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Asset identification using image descriptors

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Date Issued:
2012 -
Summary:
Asset management is a time consuming and error prone process. Information Technology (IT) personnel typically perform this task manually by visually inspecting assets to identify misplaced assets. If this process is automated and provided to IT personnel it would prove very useful in keeping track of assets in a server rack. A mobile based solution is proposed to automate this process. The asset management application on the tablet captures images of assets and searches an annotated database to identify the asset. We evaluate the matching performance and speed of asset matching using three different image feature descriptors. Methods to reduce feature extraction and matching complexity were developed. Performance and accuracy tradeoffs were studied, domain specific problems were identified, and optimizations for mobile platforms were made. The results show that the proposed methods reduce complexity of asset matching by 67% when compared to the matching process using unmodified image feature descriptors.
Title: Asset identification using image descriptors.
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Name(s): Friedel, Reena Ursula.
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Issued: 2012 -
Publisher: Florida Atlantic University
Physical Form: electronic
Extent: viii, 45 p. : ill. (some col.)
Language(s): English
Summary: Asset management is a time consuming and error prone process. Information Technology (IT) personnel typically perform this task manually by visually inspecting assets to identify misplaced assets. If this process is automated and provided to IT personnel it would prove very useful in keeping track of assets in a server rack. A mobile based solution is proposed to automate this process. The asset management application on the tablet captures images of assets and searches an annotated database to identify the asset. We evaluate the matching performance and speed of asset matching using three different image feature descriptors. Methods to reduce feature extraction and matching complexity were developed. Performance and accuracy tradeoffs were studied, domain specific problems were identified, and optimizations for mobile platforms were made. The results show that the proposed methods reduce complexity of asset matching by 67% when compared to the matching process using unmodified image feature descriptors.
Identifier: 794276903 (oclc), 3342051 (digitool), FADT3342051 (IID), fau:3854 (fedora)
Note(s): by Reena Ursula Friedel.
Thesis (M.S.C.S.)--Florida Atlantic University, 2012.
Includes bibliography.
Electronic reproduction. Boca Raton, Fla., 2012. Mode of access: World Wide Web.
Subject(s): Data mining -- Technological innovations
Mobile computing
User-centered system design
Application software -- Development
Persistent Link to This Record: http://purl.flvc.org/FAU/3342051
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU