29 January 2007 Complete boundary detection of textured objects via deformable models
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Abstract
Object recognition using the shape of objects boundaries and surface reconstruction using slice contours rely on the identification of the correct and complete boundary information of the segmented objects in the scene. Geometric deformable models (GDM) using the level sets method provide a very efficient framework for image segmentation. However, the segmentation results provided by these models are dependent on the contour initialization. Also, if there are textured objects in the scene, usually the incorrect boundaries are detected. In most cases where the strategy is to detect the correct boundary of all the objects in the scene, the results of the segmentation will only provide incomplete and/or inaccurate object's boundaries. In this work, we propose a new method to detect the correct boundary information of segmented objects, in particular textured objects. We use the average squared gradient to determine the appropriate initialization positions and by varying the size of the test regions we create multiple images, that we will call layers, to determine the appropriate boundaries.
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Renato Dedić, Madjid Allili, "Complete boundary detection of textured objects via deformable models", Proc. SPIE 6499, Vision Geometry XV, 64990H (29 January 2007); doi: 10.1117/12.705722; https://doi.org/10.1117/12.705722
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