In this paper,a binocular vision system and a method for determining 3-D motion parameters of an object from binocular sequence images are introduced.

A new method for 3D rigid motion estimation from binocular sequence image of computer system in intelligence vision supervises is proposed in this paper.

For this reason, an approach is proposed for 3D local non-rigid motion estimation from monocular image sequence using regularization.

Two oblique image sequences for buildings walls and one vertical image sequence for building s roof,acquired by digital video camera on helicopter,coarse 2D vector data of buildings and LIDAR data are used as sources of information.

Precision of relative orientation and forward intersection with high overlapping image sequence is analyzed.

This paper proposes to by using Harris operator extract the feature points of moving objects in sequence images, then combines the local information entropy with Walsh transformation to carry out the feature point matching in sequence image.

This paper studies the trifocal tensor and uses it to match the features of image sequences,then through experiment shows that this method can effectively improve the rate,the points and lines of correctly matching.

The paper proposes a method by which face 3D model is created from 2D image sequences directly but not by the common techniques using face general model.

3D reconstruction from uncalibrated image sequences is very important technology in computer vision and photogrammetry,the camera can be moved by hand and the motion need not be known,thus makes image data capture become easy and very convenient.