DocumentCode :
2541074
Title :
Robust occluding contour detection using the Hausdorff distance
Author :
Yi, Xilin ; Camps, Octmia I.
Author_Institution :
Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA
fYear :
1997
fDate :
17-19 Jun 1997
Firstpage :
962
Lastpage :
968
Abstract :
In this paper, a correlational approach for distinguishing occluding contours from object markings for 3D object modeling is presented. The proposed method is valid under weak perspective projection, does not require to search for correspondences between frames, can handle scaling between consecutive images. Thus can estimate the full Euclidean surface structure, and does not require camera calibration or camera motion measurement. Extensive experimental results show that the method is robust to the occlusion of feature points and image noise unlike previous affine-based approaches. Qualitative and quantitative results for the relation between the required minimum viewing angle change for the detection and the surface curvature are also presented
Keywords :
computational geometry; computer vision; motion estimation; solid modelling; 3D object modeling; Euclidean surface structure; Hausdorff distance; camera calibration; camera motion measurement; correlational approach; feature points; image noise; minimum viewing angle change; object markings; qualitative results; quantitative results; robust occluding contour detection; surface curvature; Cameras; Detection algorithms; Image segmentation; Information processing; Joining processes; Motion control; Multidimensional systems; Pattern matching; Pattern recognition; Robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on
Conference_Location :
San Juan
ISSN :
1063-6919
Print_ISBN :
0-8186-7822-4
Type :
conf
DOI :
10.1109/CVPR.1997.609444
Filename :
609444
Link To Document :
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