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Permanent link (DOI): https://doi.org/10.7939/R3CN6Z74R

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Human-Based Computation for Microfossil Identification Open Access

Descriptions

Other title
Subject/Keyword
microfossil identification
human-based computations
crowdsourcing
foraminifera
image understanding
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Wong, Cindy Ming
Supervisor and department
Joseph, Dileepan (Electrical and Computer Engineering)
Examining committee member and department
Miller, James (Electrical and Computer Engineering)
Yang, Herb (Computing Science)
Joseph, Dileepan (Electrical and Computer Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Signal & Image Processing
Date accepted
2012-06-08T13:51:21Z
Graduation date
2011-11
Degree
Master of Science
Degree level
Master's
Abstract
Image understanding is a general challenge in Artificial Intelligence (AI) because of its complexity. It is considered an AI-complete problem. We focus on the specific, important, and difficult case of microfossil identification, which is currently done manually. Microfossil identification has applications in paleoenvironmental research and oil exploration. We use evolutionary prototyping to engineer a complex system that employs crowdsourcing, mainly human-based computation. Our latest prototype, called the Microfossil Quest, combines human intelligence, including expert and citizen science, with computer intelligence, including unsupervised and supervised learning. A front-end website was developed to accommodate human interaction. It integrates easy-to-use interfaces for search and identification, detailed and interactive digital representations, and information for educational and motivational purposes. Computer intelligence is used in the back-end to synthesize and leverage human intelligence. To ensure a high quantity of high quality identifications are obtained quickly, the dynamic hierarchical identification algorithm was created to cluster specimens, propagate knowledge, and prioritize input. In this fashion, we provide not only a clear and strong approach to the specific problem of microfossil identification but also a significant case study for image understanding in general.
Language
English
DOI
doi:10.7939/R3CN6Z74R
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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