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A Bayesian formulation of system identification problems has become popular recently; this is mainly due to the introduction of a family of prior descriptions (kernels) which encode structural properties of dynamical systems such as stability. The simplest instance of this kernel prescribes that the impulse response coefficients are independent random variables with exponentially decaying variance...
In this paper, we present a method to generate a finite Markovian abstraction for a discrete time linear stochastic system evolving in a full dimensional polytope. Our approach involves an adaptation of an existing approximate abstraction procedure combined with a bisimulation-like refinement algorithm. It proceeds by approximating the transition probabilities from one region to another by calculating...
The tradeoff between noise reduction and speech distortion is a key concern in designing noise reduction algorithms. We have proposed a regularization framework for noise reduction with the consideration of the tradeoff problem. We regard speech estimation as a functional approximation problem in a reproducing kernel Hilbert space (RKHS). In the estimation, the objective function is formulated to...
To analyze the tubular structure correctly and obtain a record of the centerlines has become significantly more challenging and infers countless applications in a large amount of fields. Hence, a robust and automated technique for extracting the centerlines of the tubular structure is required. To address complicated 3D tubular objects, a novel kernel-based modeling approach with regard to minimizing...
In this paper, we deal with the problem of reflectance recovery from multispectral camera output using Support Vector Regression (SVR). As standard, SVR is unidimensional, the spectral reflectance recovery requires a multi-dimensional output. We propose two ways of adaptation: the transformation of the dataset (camera output) to a scalar-valued composite data model on the one hand, and the adaptation...
Video denoising based on temporal or spatiotemporal filtering is highly effective but computationally expensive due to the requirement of motion estimation. Encoder-integrated denoising is an efficient framework that embeds the filtering process into the encoding pipeline so that motion estimation for denoising can be avoided. State-of-the-arts encoder-integrated methods use Least Minimum Mean Square...
In order to overcome the problem that it is difficult for support vector machine to deal with uncertain information system, fuzzy theory and rough set are introduced to get two uncertain support vector machines, which are fuzzy support vector machine and fuzzy rough support vector machine respectively. And the principle of these two uncertain methods reducing the effect of uncertain information is...
In this paper, we have proposed a tensor-based filtering method to enhance the SNR of hyperechoic spots in human breast ultrasound images while suppressing the speckle noise. The breast images were acquired by an Automatic Breast Ultrasound Scanner (ABUS), which offers high spatial resolution and the fast full breast exam capability. The enhanced images were then gone through a pattern recognition...
In this paper, we propose a novel blind image restoration method based on total variation (TV) regularization. It involves alternate iteration of point spread function (PSF) estimation and deconvolution. Using this method, we can obtain clear images from blurred images without an increase in noise and ringing. Thus, blurred images captured using digital cameras can be restored effectively.
In this paper, a robust feature extraction method based on regularized correntropy criterion (RCC) is proposed for novelty detection. In RCC, the criterion aims to maximize the difference between the correntropy of the normal data with their mean and the correntropy of the novel data with the mean of normal data. Moreover, the optimal projection vectors in the proposed objective function can be obtained...
There is always much difficult in the MR image segmentation. Although fuzzy c-means(FCM) clustering algorithm has been widely used in the field of image segmentation study, some inherent deficiencies of this algorithm especially the high cost of computation made the algorithm to be difficult widely used in practice. A novel algorithm, based on kernel fuzzy c-means (KFCM) clustering algorithm and the...
This paper proposes a sequential Monte Carlo filter (particle filter) for state and parameter estimation of dynamic systems. It is applied to the problem of extended object tracking in the presence of dense clutter. The unknown length of a stick-shape object is estimated in addition to the kinematic parameters. The kernel density estimation technique is utilised to approximate the joint posterior...
The rapid growth of supercomputing systems, both in scale and complexity, has been accompanied by degradation in system efficiencies. The sheer abundance of resources including millions of cores, vast amounts of physical memory and high-bandwidth networks are heavily under-utilized. This happens when the resources are time-shared amongst parallel applications that are scheduled to run on a subset...
It is very difficult to detect the small target over water in the marine environment because of the complexity of water movement and the complex physical fields produced by the water interact with environment. We mainly study foreground segmentation for small target in visual image based on MRF (Markov Random Fields). A foreground segmentation method is proposed that is based on kernel function and...
In this work we present a comparative study of Gaussian process models for single-trial event-related potentials (ERPs) in electroencephalography (EEG) recordings. Our data comes from a motor task experiment where an ERP arises before the motor response of the participant to a stimulus. We consider models based on stationary and non-stationary kernel functions. The comparison is done based on two...
We propose a novel decision tree algorithm for modeling function-valued responses. This algorithm partitions the feature space into homogeneous subpopulations with common dose-response signals using a splitting criterion based on Nadaraya-Watson kernel regression and the Cramér-von Mises statistical test. We formulate an important business problem of sales team composition within the dose-response...
Drug cocktails formed by mixing multiple drugs at various doses provide more effective cures than single-drug treatments. However, drugs interact in highly nonlinear ways making the determination of the optimal combination a difficult task. The response surface of the drug cocktail has to be estimated through expensive and time-consuming experimentation. Previous research focused on the use of space-exploratory...
This paper describes a simple image noise removal method which combines a preprocessing step with the Yaroslavsky filter for strong numerical, visual, and theoretical performance on a broad class of images. The framework developed is a two-stage approach. In the first stage the image is denoised by a classical denoising method (e.g., wavelet or curvelet thresholding). In the second step a modification...
We consider the problem of learning the combination of multiple kernels given noisy pairwise constraints, which is in contrast to most of the existing studies that assume perfect pairwise constraints. This problem is particularly important when the pairwise constraints are derived from side information such as hyperlinks and paper citations. We propose a probabilistic approach for learning the combination...
The quality of an image is highly critical for applications such as robotic vision, surveillance, medical imaging, etc. The images captured in real-time are seldom noise free and therefore require noise removal for further processing. Out of several proposed noise removal schemes, an isotropic diffusion filtering is known to achieve highly precise results. However, the accuracy comes at an expense...
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