Segmentation of Images (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'}
Segmenting an image into distinct, meaningful regions is                thought to be largely a task in early visual processing,                        involving low-level processes:  local, numerical and bottom-up.                 Most segmenting algorithms can be broadly divided into categories               based on opposing strategies:  local, edge-operator methods and                 region-based, histogram methods.  A recent algorithm developed by               S. and D. Geman uses ideas from statistical mechanics to divide                 up an image into maximally homogeneous regions with shortest                    possible boundaries.  The approach used in this research is                     inspired by Geman's work and involves segmenting by minimizing                  energy functionals.  These functionals reflect properties that                  one would expect of any segmenting scheme.  Preliminary work                    shows that the Geman scheme can be thought of as a continuous                   spectrum of methods bridging edge-operator methods and region-                  based methods.  The research in this project will be focussed                   along three lines:  1) Theoretical investgation regarding the                   existence and smoothness of solutions, 2) Extension to multi-grid               relaxation and related questions of shape representation, and; 3)               Extension to segmentation based on image features other than or                 in addition to the gray level.
{'FirstName': 'Jayant', 'LastName': 'Shah', 'EmailAddress': 'shah@neu.edu', 'StartDate': '07/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Northeastern University', 'CityName': 'BOSTON', 'ZipCode': '021155005', 'PhoneNumber': '6173733004', 'StreetAddress': '360 HUNTINGTON AVE', 'CountryName': 'United States', 'StateName': 'Massachusetts', 'StateCode': 'MA'}
[{'Code': '1266', 'Text': 'APPLIED MATHEMATICS'}, {'Code': '6840', 'Text': 'ROBOTICS'}]
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8704467.xml'}