Robust Random Field Models for Images with Long and Short   Term Dependencies
{'Code': '05020000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'IIS', 'LongName': 'Div Of Information & Intelligent Systems'}}
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The objectives of this project are: to develop a random-field                   model which can represent both long- and short-correlation                      structures of an image; to investigate robust estimators in the                 long- and short-memroy random-field models; and to develop                      segmentation and restoration algorithms using theoretical models                found in this project.                                                                                                                                          The long-memory model will be generalized by adding the short-                  memory structure of autoregressive moving-average (ARMA)-class                  models.  At least two approaches will be used: the input process                of the long-memory model will be represented by ARMA models; and                the fractional differencing term in the long-memory model will be               modified by a fractionally-differenced autoregressive process.                  Robust M-estimators will be derived for the estimation of                       parameters in generalized  long-memroy models; the objective                    function will be derived by metricaly Winsorizing either a                      likelihood function  or a least-squares criterion.  Issues                      involved with M-estimators, such as scale variance, efficiency at               assumed distribution, asymptotic properties, convergence, etc.,                 will be investigated.  Two-dimensional robust Kalman filters for                image restoration, and maximum-likelihood decision rules for                    segmentation, will be developed.                                                                                                                                The performance of model-based vision algorithms is directly                    related to the modeling power of the algorithm, and will be                     greatly aided by these long- and short-memory models.
{'FirstName': 'Kie-Bum', 'LastName': 'Eom', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Kie-Bum Eom', 'EmailAddress': 'eom@seas.gwu.edu', 'NSF_ID': '000373080', 'StartDate': '07/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Syracuse University', 'CityName': 'SYRACUSE', 'ZipCode': '13244', 'PhoneNumber': '3154432807', 'StreetAddress': '900 S CROUSE AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'New York', 'StateCode': 'NY', 'CONGRESSDISTRICT': '22', 'CONGRESS_DISTRICT_ORG': 'NY22', 'ORG_UEI_NUM': 'C4BXLBC11LC6', 'ORG_LGL_BUS_NAME': 'SYRACUSE UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '684000', 'Text': 'ROBOTICS'}
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