2026-09-19T20:22:39Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/69407 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Huang, Thomas S. Blostein, Steven David 2014-12-15T19:05:38Z 2014-12-15T19:05:38Z 10000-01-01 1988 1988 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. Made available in DSpace on 2014-12-15T19:05:38Z (GMT). No. of bitstreams: 1 8908625.pdf: 5952449 bytes, checksum: 9a2c8b8b93e623803c55f327fd423d08 (MD5) Previous issue date: 1988 Embargo set by: Seth Robbins for item 69573 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 134 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1988. http://hdl.handle.net/2142/69407 (UMI)AAI8908625 Mathematics Engineering, Aerospace Engineering, Electronics and Electrical A Sequential Hypothesis Testing Approach to Detecting Small, Moving Objects in Image Sequences text Electrical Engineering Electrical Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.