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Title:A Sequential Hypothesis Testing Approach to Detecting Small, Moving Objects in Image Sequences
Author(s):Blostein, Steven David
Doctoral Committee Chair(s):Huang, Thomas S.
Department / Program:Electrical Engineering
Discipline:Electrical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Engineering, Aerospace
Engineering, Electronics and Electrical
Abstract:A new algorithm is proposed for the detection of small, barely discernible moving objects of unknown position and velocity in a sequence of digital images. First, statistically robust prewhitening techniques are used to eliminate background structure and transform the image sequence into an innovations representation, modeled as Gaussian white noise. Then, a large number of candidate trajectories, organized into a tree structure, are hypothesized at each pixel in the sequence and tested sequentially for a shift in mean intensity. Underlying the algorithm are new general results in detection theory, including the use of multistage hypothesis testing (MHT) for simultaneous inference, and a new framework for quickest detection of time-varying signals in noise. In addition, exact, closed-form expressions for MHT test performance are derived; these predict the MHT Object Detection Algorithm's computation and memory requirements, where it is shown theoretically that several orders of magnitude of processing are saved over a brute-force approach. Feasibility of a parallel implementation on an MIMD, distributed memory, message-passing architecture is also shown. Results are verified experimentally on a variety of image sequences, including outdoor scenes digitized from videotape, digitized photographs, and digital data gathered by a CCD array at the output of a telescope.
Issue Date:1988
Description:134 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1988.
Other Identifier(s):(UMI)AAI8908625
Date Available in IDEALS:2014-12-15
Date Deposited:1988

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