Segmentation of Noisy and Textured Images Modelled with     Gibbs Distributions
{'Code': '07010000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'ECCS', 'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}
{'SignBlockName': 'george hazelrigg', 'PO_EMAI': None, 'PO_PHON': None}
The goal of this research project is the development of stochastic         models and statistically optimal segmentation algorithms for noisy and          textured images.  Specific objectives include:  further development of          hierarchical Gibbs distribution or Markov random field models to                represent real images; development of models for speckle corrupted              synthetic aperture radar images; investigation of the relationships             between Markov-type models and connections between  Markov random field         models and robust processing; development of estimators for the                 parameters in hierarchical image models; and development of statistically       optimal segmentation algorithms for images modelled with hierarchical           Gibbs distribution models.  For parameter estimation and segmentation,          relaxation methods will be used.  The objective of this research is to          develop algorithms for segmenting real world images.  Results of this           research could be important in fields of synthetic aperture radar and           image interpretation.
{'FirstName': 'Haluk', 'LastName': 'Derin', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Haluk Derin', 'EmailAddress': None, 'NSF_ID': '000242212', 'StartDate': '08/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Massachusetts Amherst', 'CityName': 'AMHERST', 'ZipCode': '010039252', 'PhoneNumber': '4135450698', 'StreetAddress': '101 COMMONWEALTH AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Massachusetts', 'StateCode': 'MA', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_ORG': 'MA02', 'ORG_UEI_NUM': 'VGJHK59NMPK9', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF MASSACHUSETTS', 'ORG_PRNT_UEI_NUM': 'VGJHK59NMPK9'}
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{'Code': '1459', 'Text': 'COMMUNICATIONS SYSTEMS'}
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