Efficiency of Human Vision
{'Code': '08090200', 'Directorate': {'Abbreviation': 'BIO', 'LongName': 'Direct For Biological Sciences'}, 'Division': {'Abbreviation': 'IOS', 'LongName': 'Division Of Integrative Organismal Systems'}}
{'SignBlockName': 'Christopher Platt', 'PO_EMAI': None, 'PO_PHON': None}
There is an inherent degree of uncertainty involved in making                   perceptual decisions.  This uncertainty may come from ambiguous                 causes of sensory input, or from random variation in the                        physical or biological processes involved in transmitting                       perceptual information.  Human observers are remarkably good at                 making valid and reliable visual decisions despite random                       variation in the image, compared to capabilities of most                        artificial vision systems.                                                                                                                                      This project addresses how human observers process ambiguous or                 "noisy" visual information.  Performance can be calculated for                  a theoretically "optimal" or "ideal" observer for a visual task                 that has inherent uncertainty.  The optimal performance assumes                 ideal performance from the optics and detector elements of the                  system, and the calculations are based on a branch of                           probability theory called statistical decision theory.  By                      measuring the visual performance of a human and comparing it to                 the ideal benchmark, we can measure human absolute visual                       efficiency.  Patterns which are detected nearly optimally may                   reflect specialized neural mechanisms for a particular task.                    Here the role of spatial arrangement, color, texture, and                       motion will be studied to find out what types of images are                     detected most efficiently.  The limits to optimal coding of                     more natural images will be quantified by measuring the                         predictability  of various elements in pictures.                                                                                                                This study is an unusual combination of experimental work with                  very sophisticated mathematical modelling, and will help                        integrate visual perception theory with both machine vision and                 human performance.  It should have substantial impact on vision                 research.
{'FirstName': 'Daniel', 'LastName': 'Kersten', 'PI_MID_INIT': 'J', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Daniel J Kersten', 'EmailAddress': 'kersten@umn.edu', 'NSF_ID': '000339540', 'StartDate': '07/15/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Brown University', 'CityName': 'PROVIDENCE', 'ZipCode': '029129100', 'PhoneNumber': '4018632777', 'StreetAddress': '1 PROSPECT ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Rhode Island', 'StateCode': 'RI', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_ORG': 'RI01', 'ORG_UEI_NUM': 'E3FDXZ6TBHW3', 'ORG_LGL_BUS_NAME': 'BROWN 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': '1185', 'Text': 'SENSORY SYSTEMS'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8708532.xml'}