The Development of Visual Search Models as a Basis for      Predicting Visual Inspection Time and Accuracy
{'Code': '07030000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'CMMI', 'LongName': 'Div Of Civil, Mechanical, & Manufact Inn'}}
{'SignBlockName': 'K. (Cheena) Srinivasan', 'PO_EMAI': None, 'PO_PHON': None}
This Engineering Initiation Award research project will develop            an improved methodology for determining allowed time in human visual            inspection operations to provide industry with a tool for making                more accurate assessments of product costs.  Rather than rely on                extensive worker observation, empirically-derived visual search                 models will serve as a basis for predicting inspection time, taking             into account task characteristics such as item complexity, density,             contrast, and area.  After an initial review of visual search models            and predetermined time systems, the models will be combined with new            models obtained from experimentation, to serve as a basis for                   defining time standards.  Resultant models will be validated with a             circuit board inspection task, under a wide variety of task                     conditions.                                                                          The proposed research should have a significant impact on both             the modeling of human visual detection and on industrial job design             and evaluation methodology.  Despite the ongoing development of                 automated inspection systems, much industrial visual inspection is              expected to be carried out by humans in the foreseeable future due              to performance and cost limitations; therefore, this research should            contribute to industrial improvements in this regard.
{'FirstName': 'Joseph', 'LastName': 'Goldberg', 'PI_MID_INIT': 'H', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Joseph H Goldberg', 'EmailAddress': 'jhg2@psu.edu', 'NSF_ID': '000321183', 'StartDate': '09/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Pennsylvania State Univ University Park', 'CityName': 'UNIVERSITY PARK', 'ZipCode': '168021503', 'PhoneNumber': '8148651372', 'StreetAddress': '201 OLD MAIN', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'StateCode': 'PA', 'CONGRESSDISTRICT': '15', 'CONGRESS_DISTRICT_ORG': 'PA15', 'ORG_UEI_NUM': 'NPM2J7MSCF61', 'ORG_LGL_BUS_NAME': 'THE PENNSYLVANIA STATE UNIVERSITY', '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': '1468', 'Text': 'Manufacturing Machines & Equip'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8707650.xml'}