Active camera calibration for a head-eye platform using the variable state-dimension filter
This correspondence presents a new technique for calibrating a camera mounted on a controllable head/eye platform. It uses the trajectories of an arbitrary number of tracked corner features to improve the calibration parameter estimates over time, utilizing a novel variable state dimension form of r...
| Κύριοι συγγραφείς: | , |
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| Μορφή: | Journal article |
| Γλώσσα: | English |
| Έκδοση: |
1996
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| _version_ | 1826280966272843776 |
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| author | McLauchlan, P Murray, D |
| author_facet | McLauchlan, P Murray, D |
| author_sort | McLauchlan, P |
| collection | OXFORD |
| description | This correspondence presents a new technique for calibrating a camera mounted on a controllable head/eye platform. It uses the trajectories of an arbitrary number of tracked corner features to improve the calibration parameter estimates over time, utilizing a novel variable state dimension form of recursive filter. No special visual stimuli are required and no assumptions are made about the structure of the scene, other than that it is stationary relative to the head. The algorithm runs at 4 frames per second on a single InmosTSOS transputer, and is fully integrated into a real-time active vision system. Updated calibration parameters are regularly passed to the vision modules that require them. Although the algorithm requires an initial estimate of camera focal length, results are presented from real experiments demonstrating that convergence is achieved for initial errors up to 50%. © 1996 IFFE. |
| first_indexed | 2024-03-07T00:21:40Z |
| format | Journal article |
| id | oxford-uuid:7cc2f600-7b52-4d1d-a3c6-4cfb4b3922b9 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T00:21:40Z |
| publishDate | 1996 |
| record_format | dspace |
| spelling | oxford-uuid:7cc2f600-7b52-4d1d-a3c6-4cfb4b3922b92022-03-26T20:59:12ZActive camera calibration for a head-eye platform using the variable state-dimension filterJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:7cc2f600-7b52-4d1d-a3c6-4cfb4b3922b9EnglishSymplectic Elements at Oxford1996McLauchlan, PMurray, DThis correspondence presents a new technique for calibrating a camera mounted on a controllable head/eye platform. It uses the trajectories of an arbitrary number of tracked corner features to improve the calibration parameter estimates over time, utilizing a novel variable state dimension form of recursive filter. No special visual stimuli are required and no assumptions are made about the structure of the scene, other than that it is stationary relative to the head. The algorithm runs at 4 frames per second on a single InmosTSOS transputer, and is fully integrated into a real-time active vision system. Updated calibration parameters are regularly passed to the vision modules that require them. Although the algorithm requires an initial estimate of camera focal length, results are presented from real experiments demonstrating that convergence is achieved for initial errors up to 50%. © 1996 IFFE. |
| spellingShingle | McLauchlan, P Murray, D Active camera calibration for a head-eye platform using the variable state-dimension filter |
| title | Active camera calibration for a head-eye platform using the variable state-dimension filter |
| title_full | Active camera calibration for a head-eye platform using the variable state-dimension filter |
| title_fullStr | Active camera calibration for a head-eye platform using the variable state-dimension filter |
| title_full_unstemmed | Active camera calibration for a head-eye platform using the variable state-dimension filter |
| title_short | Active camera calibration for a head-eye platform using the variable state-dimension filter |
| title_sort | active camera calibration for a head eye platform using the variable state dimension filter |
| work_keys_str_mv | AT mclauchlanp activecameracalibrationforaheadeyeplatformusingthevariablestatedimensionfilter AT murrayd activecameracalibrationforaheadeyeplatformusingthevariablestatedimensionfilter |