Abstract
A method is proposed to enhance boundary primitives of multi-part objects of unknown specific shape and appearance in natural images. Its input is a strictly over-segmented constant-curvature contour primitive (CCP) map. Each circular arc or straight-line segment primitive from the map has an unknown origin which may be the closed boundary of a multi-part object, the textured or marked region enclosed by that boundary, or the external background region. Five simple criteria are applied in order to weight each contour primitive and eliminate the weakest ones. The criteria are defined on the basis of the superposition of the CCP map on a multiscale quadtree segmentation of the original intensity image. A subjective ground-truth binary map is used to assess the degree to which the final weighted map corresponds to a selective enhancement of the primitives on the object boundary. Experimental results confirm the potential of the method to selectively enhance, in images of variable complexity, actual boundary primitives of natural and man-made multi-part objects of diverse shapes and appearances.
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Bergevin, R., Bergeron, V. (2008). Enhancing Boundary Primitives Using a Multiscale Quadtree Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89639-5_5
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DOI: https://doi.org/10.1007/978-3-540-89639-5_5
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