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 Determining Vision Graphs for Distributed Camera Networks Using Feature Digests | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 57034 11 pages doi 2007 57034 Research Article Determining Vision Graphs for Distributed Camera Networks Using Feature Digests Zhaolin Cheng 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 18 April 2006 Accepted 18 May 2006 Recommended by Deepa Kundur We propose a decentralized method for obtaining the vision graph for a distributed ad-hoc camera network in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. Each camera encodes a spatially well-distributed set of distinctive approximately viewpoint-invariant feature points into a fixed-length feature digest that is broadcast throughout the network. Each receiver camera robustly matches its own features with the decompressed digest and decides whether sufficient evidence exists to form a vision graph edge. We also show how a camera calibration algorithm that passes messages only along vision graph edges can recover accurate 3D structure and camera positions in a distributed manner. We analyze the performance of different message formation schemes and show that high detection rates can be achieved while maintaining low false alarm rates using a simulated 60-node outdoor camera network. Copyright 2007 Zhaolin Cheng et al. 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 The automatic calibration of a collection of cameras . estimating their position and orientation relative to each other and to their environment is a central problem in computer vision that requires techniques for both detecting matching feature points in the images .
