First Advisor

Melanie Mitchell

Term of Graduation

Winter 2019

Date of Publication

3-29-2019

Document Type

Thesis

Degree Name

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

Department

Computer Science

Language

English

Subjects

Neural networks (Computer science), Deep learning (Machine learning), Image processing

DOI

10.15760/etd.6703

Physical Description

1 online resource (viii, 40 pages)

Abstract

Locating a small object in an image -- like a mouse on a computer desk or the door handle of a car -- is an important computer vision problem to solve because in many real life situations a small object may be the first thing that gets operated upon in the image scene. While a significant amount of artificial intelligence and machine learning research has focused on localizing prominent objects in an image, the area of small object detection has remained less explored. In my research I explore the possibility of using context information to localize small objects in an image. Using a Convolutional Neural Network (CNN), I create a regression model to detect a small object in an image where model training is supervised by coordinates of the small object in the image. Since small objects do not have strong visual characteristics in an image, it's difficult for a neural network to discern their pattern because their feature map exhibits low resolution rendering a much weaker signal for the network to recognize. Use of context for object detection and localization has been studied for a long time. This idea is explored by Singh et al. for small object localization by using a multi-step regression process where spatial context is used effectively to localize small objects in several datasets. I extend the idea in this research and demonstrate that the technique of localizing in steps using contextual information when used with transfer learning can significantly reduce model training time.

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

https://archives.pdx.edu/ds/psu/28074

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