The author previously introduced a direct method, called fixation, for the recovery of shape and motion in the general case that uses neither feature correspondence nor optical flow. Instead, it directly uses the spatio-temporal gradients of image brightness. The experimental results of applying some of the author's fixation algorithms to a sequence of real images, where the motion is a combination of translation and rotation, are reported. Techniques for autonomous choice of parameters that result in good estimates for important motion parameters are described.>

Earlier, the author (1991) introduced a direct method called fixation for the recovery of shape and motion in the general case. The method uses neither feature correspondence nor optical flow. Instead, it directly employs the spatio-temporal gradients of images brightnesses. The present paper reports the experimental results of applying some of the fixation algorithms to a sequence of real images where the motion is a combination of translation and rotation. Techniques for autonomous choice of parameters which result in good estimates for important motion parameters are also described.>

Earlier, we introduced a direct method called fixation for the recovery of shape and motion in the general case. The method uses neither feature correspondence nor optical pow. Instead, it directly employs the spatio-temporal gradients of image brightnesses. Xhis work reports the ezperimental results of applying some of our fization algorithms on a sequence oaf real images where the motion is a combination of translation and rotation. These results show that parameters such as the fixation patch size have crucial eflects on the e.stimation of some motion parameters. Some of the critical issues involved in the implementation of our autonomous motion vision system aw also discussed here. Among those are the criteria for automatic choice of an optimum size for the fization patch, and an appropriate location for the fixation point which result in good estimates for important motion parameters.

A direct method called fixation is introduced for solving the general motion vision problem: arbitrary motion relative to an arbitrary environment. This method results in a linear constraint equation that explicitly expresses the rotational velocity in terms of the translational velocity. The combination of this constraint equation with the brightness-change constraint equation solves the general motion vision problem. Avoiding correspondence and optical flow has been the motivation behind this direct method, which uses the image brightness information such as temporal and spatial brightness gradients directly. In contrast with previous direct methods, the fixation method does not put any severe restrictions on the motion or the environment. Moreover, the fixation method neither requires tracked images as its input nor uses tracking for obtaining fixated images. Instead, it introduces a pixel shifting process to construct fixated images for any arbitrary fixation point. This is done entirely in software without any use of camera motion for tracking.>

A direct method called fixation is introduced for solving the general motion vision problem, arbitrary motion relative to an arbitrary rigid scene. This method results in a linear constraint equation which explicitly expresses rotational velocity in terms of translational velocity. By combining this constraint equation with the brightness-change constraint equation, general solutions are found for translational and rotational velocities, and the shape. Avoiding correspondence and optical flow has been the motivation behind this direct method, which uses the brightness gradients directly. Partial implementation of the fixation method on real images has shown encouraging results which support some of the presented algorithms.>

A direct method, called fixation, for solving the general motion vision problem is introduced. This method results in a constraint equation between translational and rotational velocities that in combination with the brightness-change constraint equation solves the general motion vision problem, arbitrary motion relative to an arbitrary rigid scene. Avoiding correspondence and optical flow has been the motivation behind the direct methods. Recently, direct motion vision methods have used the brightness-change constraint equation for solving special cases such as known depth, pure translation or known rotation, pure rotation and planar world. In contrast to those solutions, the fixation method does not put such severe restrictions on the motion or the environment. Partial implementation of the fixation method shows encouraging results.>

A direct method called fixation is introduced for solving the general motion vision problem. This method results in a constraint equation between translational and rotational velocities that in combination with the brightness-change constraint equation solves the general motion vision problem, arbitrary motion relative to an arbitrary rigid scene. Avoiding correspondence and optical flow has been the motivation behind the direct methods. Recently, direct motion vision methods have used the brightness-change constraint equation for solving special cases such as known depth, pure translation or know rotation, pure rotation, and planar world. In contrast to those solutions, the fixation method does not push such severe restrictions on the motion or the environment.>