Mitsubishi Electric Research Laboratories, Inc.

Hien Nguyen

Fatih M Porikli

A shape of an object is represented by a set of points inside and outside the shape. A decision function is learned from the set of points an object. Feature points in the set of points are selected using the decision function, or a gradient of the decision function, and then a local descriptor is determined for each feature point.

Representing object shapes using radial basis function support vector machine classification

A 3D object is represented by a descriptor, wherein a model of the 3D object is a 3D point cloud. A local support for each point p in the 3D point cloud is located, and reference x, y, and z axes are generated for the local support. A polar grid is applied according to the references x, y, and z axes a along an azimuth and a radial directions on an xy plane centered on the point p such that each patch on the grid is a bin for a 2D histogram, wherein the 2D histogram is a 2D matrix F on the grid and each coefficient of the 2D matrix F corresponds to the patch on the grid. For each grid location (k, l), an elevation value F(k, l) is estimated by interpolating the elevation values of the 3D points within the patches to produce the descriptor for the point p.

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College Park, MD, United States of America

Hien Nguyen