A Sequential Hypothesis Testing Approach to Detecting Small, Moving Objects in Image Sequences
Blostein, Steven David
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https://hdl.handle.net/2142/69407
Description
Title
A Sequential Hypothesis Testing Approach to Detecting Small, Moving Objects in Image Sequences
Author(s)
Blostein, Steven David
Issue Date
1988
Doctoral Committee Chair(s)
Huang, Thomas S.
Department of Study
Electrical Engineering
Discipline
Electrical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Mathematics
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.
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