A re-evaluation of mixture-of-Gaussian background modeling

© Copyright 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.The mixture of Gaussians (MOG) has been widely used for robustly modeling complicated backgrounds, especially those with small repetitive movements (such as leaves, bushes, rotating fan, ocean waves, rain). The performance of MOG can be greatly improved by tackling several practical issues. In this paper, we quantitatively evaluate (using the Wallflower benchmarks) the performance of the MOG with and without our modifications. The experimental results show that the MOG, with our modifications, can achieve much better results - even outperforming other state-of-the-art methods.Hanzi Wang and David Sute

Fast sparse gaussian processes learning for man-made structure classification

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Informative Vector Machine (IVM) is an efficient fast sparse Gaussian process's (GP) method previously suggested for active learning. It greatly reduces the computational cost of GP classification and makes the GP learning close to real time. We apply IVM for man-made structure classification (a two class problem). Our work includes the investigation of the performance of IVM with varied active data points as well as the effects of different choices of GP kernels. Satisfactory results have been obtained, showing that the approach keeps full GP classification performance and yet is significantly faster (by virtue if using a subset of the whole training data points).Hang Zhou, David Sute

Background Initialization with A New Robust Statistical Approach

© 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Initializing a background model requires robust statistical methods as the task should be robust against random occurrences of foreground objects, as well as against general image noise. The median has been employed for the problem of background initialization. However, the median has only a breakdown point of 50%. In this paper, we propose a new robust method which can tolerate more than 50% of noise and foreground pixels in the background initialization process. We compare our new method with five others and give quantitative evaluations on background initialization. Experiments show that the proposed method achieves very promising results in background initialization.Hanzi Wang and D. Sute

An ab initio and dynamics study of the photodissociation of nitric acid HNO3

We investigated the photodissociation of HNO3 within the first (300 nm) and the third (200 nm) absorption band. The relevant S1 and S3 potential energy surfaces were calculated by taking into account the N-O single bond and N=O “double” bond distances. The striking feature of the dynamical analysis is a bifurcation of the wave packet on the S3 surface which explains the branching into the two reaction pathways with the products OH+NO2 and O+HONO found in experiments. Dissociation on the S1 surface is predicted to proceed along a single channel leading to OH+NO2, both in their electronic ground states. Corresponding author