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In this paper we focus on Maximum Likelihood Estimation (MLE) technique for classification on Grassmann manifolds using matrix variate Bingham density function. Unlike the conventional techniques for multivariate distributions in the existing literature e.g., Markov chain Monte Carlo (MCMC) sampling methods, non-parametric methods, Expectation Maximisation (EM) iterative methods or exact methods,...
Matrix manifolds such as Stiefel and Grassmann manifolds have been widely used in modern computer vision. This paper is concerned with the problem of classifying such manifold-valued data, based on the maximum likelihood estimation for the parametric probability density functions defined on the manifolds. By using a new way of computing normalisation constants for the matrix Langevin distribution...
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging techniques are used to image the inner portion of human body for medical diagnosis. In this research work, retinal colour fundus images and MRI brain images noise level has been improved. Fundus Fluorescein Angiography (FFA) is the invasive based technique used to give high...
It has been demonstrated that a finite mixture model (FMM) with Gaussian distribution is a powerful tool in modeling probability density function of image data, with wide applications in computer vision and image analysis. We propose a simple-yet-effective way to enhance robustness of finite mixture models (FMM) by incorporating local spatial constraints. It is natural to make an assumption that the...
In order to separate the chromatogram peaks and spectra from the High Performance Liquid Chromatography with Diode Array Detector (HPLC-DAD) data set, a separation model of Generalized Reference Curve Measurement and its solution by multitarget Bare Bones Particle Swarm Optimization (GRCMmBBPSO) is proposed in this paper. Firstly, parameters are constructed which will generate Reference Curves (RCs)...
Asset prices fluctuate up and down chaotically. Traders, investors and fund managers comb the chaos for exploitable patterns with methods such as moving averages from the realm of technical analysis. In this paper we focus on linear moving averages which aim to smooth asset prices removing fluctuations. First, we will develop a method to measure the smoothness for a linear filter. We will also discuss...
In order to separate a 3D chromatography, which is generated from High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD), into chromatograms and spectra, we proposed a model called parallel Independent Component Analysis constrained by Reference Curve (pICARC), which transforms the separation problem to a multi-parameter optimization issue. Then, A new algorithm named multi-areas Genetic...
We propose a new sparse model construction method aimed at maximizing a model's generalisation capability for a large class of linear-in-the-parameters models. The coordinate descent optimization algorithm is employed with a modified l1- penalized least squares cost function in order to estimate a single parameter and its regularization parameter simultaneously based on the leave one out mean square...
Image mating is the process of isolating the foreground in images and video. This task is challenging as it is severely under constrained. At each pixel we must estimate the foreground and background colour and the blending between them (alpha value). Most approaches calculate an affinity matrix and then minimise a system of linear equations to find the alpha matte. In this work we propose an extension...
The Markov model has been applied to many prediction applications including the student models of intelligent tutoring systems. In this paper, we extend this well-known model to the weighted Markov model, and then apply it to student models in order to predict student behaviors. The prediction using our models is based not only on the frequency of collective behaviors of previous users, but also on...
In many machine vision applications, a set of static and Pan-Tilt-Zoom (PTZ) cameras are used to capture a sequence of high-resolution facial images of a moving person. In this paper, we present our implementation of such a system. We emphasis two novelties in our work; the first one is our efficient PTZ camera calibration technique using hand-drawn gridlines. The second one is our head position estimation...
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