ORCID Identifier(s)

0000-0002-7079-0146

Graduation Semester and Year

2016

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Computer Science

Department

Computer Science and Engineering

First Advisor

Manfred Huber

Abstract

Most pattern recognition approaches to object identification work in the image domain. However this is ignoring potential information that can be provided by depth information. Using range images, we can build a set of geometric depth features. These depth features can be used to identify basic three-dimensional shape primitives. There have been many studies regarding object identification in humans that postulate that at least at a primary level object recognition works by breaking down objects into its component parts. To build a similar Recognition-by-component (RBC) system we need a system to identify these shape primitives. We build a depth feature learner by extending a sparse autoencoder neural network into a model similar to a convolutional neural network to learn supersized features that can be matched to patches extracted from depth images. This allows us to convert a collection of patches from a depth image of an object into converted into the space defined by the best fit on each of these supersized features. We also train a backpropagation network to identify shape primitives from patches from known shape primitives that have been converted into this feature space.

Keywords

Object recognition, Pattern recognition, Neural networks, Geon

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

Download

Share

COinS