{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/69407"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/69407","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A Sequential Hypothesis Testing Approach to Detecting Small, Moving Objects in Image Sequences","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Blostein, Steven David"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Huang, Thomas S."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-12-15T19:05:38Z","date_published":"2014-12-15T19:05:38Z","updated_at":"2026-07-22T22:26:00Z","subjects":["Mathematics","Engineering, Aerospace","Engineering, Electronics and Electrical"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(UMI)AAI8908625"],"render_values":[{"text":"(UMI)AAI8908625","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/69407","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Huang, Thomas S."]},{"key":"dc:creator","label":"Author","values":["Blostein, Steven David"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-12-15T19:05:38Z","10000-01-01","1988"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Mathematics","Engineering, Aerospace","Engineering, Electronics and Electrical"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/69407","(UMI)AAI8908625"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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). 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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). 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