Abstract
We first describe two stochastic algorithms which build trees in high dimensional Euclidean spaces with some adaptation to the geometry of a chosen target subset. The second one produces search trees and is used to approximately identify in real time the pose of a polyhedron from its external contour. A search tree is first grown in a space of shapes of plane curves which are a set of precomputed polygonal outlines of the polyhedron. The tree is then used to find in real time a best match to the outline of the polyhedron in the current pose. Analyzing the deformation of the curves along the tree thus built, shows progressive differentiation from a simple convex root shape to the various possible external contours, and the tree organizes the complex set of shapes into a more comprehensible object.
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Kergosien, Y.L. (2005). Adaptive Trees and Pose Identification from External Contours of Polyhedra. In: Fogh Olsen, O., Florack, L., Kuijper, A. (eds) Deep Structure, Singularities, and Computer Vision. DSSCV 2005. Lecture Notes in Computer Science, vol 3753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11577812_14
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DOI: https://doi.org/10.1007/11577812_14
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