By Chrysi Papalazarou, Peter M. J. Rongen, Peter H. N. de With (auth.), Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)
This publication constitutes the refereed complaints of the eleventh overseas convention on complex ideas for clever imaginative and prescient platforms, ACIVS 2009, held in Bordeaux, France in September/October 2009.
The forty three revised complete papers and 25 posters awarded have been rigorously reviewed and chosen from a hundred and fifteen submissions. The papers are geared up in topical sections on technovision, primary mathematical suggestions, photograph processing, coding and filtering, photograph and video research, desktop imaginative and prescient, monitoring, colour, multispectral and special-purpose imaging, clinical imaging, and biometrics.
Read or Download Advanced Concepts for Intelligent Vision Systems: 11th International Conference, ACIVS 2009, Bordeaux, France, September 28–October 2, 2009. Proceedings PDF
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Extra resources for Advanced Concepts for Intelligent Vision Systems: 11th International Conference, ACIVS 2009, Bordeaux, France, September 28–October 2, 2009. Proceedings
1 Basic Models Frame differencing (FD) : also known as temporal difference, this method uses the previous frame as background model for the current frame. Setting a threshold, τ , Background Subtraction Techniques: Systematic Evaluation and Comparative Analysis 35 on the squared difference between model and frame decides on foreground and background. This threshold is the analyzed parameter. Median filtering (MF) : uses the median of the previous N frames as background model for the current frame.
For every new frame, a pixel is determined to belong to the background if it falls into a deviation, σ , around the mean. The parameters of each Gaussian are updated with the current frame pixel by using a running average scheme, controlled by a learning factor α . The initial deviation value, σ o , and α are the analyzed parameters. Mixture of Gaussians (MoG) : represents each background pixel variation with a set of weighted Gaussian distributions. Distributions are ordered according to its weight; the more relevant (until the accumulated weight gets past a threshold, τ ) are considered to model the background; the remaining model the foreground.
The purpose of LDA is to select the linear subspace Φ, which maximizes the ratio of the quotient : Γ (T ) = |T T Sb T | |T T Sw T | (3) The optimal projection matrix Φ which maximizes Γ (T ) can be obtained by solving a generalized eigenvalue problem Sb Φ = Sw ΦΛ that is equivalent to −1 solve Sw Sb Φ = ΛΦ. The maximization of Γ (T ) is possible if the Sw matrix is non singular. However, in the face recognition experiments, data are always under represented. Usually, the size of the face image vector is much greater than the number of samples in the learning database which leads to a Small Sample Size problem.