<title>3D articulated object recognition: a case study</title>

Abstract

A simple method for visualizing, understanding, interpreting, and recognizing 3D objects from 2D images is presented. It extended the linear combination methods, uses parallel pattern matching and can handle 3D rigid concave objects as well convex objects, yet, needs only a very small number of learning samples. Some real images are illustrated, with future research discussed including more complicated images such as 3D concave and articulated objects. Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/20/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx * the model consists of several imagesminimally three for polyhedra. An unknown object is matched with a model by comparing the points in an image of the unknown object with a template-like collection of points produced from the model. In general, an unknown object can be arbitrarilly rotated, arbitrarilly translated and even arbitrarilly scaled relative to an arbitrary original position. From the SPIE Vol. 2904 115 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/20/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx