The Representation of Visual Feature Variables in           Connectionist Networks (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}
This research provides a general theoretical framework for              the description and analysis of representations for variables in                connectionist networks.  The space in which all representations                 lie is defined, and existing representation schemes are described               in terms of this space.  In addition, the space is used to                      suggest new representations.  A mathematical analysis of the                    representations is used to compare different representations in                 terms of various properties.  This analysis has lead to theories                about visual processing, and has been used to determine which                   types of information are best encoded by a particular                           representation, and is thus useful for choosing an optimal                      encoding for a given computational task.                                                This research shows that in the early stages of visual                  processing the choice of representation for a visual feature                    variable depends not only on the type of information about the                  variable that is desired, but also on the measurement process                   used to extract the feature from an image.  A tuning-curve                      technique has been developed for analyzing the measurment                       process, and thus determining the constraints placed on the                     choice of representation that are a function of the measurement                 process.  The focus of this project is to study a special                       representation techniques, call Rho-space representation.  It has               been developed along with simple parallel connectionist                         computations for edge analysis.  Rho-space has many advantages                  over other edge representations including:  no thresholding of                  local edge elements is required; a natural representation of                    connectivity is created which agrees with human perception; both                coarse and fine representation of orientation information is                    possible; and, illusory contours, of the type produced by the                   human visual system occur.
{'FirstName': 'Deborah', 'LastName': 'Walters', 'PI_MID_INIT': 'K', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Deborah K Walters', 'EmailAddress': 'walters@cs.buffalo.edu', 'NSF_ID': '000244044', 'StartDate': '07/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'SUNY at Buffalo', 'CityName': 'AMHERST', 'ZipCode': '142282577', 'PhoneNumber': '7166452634', 'StreetAddress': '520 LEE ENTRANCE STE 211', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'New York', 'StateCode': 'NY', 'CONGRESSDISTRICT': '26', 'CONGRESS_DISTRICT_ORG': 'NY26', 'ORG_UEI_NUM': 'LMCJKRFW5R81', 'ORG_LGL_BUS_NAME': 'RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK, THE', 'ORG_PRNT_UEI_NUM': 'GMZUKXFDJMA9'}
{'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': '8705553.xml'}