Kinematics and Structure of a 3-D Rigid Object from a       Sequence of Noise 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', 'PO_EMAI': None, 'PO_PHON': None}
The problem addressed by this project is the inference of 3-               D attributes of a moving object from a sequence of noisy images                 of the object.  The 3-D attributes of interest are the                          translational motion and position, to a global scale factor, the                rotational motion and position, and the object structure, again                 to a global scale factor.  This research, high image noise levels               are allowed, perhaps as much as  20% of the object image size.                  Such noise levels can occur even in high resolution imagery,                    whenever the image of the object is small relative to the sensor                field of view.                                                                       The approach taken in this proposal is to model the 3-D                    rigid body motion using the principles of kinematics.  The                      kinematic equations propagationg translation and rotation are                   written in the form of a state space model.  The noisy feature                  points or lines form the measurement model.  Given that point or                line correspondences over a long sequence of frames are available               (or established) our goal to develop recursive and batch                        techniques for the estimation of 3-D motion and structure                       parameters.  Performance measures such as the theoretically                     attainable lower bounds for the estimated parameters will be                    derived.  Extensions to the two camera problem will also be                     attempted and algorithms developed will be tested on both                       synthetic and real image sequences.
{'FirstName': 'Rama', 'LastName': 'Chellappa', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Rama Chellappa', 'EmailAddress': 'rchella4@jhu.edu', 'NSF_ID': '000294236', 'StartDate': '12/25/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Southern California', 'CityName': 'LOS ANGELES', 'ZipCode': '90033', 'PhoneNumber': '2137407762', 'StreetAddress': '3720 S FLOWER ST FL 3', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '34', 'CONGRESS_DISTRICT_ORG': 'CA34', 'ORG_UEI_NUM': 'G88KLJR3KYT5', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF SOUTHERN CALIFORNIA', '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': '684000', 'Text': 'ROBOTICS'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8713585.xml'}