A Bayesian Approach to Computer Vision
{'Code': '05020000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'IIS', 'LongName': 'Div Of Information & Intelligent Systems'}}
{'SignBlockName': 'Howard Moraff', 'PO_EMAI': None, 'PO_PHON': None}
This project would continue the development of a Bayesian                       approach to robot vision.  Its goal is the recognition, and                     position estimation, of 3-D indoor and outdoor objects.  The                    method will use as input the images taken by two or more cameras                in different positions, or on range data, or on other sensed                    data.  Textured-image analysis is also considered, both as a                    separate topic, and as it relates to the preceding.  The approach               has three components:                                                                   1.  Modeling of objects to the necessary detail, based on a             combination of geometric, algebraic, and probabilistic models.                          2.  Development, for each problem, of the joint likelihood              of the measured data, and the a priori unknown model parameters,                followed by estimation or recognition using new extensions of                   classical statistical concepts applied to this joint probability                function.  This permits optimal estimation of a priori unknown                  quantities and optimal recognition of objects of interest.                              3.  Development of new algorithms designed to run on                    parallel processors, motivated by the need to deal with the large               number of parameters involved in these methods.                                                                                                                 The significance of the work is:                                                        1.  It provides a unified framework for computer vision and             for handling measured data, a priori model information, and                     uncertainty.                                                                            2.  It provides a very good performance functional so that              robot vision can be treated as a maximization problem.                                  3.  It permits the derivation of performance bounds (e.g.,              on covariance matrices for parameter estimation errors), which                  help in understanding the performance limitations of the new                    system.  More importantly, it facilitates the optimization of                   intermediate stages.                                                                    4.  It provides a new set of interesting, potentially                   powerful algorithms and operators to be studied for parallel                    processing in computer vision.
{'FirstName': 'David', 'LastName': 'Cooper', 'PI_MID_INIT': 'B', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'David B Cooper', 'EmailAddress': 'cooper@lems.brown.edu', 'NSF_ID': '000139754', 'StartDate': '07/15/1988', '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': '6840', 'Text': 'ROBOTICS'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8715774.xml'}