The Visual Hierarchy of Model-Based Recognition             (Computer and Information Science)
{'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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Flexible computer vision systems for robotics and automation            must be capable of recognizing three-dimensional objects in                     arbitrary positions and orientations from partial image data.                   Previous research by the author has led to the development of a                 system that can solve this task for a small library of possible                 objects.                                                                                This project will extend these successful methods to much               more general problems which will allow the system to work                       efficiently with large numbers of objects containing internal                   degrees of freedom.  Research will continue on developing new                   methods for perceptual organization, in which image features are                grouped into meaningful structures for matching to object models.                                                                                               Object models will incorporate variable parameters and                          articulations, and these internal parameters will be solved for                 simultaneously with the determination of viewpoint.  Objects will               be described as a hierarchy of models, in which components can be               recognized and used to infer the presence of higher-levels.                     Low-level segmentation and perceptual grouping will be extended                 to allow for the detection of arbitrary curves.  A major goal                   will be to develop an overall theory of recognition that can be                 applied to each level of the visual hierarchy.  Such a theory                   would describe each level of recognition in terms of fitting                    over-constrained data to partial models, in which the evaluation                of each level of recognition would be based on the use of similar               statistical operations.
{'FirstName': 'Robert', 'LastName': 'Hummel', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Robert Hummel', 'EmailAddress': 'hummel@cims.nyu.edu', 'PI_PHON': '2129983373', 'NSF_ID': '000228057', 'StartDate': '06/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'New York University', 'CityName': 'NEW YORK', 'ZipCode': '100121019', 'PhoneNumber': '2129982121', 'StreetAddress': '70 WASHINGTON SQ S', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'New York', 'StateCode': 'NY', 'CONGRESSDISTRICT': '10', 'CONGRESS_DISTRICT_ORG': 'NY10', 'ORG_UEI_NUM': 'NX9PXMKW5KW8', 'ORG_LGL_BUS_NAME': 'NEW YORK UNIVERSITY', 'ORG_PRNT_UEI_NUM': 'NX9PXMKW5KW8'}
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[{'Code': '6840', 'Text': 'ROBOTICS'}, {'Code': '6850', 'Text': 'DIGITAL SOCIETY&TECHNOLOGIES'}, {'Code': '9145', 'Text': 'SPECIAL PROGRAMS-RESERVE'}]
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