UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Author: search.filters.author.Liu, XuSubject: search.filters.subject.Pattern Recognition

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    Computer Vision and Image Processing Techniques for Mobile Applications
    (2008-12-08) Liu, Xu; Davis, Larry; Doermann, David; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Camera phones have penetrated every corner of society and have become a focal point for communications. In our research we extend the traditional use of such devices to help bridge the gap between physical and digital worlds. Their combined image acquisition, processing, storage, and communication capabilities in a compact, portable device make them an ideal platform for embedding computer vision and image processing capabilities in the pursuit of new mobile applications. This dissertation is presented as a series of computer vision and image processing techniques together with their applications on the mobile device. We have developed a set of techniques for ego-motion estimation, enhancement, feature extraction, perspective correction, object detection, and document retrieval that serve as a basis for such applications. Our applications include a dynamic video barcode that can transfer significant amounts of information visually, a document retrieval system that can retrieve documents from low resolution snapshots, and a series of applications for the users with visual disabilities such as a currency reader. Solutions for mobile devices require a fundamentally different approach than traditional vision techniques that run on traditional computers, so we consider user-device interaction and the fact that these algorithms must execute in a resource constrained environment. For each problem we perform both theoretical and empirical analysis in an attempt to optimize performance and usability. The thesis makes contributions related to efficient implementation of image processing and computer vision techniques, analysis of information theory, feature extraction and analysis of low quality images, and device usability.