Recently, many application systems have been developed by using a large number of cameras. If 3D points are observed from synchronized cameras, the multiple view geometry of these cameras can be computed and the 3D reconstruction of the scene is available. Thus, the synchronization of multiple cameras is essential. In this paper, we propose a method for synchronizing multiple cameras and for computing the epipolar geometry from uncalibrated and unsynchronized cameras. In particular we using affine invariance to match the frame numbers of camera images for finding the synchronization. The proposed method is tested by using real image sequences taken from uncalibrated and unsynchronized cameras.

Image rectification is a method of aligning epipolar lines of image pairs taken from widely variant viewpoints. Using the rectified images, we can easily obtain corresponding points. This paper presents a rectification method for object segmentation. Using the rectified image pairs obtained with the proposed method, we are able to find the reliable disparity and estimate the 3D depth of the pixel that is effective in the object segmentation.

This paper has two parts. In the first part of the paper, we note the property that under the para perspective camera projection model of a camera, the set of 2D images produced by a 3D point can be optimally represented by two lines in the affine space (α-β space). The slope of these two lines are same, and we observe that this constraint is exactly the same as the epipolar line constraint. Using this constraint, the equation of the epipolar line can be derived. In the second part of the paper, we use the "same slope" property of the lines in the α-β space to derive the affine structure of the human face. The input to the algorithm is not limited to an image sequence of a human head under rigid motion. It can be snapshots of the human face taken by the same or different cameras, over different periods of time. Since the depth variation of the human face is not very large, we use the para perspective camera projection model. Using this property, we reformulate the (human) face structure reconstruction problem in terms of the much familiar multiple baseline stereo matching problem. Apart from the face modeling aspect, we also show how we use the results for reprojecting human faces in identification tasks.

The reconstruction of motion and structure from multiple images is fundamental and important problem in computer vision. This paper highlights the recovery of the camera motion and the object shape under some camera projection model from feature correspondences especially the epipolar geometry and the factorization method for mainly used projection models.