Estimation and Signal Processing for Spatial Data:          Efficient Algorithms, Inverse Problems, and Computational   Vision and Geometry
{'Code': '07010000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'ECCS', 'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}
{'SignBlockName': 'Radhakisan Baheti'}
This research focuses on estimation and signal processing for           spatial data.  Three major areas are considered.  The first deals with          efficient algorithms for the processing of spatial data.  Problems to           be addressed include (i) the employment of novel notions of recursion           as well as spatial decompositions to develop recursive and highly               parallel algorithms for the estimation of 2-D processes described by            noncausal models and for other 2-D digital filtering applications; and          (ii) the exploitation of the symmetries present in some classes of              random fields (e.g. isotropic fields) to develop efficient procedures           for recursive and spectral estimation.  The second deals with the               development of algorithms for inverse and signal reconstruction                 problems in several dimensions.  Among the problems to be investigated          are (i) the development of efficient and generalized-tomographic                methods for exact and approximate solution of 2-D and 3-D inverse               scattering problems; and (ii) the development of system-                        identification-based approaches to inverse problems with particular             emphasis on algorithms that work at multiple spatial scales.  The               third area deals with computational vision and geometry.  A number of           research problems are described including (i) the development of                estimation-based algorithms for problems of computational vision such           as motion and depth estimation; and (ii) the investigation of system            and estimation-theoretic formulations of problems in computational              geometry such as the reconstruction of objects given uncertain                  measurements of various quantities such as the support of a convex              object, its silhouette, its interior, etc.                                              This research focuses on estimation and signal processing for           spatial data.  The three major areas considered are (i) efficient               algorithms for the processing of spatial data (ii) the development of           algorithms for inverse and signal reconstruction problems in two and            three dimensions and (iii) questions related to computational vision            and geometry.
[{'FirstName': 'Bernard', 'LastName': 'Levy', 'EmailAddress': 'levy@ece.ucdavis.edu', 'StartDate': '08/01/1987', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Alan', 'LastName': 'Willsky', 'EmailAddress': 'willsky@mit.edu', 'StartDate': '08/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}]
{'Name': 'Massachusetts Institute of Technology', 'CityName': 'Cambridge', 'ZipCode': '021394301', 'PhoneNumber': '6172531000', 'StreetAddress': '77 MASSACHUSETTS AVE', 'CountryName': 'United States', 'StateName': 'Massachusetts', 'StateCode': 'MA'}
[{'Code': '1454', 'Text': 'SYSTEMS THEORY'}, {'Code': '1459', 'Text': 'COMMUNICATIONS SYSTEMS'}, {'Code': '1518', 'Text': 'CONTROL, NETWORKS, & COMP INTE'}, {'Code': '4720', 'Text': 'SIGNAL PROCESSING SYS PROGRAM'}, {'Code': '6840', 'Text': 'ROBOTICS'}]
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8700903.xml'}