The article investigates high-level general invertible-sequential processing in the digital and quantum domains. In particular it is shown that (i) invertible digital-sequential processes, constructed using a standard general-inversion procedure, and (ii) sequential quantum processes, determine Coxeter groups. In each case the groups are seen to define all processes that may be constructed from the, given, elemental processes of the sequences. Explicit forms of the presentations of the Coxeter groups are given for all cases. The quantum processes are seen to define unitary representations of the associated Coxeter groups in tensor-product qubit spaces.

Inflammatory muscle disease is a group of rare idiopathic conditions that cause progressive skeletal muscle weakness. Diagnosis typically requires an assortment of clinical tests, including magnetic resonance imaging (MRI) of the thigh muscles to assess fat infiltration and inflammation. We hypothesise that features from multi-spectral MRI can accurately predict patient diagnosis, without the need for additional tests. A novel method is presented using computer-extracted features of the T1-STIR bivariate histogram to detect disease. The dataset comprised of 78 image-pairs from 8 patients with inflammatory muscle disease symptoms and 61 image-pairs from 9 control cases with no disease. T1 and STIR slices were co-registered and the background discarded. A feature vector was designed to measure the distribution of standardised intensity values (e.g. standard deviation, kurtosis, gini) for the muscle, fat, and leg regions of the bivariate histogram. Feature dimensionality was reduced using a combined leave-one-out and k-folds cross-validation method to select the most important features. A Bayes network was trained to predicted patient diagnosis on a per-slice basis, 10-fold cross-validated. The system attained 92% sensitivity and 82% specificity (ROC area 0.93). These results support the hypothesis that accurate diagnosis of inflammatory muscle disease is possible using MRI alone, without the need for additional clinical tests, with the potential benefit of faster diagnosis and better care for patients with this group of rare conditions.

The present generation of Ambient Light Sensors (ALS) of a mobile handheld device suffer from two practical shortcomings. The ALSs are narrow angle, i.e. they respond effectively only within a narrow angle of operation and there is a latency of operation. As a result mobile applications that operate based on the ALS readings could perform sub-optimally especially when operated in environments with non-uniform illumination. The applications will either adopt with unacceptable levels of latency or/and may demonstrate a discrete nature of operation. In this paper we propose a framework to predict the ambient illumination of an environment in which a mobile device is present. The predictions are based on an illumination model that is developed based on a small number of readings taken during an application calibration stage. We use a machine learning based approach in developing the models. Five different regression models were developed, implemented and compared based on Polynomial, Gaussian, Sum of Sine, Fourier and Smoothing Spline functions. Approaches to remove noisy data, missing values and outliers were used prior to the modelling stage to remove their negative effects on modelling. The prediction accuracy for all models were found to be above 0.99 when measured using R-Squared test with the best performance being from Smoothing Spline. In this paper we will discuss mathematical complexity of each model and investigate how to make compromises in finding the best model.

We analyse the mask associated with the 2n-point interpolatory Dubuc-Deslauriers subdivision scheme S-a[n]. Sharp bounds are presented for the magnitude of the coefficients a(2i-1)([n]) of the mask. For scales i is an element of [1, root n] it is shown that vertical bar a(2i-1)([n])vertical bar is comparable to i(-1), and for larger power scales, exponentially decaying bounds are obtained. Using our bounds, we may precisely analyse the summability of the mask as a function of n by identifying which coefficients of the mask contribute to the essential behaviour in n, recovering and refining the recent result of Deng-Hormann Zhang that the operator norm of S-a[n] on l(infinity) grows logarithmically in n.

New minimal bounds are derived for the magnitudes of the derivatives of the rational Bezier paths and the rational rectangular Bezier surface patches of arbitrary degree, which improve previous work of this type in many cases. Moreover, our new bounds are explicitly given by simple and closed-form expressions. An important advantage of the closed-form expressions is that they allow us to prove that our bounds are sharp under certain well-defined conditions. Some numerical examples, highlighting the potential of the new bounds in providing improved estimates, are given in an appendix. (C) 2013 Elsevier Inc. All rights reserved.

Cast shadows need careful consideration in the development of robust dynamic scene analysis systems. Cast shadow detection is critical for accurate object detection in video streams, and their misclassification can cause errors in segmentation and tracking. Many algorithms for shadow detection have been proposed in the literature; however a complete, comparative evaluation of existing approaches is lacking. This paper presents a comprehensive survey of shadow detection methods, organised in a novel taxonomy based on object/environment dependency and implementation domain. In addition a comparative evaluation of representative algorithms, based on quantitative and qualitative metrics is presented to evaluate the algorithms on a benchmark suite of indoor and outdoor video sequences.

New derivative bounds for the rational quadratic Bézier paths are obtained, both for particular weight vectors and for classes of equivalent parametrisations. A comprehensive analysis of our bounds against existing bounds is made.

The advancement of technology allows video acquisition devices to have a better performance, thereby increasing the number of applications that can effectively utilize digital video. Compared to still images, video sequences provide more information about how objects and scenarios change over time. Tracking humans is of interest for a variety of applications including surveillance, activity monitoring and gate analysis. Many efficient object tracking algorithms have been proposed in literature, however part of those algorithms are semi-automatic requiring human interference. As for the fully automated algorithms, most of them are not applicable to real-time applications. This paper presents a low cost automatic object tracking algorithm suitable for use in real-time video based systems. The novelty of the proposed system is that it uses a simplified version of the Kanade-Lucas-Tomasi (KLT) technique to detect features of both continuous and discontinuous nature. As discontinuous feature selection is subject to noise, and would result in non-optimal feature based object tracking, the authors propose the use of a Kalman filter for the purpose of seeking optimal estimates in tracking. The integrated tracking system is capable of handling shadows and is based on a dynamic background subtraction strategy that minimises errors and quickly adapts to scene changes. Experimental results are provided to demonstrate the system's capability of accurately tracking objects in real-time applications where scenes are subject to noise particularly resulting from occlusions and sudden illumination variations.