Neural Network Architectures for the Direct Perception      of 3D Motion and Depth
{'Code': '05020000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'IIS', 'LongName': 'Div Of Information & Intelligent Systems'}}
{'SignBlockName': 'Howard Moraff', 'PO_EMAI': None, 'PO_PHON': None}
A massively parallel approach to the tracking of rigid motions in               image sequences can be based on a Lie-algebra formalism.  This                  research will extend the theoretical basis of such neural-network               visual analysis, with experimental verification via a Connection                Machine simulation.  The key insight is that instantaneous                      tracking of variations in Gibson's "flow of the optic array" can                be achieved by local wiring of Hoffman orbits derived from                      a canonical Lie decomposition.  This is similar to repeated                     application of a Hough transform, but will here be combined with                a new technique for exploiting "momentum" of the visual flow.                   Inspiration for this work comes from Pitts and McCulloch's theory               on the extraction of universals from transformation groups.  The                goal is to track objects simultaneously in position and depth,                  without first segmenting them from the optic field.
{'FirstName': 'Laveen', 'LastName': 'Kanal', 'PI_MID_INIT': 'N', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Laveen N Kanal', 'EmailAddress': 'kanal@cs.umd.edu', 'NSF_ID': '000429938', 'StartDate': '08/18/1993', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Maryland, College Park', 'CityName': 'COLLEGE PARK', 'ZipCode': '207425100', 'PhoneNumber': '3014056269', 'StreetAddress': '3112 LEE BUILDING', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Maryland', 'StateCode': 'MD', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'MD04', 'ORG_UEI_NUM': 'NPU8ULVAAS23', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF MARYLAND, COLLEGE PARK', 'ORG_PRNT_UEI_NUM': 'NPU8ULVAAS23'}
{'Name': 'University of Maryland, College Park', 'CityName': 'COLLEGE PARK', 'StateCode': 'MD', 'ZipCode': '207425100', 'StreetAddress': '3112 LEE BUILDING', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Maryland', 'CountryFlag': '1', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_PERF': 'MD04'}
{'Code': '6840', 'Text': 'ROBOTICS'}
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