Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Calibrating Distributed Camera Networks Using Belief Propagation | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 60696 10 pages doi 2007 60696 Research Article Calibrating Distributed Camera Networks Using Belief Propagation Dhanya Devarajan and Richard J. Radke Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY 12180 USA Received 4 January 2006 Revised 10 May 2006 Accepted 22 June 2006 Recommended by Deepa Kundur We discuss how to obtain the accurate and globally consistent self-calibration of a distributed camera network in which camera nodes with no centralized processor may be spread over a wide geographical area. We present a distributed calibration algorithm based on belief propagation in which each camera node communicates only with its neighbors that image a sufficient number of scene points. The natural geometry of the system and the formulation of the estimation problem give rise to statistical dependencies that can be efficiently leveraged in a probabilistic framework. The camera calibration problem poses several challenges to information fusion including overdetermined parameterizations and nonaligned coordinate systems. We suggest practical approaches to overcome these difficulties and demonstrate the accurate and consistent performance of the algorithm using a simulated 30-node camera network with varying levels of noise in the correspondences used for calibration as well as an experiment with 15 real images. Copyright 2007 D. Devarajan and R. J. Radke. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION Camera calibration up to a metric frame based on a set of images acquired from multiple cameras is a central issue in computer vision. While this problem has been extensively studied most prior work assumes that the calibration .
