Object classification using tripod operators

David Bonanno; Frank Pipitone; G. Charmaine Gilbreath; Kristen Nock; Carlos A. Font; Chadwick T. Hawley

doi:10.1117/12.2069529

8 August 2014 Object classification using tripod operators

David Bonanno, Frank Pipitone, G. Charmaine Gilbreath, Kristen Nock, Carlos A. Font, Chadwick T. Hawley

Proceedings Volume 9082, Active and Passive Signatures V; 90820C (2014) https://doi.org/10.1117/12.2069529
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

Over the last few decades, we have seen an increase in both quality and quantity of 3D data sets. These data sets primarily come in the form of discrete points that are projected onto the surface of the object (point clouds) and are often derived from either LIDAR data (in which case, the surface points are actively sensed) or stereoscopic pairs (in which case, the surface points are derived using two dimensional (2D) feature matching algorithms). As these data sets become larger and denser, they also become harder to sift through which demands methods for automatic object classification through computer vision processes. In this paper we revisit a method of recognizing objects from their surface features known as Tripod Operators.[1] More specifically, we explore how matching multiple features from an unknown object to a known shape allows us to determine the extent to which the objects are similar using the resultant Digital Elevation Model (DEM) or Surface Elevation Model (SEM) that results from manipulation of point clouds.. We apply this method to determine how to separate objects of various classes.

Citation Download Citation

David Bonanno, Frank Pipitone, G. Charmaine Gilbreath, Kristen Nock, Carlos A. Font, and Chadwick T. Hawley "Object classification using tripod operators", Proc. SPIE 9082, Active and Passive Signatures V, 90820C (8 August 2014); https://doi.org/10.1117/12.2069529

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