Integration of feature distributions for colour texture segmentation

This paper proposes a new framework for colour texture segmentation and determines the contribution of colour and texture. The distributions of colour and texture features provides the discrimination between different colour textured regions in an image. The proposed method was tested using different mosaic and natural images. From the results, it is evident that the incorporation of colour information enhanced the colour texture segmentation and the developed framework is effective

Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page

VALUE OF INCREASING KERNEL UNIFORMITY

Kernel size uniformity is an important physical quality attribute in terms of processing efficiency, quality control, and milling yield. This study developed optimal grain sorting strategies for elevators to use to increase kernel size uniformity and determined the size of potential benefits from sorting. Cluster analysis and global optimization were used to sort grain loads to increase kernel size uniformity. Cluster analysis and global optimization increased the percent flour yield relative to no sorting by 0.13% and 0.32% respectively. Cluster analysis and global optimization increased the daily milling income relative to no sorting by 105 dollars (5%) and 266 dollars (13%) respectively.Crop Production/Industries,

From patterned response dependency to structured covariate dependency: categorical-pattern-matching

Data generated from a system of interest typically consists of measurements from an ensemble of subjects across multiple response and covariate features, and is naturally represented by one response-matrix against one covariate-matrix. Likely each of these two matrices simultaneously embraces heterogeneous data types: continuous, discrete and categorical. Here a matrix is used as a practical platform to ideally keep hidden dependency among/between subjects and features intact on its lattice. Response and covariate dependency is individually computed and expressed through mutliscale blocks via a newly developed computing paradigm named Data Mechanics. We propose a categorical pattern matching approach to establish causal linkages in a form of information flows from patterned response dependency to structured covariate dependency. The strength of an information flow is evaluated by applying the combinatorial information theory. This unified platform for system knowledge discovery is illustrated through five data sets. In each illustrative case, an information flow is demonstrated as an organization of discovered knowledge loci via emergent visible and readable heterogeneity. This unified approach fundamentally resolves many long standing issues, including statistical modeling, multiple response, renormalization and feature selections, in data analysis, but without involving man-made structures and distribution assumptions. The results reported here enhance the idea that linking patterns of response dependency to structures of covariate dependency is the true philosophical foundation underlying data-driven computing and learning in sciences.Comment: 32 pages, 10 figures, 3 box picture

Statistics and geometry of cosmic voids

We introduce new statistical methods for the study of cosmic voids, focusing on the statistics of largest size voids. We distinguish three different types of distributions of voids, namely, Poisson-like, lognormal-like and Pareto-like distributions. The last two distributions are connected with two types of fractal geometry of the matter distribution. Scaling voids with Pareto distribution appear in fractal distributions with box-counting dimension smaller than three (its maximum value), whereas the lognormal void distribution corresponds to multifractals with box-counting dimension equal to three. Moreover, voids of the former type persist in the continuum limit, namely, as the number density of observable objects grows, giving rise to lacunar fractals, whereas voids of the latter type disappear in the continuum limit, giving rise to non-lacunar (multi)fractals. We propose both lacunar and non-lacunar multifractal models of the cosmic web structure of the Universe. A non-lacunar multifractal model is supported by current galaxy surveys as well as cosmological $N$-body simulations. This model suggests, in particular, that small dark matter halos and, arguably, faint galaxies are present in cosmic voids.Comment: 39 pages, 8 EPS figures, supersedes arXiv:0802.038

Performance characterization of clustering algorithms for colour image segmentation

This paper details the implementation of three traditional clustering techniques (K-Means clustering, Fuzzy C-Means clustering and Adaptive K-Means clustering) that are applied to extract the colour information that is used in the image segmentation process. The aim of this paper is to evaluate the performance of the analysed colour clustering techniques for the extraction of optimal features from colour spaces and investigate which method returns the most consistent results when applied on a large suite of mosaic images

Creating Open Source Geodemographic Classifications for Higher Education Applications

This paper explores the use of geodemographic classifications to investigate the social, economic and spatial dimensions of participation in higher education. Education is a public service that confers very significant and tangible benefits upon receiving individuals: as such, we argue that understanding the geodemography of educational opportunity requires an application-specific classification, that exploits under-used educational data sources. We develop a classification for the UK higher education sector, and apply it to the Gospel Oak area of London. We discuss the wider merits of sector specific applications of geodemographics, with particular reference to issues of public service provision