Recent developments in wireless sensor networks have made feasible distributed camera networks, in which cameras and processing nodes may be spread over a wide geographical area, with no centralized processor and limited ability to communicate a large amount of information over long distances. This paper overviews distributed algorithms for the calibration of such camera networks- that is, the automatic estimation of each camera's position, orientation, and focal length. In particular, we discuss a decentralized method for obtaining the vision graph for a distributed camera network, in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. We next describe a distributed algorithm in which each camera performs a local, robust nonlinear optimization over the camera parameters and scene points of its vision graph neighbors in order to obtain an initial calibration estimate. We then show how a distributed inference algorithm based on belief propagation can refine the initial estimate to be both accurate and globally consistent.

This paper comes up with a linear self-calibration approach for camera focal length estimation. Compared with other linear techniques, the advantage of this method is that no priori information of motion is needed. From the degenerated equations (one quadratic and two linear), the essential constraints are generated on calibration of camera intrinsic parameters. Thus the focal length of camera is conveniently obtained in closed form on the reliable assumption that only the focal length is unknown but constant, and the skew factor is zero. Synthetic object/image, real images of indoor and outdoor generic scenes and 3D model reconstruction are involved in our experiments to analyze the accuracy of the estimated focal length. The experimental results indicate that our proposed method is both robust and efficient.

In this paper, a linear self-calibration approach for camera focal length estimation is proposed. No priori information of camera motion is needed for the method. The unknown focal length of camera is conveniently obtained in closed form from degenerated Kruppa's equations. These degenerated equations (one quadratic and two linear) provide the essential constraints on calibration of camera intrinsic parameters. Real images of indoor and outdoor generic scenes and 3D metric reconstruction are used to analyze the accuracy of the estimated focal length. Experiments results show that the method is both robust and efficient.

In this paper, the optimal control for regulating linear singularly perturbed systems with perturbed quadratic-cost is presented by discussing the singular quadratic-cost control problem for the reduced-order slow subsystem. Under some assumptions, the optimal performance cost of the singularly perturbed system will tend to that of the reduced-order slow subsystem.