Estimation of Object Motion Parameters from Noisy Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Qualitative target motion detection and tracking
Proceedings of a workshop on Image understanding workshop
International Journal of Computer Vision - Special issue on image-based servoing
Motion Parameter Estimation from Global Flow Field Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic-Scene and Motion Analysis Using Passive Sensors - Part 1: A Qualitative Approach
IEEE Expert: Intelligent Systems and Their Applications
Egomotion Estimation Using Quadruples of Collinear Image Points
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Outlier rejection for cameras on intelligent vehicles
Pattern Recognition Letters
Automatic free parking space detection by using motion stereo-based 3D reconstruction
Machine Vision and Applications
Egomotion estimation as an appearance-based classification problem
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
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The computation of sensor motion from sets of displacement vectors obtained from consecutive pairs of images is discussed. The problem is investigated with emphasis on its application to autonomous robots and land vehicles. The effects of 3D camera rotation and translation upon the observed image are discussed, particularly the concept of the focus of expansion (FOE). It is shown that locating the FOE precisely is difficult when displacement vectors are corrupted by noise and errors. A more robust performance can be achieved by computing a 2D region of possible FOE locations (termed the fuzzy FOE) instead of looking for a single-point FOE. The shape of this FOE region is an explicit indicator of the accuracy of the result. It has been shown elsewhere that given the fuzzy FOE, a number of powerful inferences about the 3D sense structure and motion become possible. Aspects of computing the fuzzy FOE are emphasized, and the performance of a particular algorithm on real motion sequences taken from a moving autonomous land vehicle is shown.