Knowledge-Guided Detection of Inner Ear Hair Cells in       Digitial Images (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}
The designer of an automated scene understanding system needs                to have a repertoire of methods for the effective representation                of knowledge and access to a variety of methods for image                       processing.  In addition, such a design process is helped by an                 understanding of methods for controlling search in the                          construction of solutions which combine uncertain evidence from                 multiple plausible interpretations.  One strategy for adding to                 the available methods for scene understaning is to build                        successive systems for interpretation in increasingly difficult                 domains.  This research, focuses on a domain that demands                       capabilities which exceed those of the current techniques.                           The project is aimed at developing a system for the                        automatic interpretation of tissue section images of hair cells                 of the organ of Corti in the inner ear.  This domain is complex                 enough to engender the development of new techniques and                        tractable enough for study.  Domain knowledge is available in the               form of expectations for location, orientation, and geometric                   relations of objects, and in the form of expert protocol for                    interpretation.  Because of the nature of the images,                           interpretation based only on local features is impractical,                     providing the opportunity to investigate methods for combining                  uncertain evidence from several sources under the guidance of                   domain knowledge.  Further, interpretation requires the use of                  images from several focal planes, giving us the opportunity to                  investigate symbolic feedback in a combination of both data-                    driven and knowledge-controlled interpretation methods.  Finally,               results from human interpretation of slides provide clear                       standards for evalutiion of the system.
{'FirstName': 'Terry', 'LastName': 'Weymouth', 'PI_MID_INIT': 'E', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Terry E Weymouth', 'EmailAddress': 'weymouth@si.umich.edu', 'NSF_ID': '000404466', 'StartDate': '12/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Regents of the University of Michigan - Ann Arbor', 'CityName': 'ANN ARBOR', 'ZipCode': '481091079', 'PhoneNumber': '7347636438', 'StreetAddress': '1109 GEDDES AVE, SUITE 3300', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Michigan', 'StateCode': 'MI', 'CONGRESSDISTRICT': '06', 'CONGRESS_DISTRICT_ORG': 'MI06', 'ORG_UEI_NUM': 'GNJ7BBP73WE9', 'ORG_LGL_BUS_NAME': 'REGENTS OF THE UNIVERSITY OF MICHIGAN', 'ORG_PRNT_UEI_NUM': None}
{'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': '8711957.xml'}