183
261.31
1.43
200

Publication History View all

  • [Show abstract] [Hide abstract]
    ABSTRACT: Microarray data allows an unprecedented view of the biochemical mechanisms contained within a cell although deriving useful information from the data is still proving to be a difficult task. In this paper, a novel method based on a multi-objective genetic algorithm is investigated that evolves a near-optimal trade-off between Artificial Neural Network (ANN) classifier accuracy (sensitivity and specificity) and size (number of genes). This hybrid method is shown to work on four well-established gene expression data sets taken from the literature. The results provide evidence for the rule discovery ability of the hybrid method and indicate that the approach can return biologically intelligible as well as plausible results and requires no pre-filtering or pre-selection of genes.
    International Journal of Data Mining and Bioinformatics 01/2013; 7(4):376-96.
  • [Show abstract] [Hide abstract]
    ABSTRACT: The Relevance Vector Machine (RVM) is a sparse classifier in which complexity is controlled with the Automatic Relevance Determination prior. However, sparsity is dependent on kernel choice and severe over-fitting can occur.We describe multi-objective evolutionary algorithms (MOEAs) which optimise RVMs allowing selection of the best operating true and false positive rates and complexity from the Pareto set of optimal trade-offs. We introduce several cross-validation methods for use during evolutionary optimisation. Comparisons on benchmark datasets using multi-resolution kernels show that the MOEAs can locate markedly sparser RVMs than the standard, with comparable accuracies.
    Pattern Recognition 09/2012; 45(9):3535–3543.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Multi-modal multiphoton microscopy was used to investigate tissue microstructure in the zone of calcified cartilage, focussing on the collagen fibre organisation at the tidemark and cement line. Thick, unstained and unfixed sagittal sections were prepared from the equine metacarpophalangeal joint. Second harmonic generation (SHG) provided contrast for collagen, two-photon fluorescence (TPF) for endogenous fluorophores, and coherent anti-Stokes Raman scattering (CARS) allowed the cells to be visualised. The structure of radial and calcified cartilage was found to vary with location across the joint, with the palma regions showing a more ordered parallel arrangement of collagen fibres than the cortical ridge and dorsal regions. These patterns may be associated with regional variations in joint loading. In addition, the cell lacunae had a greater diameter in the dorsal region than in the palmar region. At the cement line some collagen fibres were observed crossing between the calcified cartilage and the subchondral bone. At the tidemark the fibres were parallel and continuous between the radial and calcified cartilage. Beneath early superficial lesions the structure of the tidemark and calcified cartilage was disrupted with discontinuities and gaps in the fibrillar organisation. Cartilage microstructure varies in the deep zones between regions of different loading. The variations in collagen structure observed may be significant to the local mechanical properties of the cartilage and therefore may be important to its mechanical interactions with the subchondral bone. The calcified cartilage is altered even below early superficial lesions and therefore is important in the understanding of the aetiology of osteoarthritis.
    Journal of Anatomy 02/2012; 220(4):405-16.
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we show that the recently introduced family of the cubeness measures Cβ(S)(β>0) satisfy the following desirable property: limβ→∞Cβ(S)=0, for any given 3D shape S different from a cube. The result implies that the behaviour of cubeness measures changes depending on the selected value of β and the cubeness measure can be arbitrarily close to zero for a suitably large value of β. This also implies that for a suitable value of β, the measure Cβ(S) can be used for detecting small deviations of a shape from a perfect cube. Some examples are given to illustrate these properties.
    Applied Mathematics and Computation 07/2011; 217:8860-8865.
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we consider the distance between the shape centroid computed from the shape interior points and the shape centroid computed from the shape boundary points. We show that the distance between those centroids is upper bounded by the quarter of the perimeter of the shape considered. The obtained upper bound is sharp and cannot be improved.Next, we introduce the shape centredness as a new shape descriptor which, informally speaking, should indicate to which degree a shape has a uniquely defined centre. By exploiting the result mentioned above, we give a formula for the computation of the shape centredness. Such a computed centredness is invariant with respect to translation, rotation and scaling transformations.
    Pattern Recognition. 01/2011; 44:2161-2169.
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we introduce cubeness measure C(S): a shape similarity measure between a given 3D shape and a cube. The cubeness measure has several desirable properties: it ranges over (0,1] and reaches 1 only when the given shape is a cube, it is invariant with respect to rotation, translation and scaling, and is also robust with respect to noise. The measure is compared with discrete 3D compactness measure from the existing literature.A modification of the basic definition of cubeness is also given. This modification enables the creation of a family of descriptors Cγ,δ(S), which vary their behaviour depending on the choice of parameters (γ,δ). Several examples are given, which illustrates the behaviour of these measures. Also some shape retrieval experiments are presented which illustrate the suitability of cubeness measures for such applications. The experimental results are in accordance with theoretical considerations and with our perception.
    Pattern Recognition Letters - PRL. 01/2011; 32(14):1871-1881.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we propose a measure which defines the degree to which a shape differs from a square. The new measure is easy to compute and being area based, is robust—e.g., with respect to noise or narrow intrusions. Also, it satisfies the following desirable properties: – it ranges over (0,1] and gives the measured squareness equal to 1 if and only if the measured shape is a square; – it is invariant with respect to translations, rotations and scaling. In addition, we propose a generalisation of the new measure so that shape squareness can be computed while controlling the impact of the relative position of points inside the shape. Such a generalisation enables a tuning of the behaviour of the squareness measure and makes it applicable to a range of applications. A second generalisation produces a measure, parameterised by δ, that ranges in the interval (0,1] and equals 1 if and only if the measured shape is a rhombus whose diagonals are in the proportion 1:δ. The new measures (the initial measure and the generalised ones) are naturally defined and theoretically well founded—consequently, their behaviour can be well understood. As a by-product of the approach we obtain a new method for the orienting of shapes, which is demonstrated to be superior with respect to the standard method in several situations. The usefulness of the methods described in the manuscript is illustrated on three large shape databases: diatoms (ADIAC), MPEG-7 CE-1, and trademarks. KeywordsShape–Squareness measure–Shapeclassification–Orientation–Early vision
    Journal of Mathematical Imaging and Vision 01/2011; 39(1):13-27.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we propose a new circularity measure which defines the degree to which a shape differs from a perfect circle. The new measure is easy to compute and, being area based, is robust—e.g., with respect to noise or narrow intrusions. Also, it satisfies the following desirable properties:•it ranges over (0,1] and gives the measured circularity equal to 1 if and only if the measured shape is a circle;•it is invariant with respect to translations, rotations and scaling.Compared with the most standard circularity measure, which considers the relation between the shape area and the shape perimeter, the new measure performs better in the case of shapes with boundary defects (which lead to a large increase in perimeter) and in the case of compound shapes. In contrast to the standard circularity measure, the new measure depends on the mutual position of the components inside a compound shape.Also, the new measure performs consistently in the case of shapes with very small (i.e., close to zero) measured circularity. It turns out that such a property enables the new measure to measure the linearity of shapes.In addition, we propose a generalisation of the new measure so that shape circularity can be computed while controlling the impact of the relative position of points inside the shape. An additional advantage of the generalised measure is that it can be used for detecting small irregularities in nearly circular shapes damaged by noise or during an extraction process in a particular image processing task.
    Pattern Recognition 01/2010;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Determining the orientation of a shape is a common task in many image processing applications. It is usually part of the image preprocessing stages and further processing may rely on an adequate method to determine the orientation. There are several methods for computing the orientation of a shape, each of them with its own strengths and weaknesses; a method which performs outstandingly for one application may have a poor performance for a different application. In this paper we present a new method for computing shape orientation based on the projection of the tangent vectors of a shape onto a line and weighting them using a function of the curvature. Some of the results from Žunić (2008) [14] are particular cases of the results presented here.
    Pattern Recognition 01/2010; 43:3035-3041.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We consider the method that computes the shape orientation as the direction α that maximises the integral of the length of projections, taken to the power of 2N, of all the straight line segments whose end points belong to the shape, to a line that has the slope α. We show that for N=1 such a definition of shape orientation is consistent with the shape orientation defined by the axis of the least second moment of inertia. For N>1 this is not the case, and consequently our new method can produce different results. As an additional benefit our approach leads to a new method for computation of the orientation of compound objects.
    International Journal of Computer Vision 01/2009; 81(2):138-154.
Information provided on this web page is aggregated encyclopedic and bibliographical information relating to the named institution. Information provided is not approved by the institution itself. The institution’s logo (and/or other graphical identification, such as a coat of arms) is used only to identify the institution in a nominal way. Under certain jurisdictions it may be property of the institution.