Sponsor
Portland State University. Department of Computer Science
First Advisor
Melanie Mitchell
Date of Publication
Spring 5-18-2016
Document Type
Thesis
Degree Name
Master of Science (M.S.) in Computer Science
Department
Computer Science
Language
English
Subjects
Computer vision, Pattern recognition systems, Image processing
DOI
10.15760/etd.2944
Physical Description
1 online resource (v, 38 pages)
Abstract
Object localization is the task of locating objects in an image, typically by finding bounding boxes that isolate those objects. Identifying objects in images that have not had regions of interest labeled by humans often requires object localization to be performed first. The sliding window method is a common naïve approach, wherein the image is covered with bounding boxes of different sizes that form windows in the image. An object classifier is then run on each of these windows to determine if each given window contains a given object. However, because object classification algorithms tend to be computationally expensive, it is helpful to have an effective filter to reduce the number of times those classifiers have to be run.
In this thesis I evaluate one promising approach to object localization: the objectness algorithm proposed by Alexe et al. Specifically, I verify the results given by Alexe et al., and further explore the weaknesses and strengths of their "objectness"
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/17526
Recommended Citation
Coates, Lewis Richard James, "Investigations of an "Objectness" Measure for Object Localization" (2016). Dissertations and Theses. Paper 2949.
https://doi.org/10.15760/etd.2944