Learning and Recognition in Natural Environments
{'Code': '05020000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'IIS', 'LongName': 'Div Of Information & Intelligent Systems'}}
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During the last two years research in machine vision has                   used psychological evidence to develope a representation for                    shape that attempts to capture people's notions about the part                  structure of 3-D objects.  The mathematical basis for this                      representation is superquadric profile solids, linear and                       quadratic deformations, and contructive solid geometry (CSG).                   The concise nature of this representation has led to the                        development of a technique for approximating the minimal length                 encoding of range data in terms of this 3-D shape representation.               This technique has been used to experimentally demonstrated both                learning and recognition of 3-D objects in unconstrained outdoor                environments.                                                                        The current technique, however, is still quite experimental.               Further, it is inefficient on serial machines, has difficulties                 with largely occluded forms, and is limited to use with range                   data.  This project, therefore, will investigate the potential                  for improving the reliability, efficiency, and applicability of                 our general approach by (1) incorporating feature information                   such as edges, corners, and T-junctions, (2) employing a coarse-                to-fine search strategy, (3) extending the current technique to                 multiple views (e.g., stereo and motion), and (4) investigating                 how our technique might be adapted for use with standard                        intensity imagery.
{'FirstName': 'Alex', 'LastName': 'Pentland', 'PI_MID_INIT': 'P', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Alex P Pentland', 'EmailAddress': 'alexpentland@gmail.com', 'NSF_ID': '000328855', 'StartDate': '06/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Massachusetts Institute of Technology', 'CityName': 'CAMBRIDGE', 'ZipCode': '021394301', 'PhoneNumber': '6172531000', 'StreetAddress': '77 MASSACHUSETTS AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Massachusetts', 'StateCode': 'MA', 'CONGRESSDISTRICT': '07', 'CONGRESS_DISTRICT_ORG': 'MA07', 'ORG_UEI_NUM': 'E2NYLCDML6V1', 'ORG_LGL_BUS_NAME': 'MASSACHUSETTS INSTITUTE OF TECHNOLOGY', 'ORG_PRNT_UEI_NUM': 'E2NYLCDML6V1'}
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[{'Code': '6840', 'Text': 'ROBOTICS'}, {'Code': '9145', 'Text': 'SPECIAL PROGRAMS-RESERVE'}]
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