached to two cameras is proposed. A Gaussian pyramid template-matching approach is used to align the view of the slave camera to the fixation point of the master camera. The master camera uses an object detection algor...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8502763/ |
| _version_ | 1818329532681158656 |
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| author | Abdulla Mohamed Phil F. Culverhouse Angelo Cangelosi Chenguang Yang |
| author_facet | Abdulla Mohamed Phil F. Culverhouse Angelo Cangelosi Chenguang Yang |
| author_sort | Abdulla Mohamed |
| collection | DOAJ |
| description | A depth estimation algorithm based on vergence vision using a mechanical joint attached to two cameras is proposed. A Gaussian pyramid template-matching approach is used to align the view of the slave camera to the fixation point of the master camera. The master camera uses an object detection algorithm to find the target's centroid and centers it relative to the image coordinates. Then, the vergence movement of the slave camera is performed using a pyramid normalized cross-correlation algorithm. Simple geometric triangulation is employed to compute the depth of that target. This proposed method was implemented using an active binocular vision platform with five degrees of freedom where four degrees of freedom to control the pan and tilt independently, and one degree of freedom to control the baseline, which is the distance between the camera. This system was designed for implementation in agriculture harvesting applications. The Analysis of field trial results indicates a worst-case precision of a target tomatoes' depth to be ±1.32 cm at a depth of 85 cm. |
| first_indexed | 2024-12-13T12:49:34Z |
| format | Article |
| id | doaj.art-3dc578ee70b94ab586364dd3e81a7343 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T12:49:34Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3dc578ee70b94ab586364dd3e81a73432022-12-21T23:45:22ZengIEEEIEEE Access2169-35362018-01-016651996521110.1109/ACCESS.2018.28777218502763Depth Estimation Based on Pyramid Normalized Cross-Correlation Algorithm for Vergence ControlAbdulla Mohamed0https://orcid.org/0000-0001-7626-9346Phil F. Culverhouse1Angelo Cangelosi2Chenguang Yang3Centre for Robotics and Neural Systems, University of Plymouth, Plymouth, U.K.Centre for Robotics and Neural Systems, University of Plymouth, Plymouth, U.K.School of Computer Science, University of Manchester, Manchester, U.K.Zienkiewicz Centre for Computational Engineering, Swansea University, Swansea, U.K.A depth estimation algorithm based on vergence vision using a mechanical joint attached to two cameras is proposed. A Gaussian pyramid template-matching approach is used to align the view of the slave camera to the fixation point of the master camera. The master camera uses an object detection algorithm to find the target's centroid and centers it relative to the image coordinates. Then, the vergence movement of the slave camera is performed using a pyramid normalized cross-correlation algorithm. Simple geometric triangulation is employed to compute the depth of that target. This proposed method was implemented using an active binocular vision platform with five degrees of freedom where four degrees of freedom to control the pan and tilt independently, and one degree of freedom to control the baseline, which is the distance between the camera. This system was designed for implementation in agriculture harvesting applications. The Analysis of field trial results indicates a worst-case precision of a target tomatoes' depth to be ±1.32 cm at a depth of 85 cm.https://ieeexplore.ieee.org/document/8502763/Active stereo visionimage pyramidtemplate-matchingvergence visionharvesting |
| spellingShingle | Abdulla Mohamed Phil F. Culverhouse Angelo Cangelosi Chenguang Yang Depth Estimation Based on Pyramid Normalized Cross-Correlation Algorithm for Vergence Control IEEE Access Active stereo vision image pyramid template-matching vergence vision harvesting |
| title | Depth Estimation Based on Pyramid Normalized Cross-Correlation Algorithm for Vergence Control |
| title_full | Depth Estimation Based on Pyramid Normalized Cross-Correlation Algorithm for Vergence Control |
| title_fullStr | Depth Estimation Based on Pyramid Normalized Cross-Correlation Algorithm for Vergence Control |
| title_full_unstemmed | Depth Estimation Based on Pyramid Normalized Cross-Correlation Algorithm for Vergence Control |
| title_short | Depth Estimation Based on Pyramid Normalized Cross-Correlation Algorithm for Vergence Control |
| title_sort | depth estimation based on pyramid normalized cross correlation algorithm for vergence control |
| topic | Active stereo vision image pyramid template-matching vergence vision harvesting |
| url | https://ieeexplore.ieee.org/document/8502763/ |
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