A novel feature-based tracking approach to the detection, localization, and 3-D reconstruction of internal defects in hardwood logs using computer tomography
| Autor: | Richard L. Daniels, E. William Tollner, Xingzhi Luo, Suchendra M. Bhandarkar |
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| Rok vydání: | 2006 |
| Předmět: |
business.industry
Computer science Image segmentation Kalman filter Tracking (particle physics) Edge detection Artificial Intelligence Feature (computer vision) Nondestructive testing Pattern recognition (psychology) Computer vision Computer Vision and Pattern Recognition Artificial intelligence Tomography business |
| Zdroj: | Pattern Analysis and Applications. 9:155-175 |
| ISSN: | 1433-755X 1433-7541 |
| Popis: | A novel feature-based tracking approach based on the Kalman filter is proposed for the detection, localization, and 3-D reconstruction of internal defects in hardwood logs from cross-sectional computer tomography (CT) images. The defects are simultaneously detected, classified, localized, and reconstructed in 3-D space, making the proposed scheme computationally much more efficient than existing methods where the defects are detected and localized independently in individual CT image slices and the 3-D reconstruction of the defects accomplished via correspondence analysis across the various CT image slices. Robust techniques for defect detection and classification are proposed. Defect class-specific tracking schemes based on the Kalman filter, B-spline contour approximation, and Snakes contour fitting are designed which use the geometric parameters of the defect contours as the tracking variables. Experimental results on cross-sectional CT images of hardwood logs from select species such as white ash, hard maple, and red oak are presented. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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