Solving Ill-Conditioned Problems in Computer Vision         (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'}}
{'SignBlockName': 'Joyce', 'PO_EMAI': None, 'PO_PHON': None}
Ill-conditioned problems arise frequently in computer vision            because the image information contains noise and ambiguities such               that the true identity of the scene is not uniquely specified.                  It is nonetheless possible for humans to infer the structure of                 objects from incomplete data, presumably through the use of                     assumptions and computations consistent with natural images.                    This project focuses on a method in numerical analysis for                      solving "inverse problems" when the degraded scene has undergone                a sequence of steps modeled by a known equation.  Reconstruction                can then be attempted by finding a solution that minimizes the                  deviation from that equation.  The method is exemplified by its                 application to the deblurring problem.  In this problem,                        deblurring is achieved by computing a succession of images, each                slightly deblurred from the previous, such that the complete set                satisfies the equations specifying the diffusion process of                     blurring.  Deblurring and more general problems are studied in                  this project, using the method of minimization of equation error                to inject the necessary assumptions to uniquely infer information               in visual data.
{'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': '07/15/1987', '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'}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
{'Code': '6840', 'Text': 'ROBOTICS'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8703335.xml'}