Title :
Direct dynamic motion vision
Author_Institution :
Artificial Intelligence Lab., MIT, Cambridge, MA, USA
Abstract :
A method is presented for estimating the structure of a scene and the motion of an observer relative to the scene from a sequence of images. Two key features distinguish this method from previous solutions to this problem: no computation of optical flow is required, which leads to considerable speedup, and a Kalman filtering algorithm takes advantage of the entire sequence and leads to noise reduction. Dense depth is estimated by the Kalman filter and is obtained from motion by a least-squares method. No assumptions about surface structure or motion are made. Experimental results on real images are presented
Keywords :
Kalman filters; computer vision; least squares approximations; Kalman filtering; direct dynamic motion vision; least-squares; noise reduction; Filtering algorithms; Image motion analysis; Kalman filters; Layout; Motion estimation; Noise reduction; Optical computing; Optical filters; Optical noise; Surface structures;
Conference_Titel :
Robotics and Automation, 1990. Proceedings., 1990 IEEE International Conference on
Conference_Location :
Cincinnati, OH
Print_ISBN :
0-8186-9061-5
DOI :
10.1109/ROBOT.1990.126150