26 September 1997 Finding salient groups in range image data
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Abstract
The human visual system is very good at detecting geometric relationships such as colinearity, parallelism, connectivity, and repetitive patterns in a randomly distributed set of image elements. We believe that such capabilities are useful, if not essential, for the task of object detection and segmentation, shape description and object matching. We propose an algorithm based on some of these capabilities for organizing the fragmented low-level features into meaningfully mid-level description. This algorithm uses an estimated measurement of the curvature, the orientation and the gradient magnitude at each possible location of salient curves in the image. We then presented some geometric constraints between edge elements in term of a neighborhood relationships in order to organize the detected edges into groups. The grouping precess results from a procedure for computing the orientation and curvature by minimizing a natural functional that reduce ambiguity and noise effect. The minimization is accomplished using relaxation labeling technique. The procedure is applied to some images to evaluate the groupings processes and noise sensitivity.
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Mohamed Mkaouar, Mohamed Mkaouar, Richard Lepage, Richard Lepage, Denis Laurendeau, Denis Laurendeau, } "Finding salient groups in range image data", Proc. SPIE 3208, Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling, (26 September 1997); doi: 10.1117/12.290296; https://doi.org/10.1117/12.290296
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