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1	Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces Taati, BABAK 01 September 2009 (has links) We formulate Local Shape Descriptor selection for model-based object recognition in range data as an optimization problem and offer a platform that facilitates a solution. The goal of object recognition is to identify and localize objects of interest in an image. Recognition is often performed in three phases: point matching, where correspondences are established between points on the 3-D surfaces of the models and the range image; hypothesis generation, where rough alignments are found between the image and the visible models; and pose refinement, where the accuracy of the initial alignments is improved. The overall efficiency and reliability of a recognition system is highly influenced by the effectiveness of the point matching phase. Local Shape Descriptors are used for establishing point correspondences by way of encapsulating local shape, such that similarity between two descriptors indicates geometric similarity between their respective neighbourhoods. We present a generalized platform for constructing local shape descriptors that subsumes a large class of existing methods and allows for tuning descriptors to the geometry of specific models and to sensor characteristics. Our descriptors, termed as Variable-Dimensional Local Shape Descriptors, are constructed as multivariate observations of several local properties and are represented as histograms. The optimal set of properties, which maximizes the performance of a recognition system, depend on the geometry of the objects of interest and the noise characteristics of range image acquisition devices and is selected through pre-processing the models and sample training images. Experimental analysis confirms the superiority of optimized descriptors over generic ones in recognition tasks in LIDAR and dense stereo range images. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2009-09-01 11:07:32.084 computer vision range data object recognition tracking local shape descriptor point matching pose estimation pose acquisition 3-D 3D point cloud satellite tracking optimization range image processing range image RANSAC registration alignment surface computational geometry detection localization model-based object identification point correspondence feature selection VD-LSD LSD genetic algorithm simulated annealing forward feature selection multivariate features subset selection local properties LIDAR dense stereo stereo precision feature matching machine learning training learning phase preprocessing

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Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces