First Advisor

Melanie Mitchell

Date of Publication

Summer 9-28-2017

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Computer Science

Department

Computer Science

Language

English

Subjects

Neural networks (Computer science), Pattern recognition systems, Computer vision

DOI

10.15760/etd.5824

Physical Description

1 online resource (vi, 48 pages)

Abstract

For the last several years, convolutional neural network (CNN) based object detection systems have used a regression technique to predict improved object bounding boxes based on an initial proposal using low-level image features extracted from the CNN. In spite of its prevalence, there is little critical analysis of bounding-box regression or in-depth performance evaluation. This thesis surveys an array of techniques and parameter settings in order to further optimize bounding-box regression and provide guidance for its implementation. I refute a claim regarding training procedure, and demonstrate the effectiveness of using principal component analysis to handle unwieldy numbers of features produced by very deep CNNs.

Rights

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Persistent Identifier

http://archives.pdx.edu/ds/psu/22151

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