Sensor Motion and Relative Depth From Difference Fields of Optic Flows
Proceedings of the Eighth International Joint Conference on Artificial Intelligence. Vision. Pages 1027-1030.
Abstract
This paper develops a simple and robust procedure for recovering sensor motion parameters from image sequences induced by unconstrained sensor motion relative to a stationary environment. Difference vectors of optic flows approximate the orientations of the translational field lines in image areas in which there is depth variance between the corresponding environmental points and sufficient angular separation from the translational axis. This is developed into a procedure consisting of four steps: 1) locally computing difference vectors from an optic flow field; 2) thresholding the difference vectors; 3) minimizing the angles between the difference vector field and a set of radial field lines which correspond to a particular translational axis; and 4) extracting the translational and rotational component fields given the translational axis. This procedure does not require a priori knowledge_ about sensor motion or structure of the scene. It depends critically on sufficient variation in depth along some visual directions to endow the flow field with discontinuities. We present results of applying the procedure to sparse and low resolution displacement fields.
@inproceedings{ijcai1983p237,
title = {Sensor Motion and Relative Depth From Difference Fields of Optic Flows},
author = {Rieger, Joachim H. and Lawton, Daryl T.},
booktitle = {Proceedings of the Eighth International Joint Conference on
Artificial Intelligence, {IJCAI-83}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Alan Bundy},
pages = {1027--1030},
year = {1983},
note = {Vision},
url = {https://ijcai.org/proceedings/1983/237},
}
TY - CPAPER TI - Sensor Motion and Relative Depth From Difference Fields of Optic Flows AU - Rieger, Joachim H. AU - Lawton, Daryl T. T2 - Proceedings of the Eighth International Joint Conference on Artificial Intelligence, IJCAI-83 PB - International Joint Conferences on Artificial Intelligence PY - 1983 SP - 1027 EP - 1030 UR - https://ijcai.org/proceedings/1983/237 ER -Copied
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