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Document Type
Thesis - University Access Only
Award Date
2015
Degree Name
Master of Science (MS)
Department / School
Electrical Engineering and Computer Science
First Advisor
Sung Shin
Abstract
Automated vehicle detection and classification is one of many highly researched areas in Computer Science. This paper proposes and presents a hybridized method for classifying objects based on spatial orientation. Specifically, the proposed classification system explores the Histogram of Oriented Gradients feature extractor conjoined with a clustering algorithm to classify vehicle images in an unsupervised manner. HOG is a well-suited feature extractor for dense images rich with contours and edges. The pairing provides an efficacious orientation classifier for vehicle images. Training and sample data exceeded 1.8 million images and was provided by Carsforsale.com, Inc.’s vast catalogue of historical vehicle images.
Library of Congress Subject Headings
Object-oriented methods (Computer science)
Computer vision
Description
Includes bibliographical references (pages 32-35)
Format
application/pdf
Number of Pages
44
Publisher
South Dakota State University
Recommended Citation
Hanson, Austin, "Hybrid Model for Object Orientation Classification" (2015). Electronic Theses and Dissertations. 1841.
https://openprairie.sdstate.edu/etd/1841
Rights Statement
