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Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical...
Adding changepoints to a linear Gaussian state space model, such that it can switch between multiple regimes of action, provides a powerful extension which allows more complex system behaviour to be described. However, it also means that more sophisticated algorithms are needed for parameter estimation. This paper addresses the task of Bayesian learning of changepoint model parameters using MCMC....
In this paper, we are devoted to the labeled multi-object tracking problem for generic observation model (GOM) in the framework of Finite set statistics. Firstly, we derive a product-labeled multi-object (P-LMO) filter which is a closed form solution to labeled multi-object Bayesian filter under the standard multi-object transition kernel and generic multi-object likelihood, and thus can be used as...
Within the supervised machine learning framework, classifier performance is significantly affected by the size of training datasets. One of the ways to improve classification accuracy with small training datasets is to utilize additional knowledge about training data that is not present in testing data. In the Learning Using Privileged Information (LUPI) learning paradigm, this additional knowledge...
A novel method called boundary distance is proposed for pre-extracting support vectors. It first calculates the distance between the sample and the other class sample. According to sort distance, the less distance samples, and nearest neighbor samples in the other class, are used as boundary samples. As the boundary samples include most support vectors, it greatly declines the training time without...
This paper presents a unified Non-local Spectral-spatial Centralized Sparse Representation (NL-CSR) model for the hyper-spectral image classification. The proposed model integrates local sparsity and non-local mean centralized induced sparsity. To achieve rich spectral-spatial information, the centralized sparsity enforces the sparse coding vector towards its non-local structural self-similar mean...
Short, transient radio-frequency interference (RFI) events could threaten the quality of astronomical observations made by new and planned radio telescopes such as MeerKAT, the SKA and HERA in the radio quiet reserve in South Africa. Because they are so short, often of the order of microseconds long, these events are difficult to detect and identify in the time-frequency plots typically produced by...
This paper proposes an ideal regularized composite kernel (IRCK) framework for hyperspectral images (HSI) classification. In learning a composite kernel, IRCK exploits spectral information, spatial information, and label information simultaneously. It incorporates the labels into standard spectral and spatial kernels by means of ideal kernel according to a regularization kernel learning framework,...
Hyperspectral data unmixing has attracted considerable attention in recent years. Hyperspectral data may however suffer from varying levels of signal-to-noise ratio over spectral bands. In this paper, we investigate a robust approach for nonlinear hyperspectral data unmixing. Each observed pixel is modeled as a linear mixing of endmember spectra with nonlinear fluctuations embedded in a reproducing...
Kriging-based Global optimization has been proposed and extensively used for solving black-box optimization problems with expensive function evaluations. The performance of such algorithm relies heavily on the effectiveness of the infill criterion that is used to decide which point to evaluate next. Two common infill criteria are, the probability of improvement (PI) and the expected improvement (EI)...
The increasing interest in bistatic SAR missions and in the new opportunities they can create has led to the development during time of a new set of processing solutions, as wide as the possible configurations assumed by the involved satellites and the corresponding applications.
This paper analyzes and compares different Multiple Kernel Learning (MKL) algorithms for the classification of remote sensing (RS) images. The main purpose of the comparison is to identify advantages and disadvantages of different MKL algorithms in terms of their computational time and classification accuracy. Furthermore, some guidelines on the proper selection of the MKL algorithms associated with...
To address the multi-classification problems of hyperspectral dataset, a new method with weighted kernel function based on Chernoff distance is proposed. Chernoff distance utilizes the information between categories and strengthens the separability of original dataset. The adjustable parameter in Chernoff distance can fit the hyperspectral dataset well compared with other least upper bounds. Pairwise...
Besides well-known CPU based architectures, the so-called accelerators (GPU, DSP, FPGA) are about to gain ground in everyday programming, computing tasks. However, programming such computation units is quite different from traditional programming for CPUs,, special skills are required from the developers. In this paper we present techniques, tooling support for the developers in the first step of...
In this paper, we present the Kernel Subclass Support Vector Data Description classifier. We focus on face recognition and human action recognition applications, where we argue that sub-classes are formed within the training class. We modify the standard SVDD optimization problem, so that it exploits subclass information in its optimization process. We extend the proposed method to work in feature...
Support vector data description (SVDD) is a popular kernel method for novelty detection, which constructs a spherical boundary around the normal class with minimum volume. Sphere being a special case of ellipsoid, it thus will be more general to extend classical SVDD to ellipsoidal boundary and better description ability can be anticipated. In this paper, we propose an ellipsoidal SVDD (ESVDD) by...
In this paper we show that a minimal state space realisation in Jordan canonical form for linear continuous-time systems described by rational transfer function could be obtained in a natural and basic way by using the concept of Nerode equivalence. whilst the state space realisation is known, the contribution of this note is that the proposed realisation procedure is directly introduced and not like...
We present a model for time series consisting of an infinite mixture of basis functions, whereby the bases and the mixing process are modelled as posterior means of latent Gaussian processes (GPs). Conditional to observed data, the bases and the mixing process are learnt using a parametric approximation based on pseudo-observations, where the complexity and accuracy of the method are controlled by...
Compactness conditions and estimates for the singular values of the sandwiched Airy transform fAg in ¿2(R) are investigated for suitable functions f (x), g(x), x G R. Sufficient conditions for inclusion of the operator f Ag in the Schatten — von Neumann classes Sp, p G (0,2), are obtained. In particular, conditions for inclusion of the operator fAg in the trace class are found.
Service performance degradation and downtimes are a common on the Internet today. Many on-line services (e.g. Amazon.com, Spotify, and Netflix, etc.) report huge loss in revenue and traffic per episode. This is perhaps due to the correlation between performance and end-users's satisfaction.
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