tailieunhanh - Báo cáo hóa học: " Research Article Better Flow Estimation from Color Images ¨ Hui Ji1 and Cornelia Fermuller2"

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 Better Flow Estimation from Color Images ¨ Hui Ji1 and Cornelia Fermuller2 | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 53912 9 pages doi 2007 53912 Research Article Better Flow Estimation from Color Images Hui Ji1 and Cornelia Fermuller2 1 Department of Mathematics National University of Singapore Singapore 117543 2 Computer Vision Laboratory Institute for Advanced Computer Studies University of Maryland College Park MD 20742-3275 USA Received 1 October 2006 Accepted 20 March 2007 Recommended by Nicola Mastronardi One of the difficulties in estimating optical flow is bias. Correcting the bias using the classical techniques is very difficult. The reason is that knowledge of the error statistics is required which usually cannot be obtained because of lack of data. In this paper we present an approach which utilizes color information. Color images do not provide more geometric information than monochromatic images to the estimation of optic flow. They do however contain additional statistical information. By utilizing the technique of instrumental variables bias from multiple noise sources can be robustly corrected without computing the parameters of the noise distribution. Experiments on synthesized and real data demonstrate the efficiency of the algorithm. Copyright 2007 H. Ji and C. Fermuller. 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 Optical flow estimation is a heavily studied problem in computer vision. It is well known that the problem is difficult because of the discontinuities in the scene. However even at the locations of smooth scene patches the flow cannot be estimated very accurately because of statistical difficulties. In this paper we consider gradient-based approaches to optical flow estimation. The estimation is based on the basic constraint of constant brightness at an image .