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Multi-frame super resolution has been well studied in recent years, but blur kernel is always assumed to be known in video super resolution problem. Most blind deconvolution algorithm can both estimate the blur kernel and the sharp image. In this paper, we originally adopt a fast single image blind deconvolution algorithm in video super resolution to estimate high-resolution image and blur kernel...
In order to cope with the complex variation of target appearance during visual tracking, a robust tracking algorithm based on multi-scale kernelized least squares (KLS) is proposed. First, by showing that the dense sampling set of translated patches is circulant, using the well-established theory of circulant matrices, kernelized least squares is efficient computed with fast Fourier transform (FFT)...
Sufficient dimension reduction (SDR) is a popular framework for supervised dimension reduction, aiming at reducing the dimensionality of input data while information on output data is maximally maintained. On the other hand, in many recent supervised classification learning tasks, it is conceivable that the balance of samples in each class varies between the training and testing phases. Such a phenomenon,...
Dermoscopy images usually suffer from spatially-varying defocus blur, which will easily influence the lesion analysis result and lead to wrong aided diagnosis. In this paper, a novel blind deblurring framework is proposed for dermoscopy images with spatially-varying defocus blur. The defocus map is firstly estimated by support vector regressor (SVR) learning model using the natural scene statistics...
Image blurring attenuate crucial textures and thus always result in a pretty dismal visual experience. Unfortunately, image blurring is difficult to avoid during the image acquisition, hence, lots of recent research focus on how to preserve subtle textures while suppress visual artifacts during image deblurring. Among all of the existing image deblurring methods, image priors, such as non-local priors...
In order to understand modifications in a neural network due to learning, it is paramount to develop an effective tool that is capable of mapping the circuit connectivity and its changes from large-scale recordings of neuronal activity patterns. In this paper, context tree maximizing (CTM) is used to estimate directed information (DI) to measure causal influences among neural spike trains. The method...
Wiener systems represent a linear time invariant (LTI) system followed by a static nonlinearity. The identification of these systems has been a research problem for a long time as it is not a trivial task. A new methodology for identifying Wiener systems is proposed in this paper. The proposed method is a combination of well known techniques, namely the Best Linear Approximation (BLA) from the system...
Blind steganalysis is a method used to detect whether there is a hidden message in a media without having to know the steganography algorithm behind it. Digital image is converted into features using feature extraction algorithm subtractive pixel adjacency matrix. A model is built based on the resulting features using machine learning method support vector machine. The support vector machine method...
In this paper, we propose an efficient and robust gross outlier removal method, called the Conceptual Space based Gross Outlier Removal (CSGOR) method, to remove gross outliers for geometric model fitting. In the proposed method, each data point is mapped to a conceptual space by computing the preference of "good" model hypotheses. In the conceptual space, the distributions of inliers and...
Correlation filter based tracking method has been widely used for its high efficiency and robustness. However, reducing model drifting while achieving both high robustness and fast scale estimation is still an open problem. In this paper, we represent the target in kernel feature space and train a classifier on a scale pyramid to achieve adaptive scale estimation. We then integrate three complementary...
Median filter is usefiil in image and video processing. It can be used for filtering noises, for estimating background images, and so on. However, median filter is time-consuming. This paper presents a new method for realizing the median filter in GPU (Graphics Processing Unit) for background estimation problem. The proposed method can perform the filtering in just several milliseconds for each frame...
In this paper, we propose a blind restoration method that is insusceptible to noise. This method is used for point spread function estimation. In addition, we propose to use a filter that can remove noise. Experimental results show that our proposed method more accurately estimates a motion blur and a deblurring image compared with the conventional method.
Online social media networks play important roles for people to share opinions, communicate with others. One of important features behind these activities is trust. This paper investigates the trust model in Online social media networks. Considering the interaction between two users and the reputation in the social networks, this trust model gives a definition about the trust value between two users...
Regrasping is the process of adjusting the position and orientation of an object in one's hand. The study of robotic regrasping has generally been limited to use of theoretical analytical models and cases with little uncertainty. Analytical models and simulations have so far proven unable to capture the complexity of the real world. Empirical statistical models are more promising, but collecting good...
We relax parametric inference to a nonparametric representation towards more general solutions on factor graphs. We use the Bayes tree factorization to maximally exploit structure in the joint posterior thereby minimizing computation. We use kernel density estimation to represent a wider class of constraint beliefs, which naturally encapsulates multi-hypothesis and non-Gaussian inference. A variety...
Segmenting moving objects from the background is an important step in intelligent video applications, such as intelligent video surveillance. Many approaches use optimal threshold for the separation of moving object from a background. However they suffer from two limitations: It is not only difficult to compute an optimal threshold, but also ignore the correlation that exists between the intensity...
PM2.5 concentration can have significant impacts on solar irradiation and thus on photovoltaic (PV) power output. This paper presents a method to model impacts of PM2.5 concentration on PV power. A non-parametric kernel density estimation is used to fit the probability distribution of PM2.5 concentration. An incremental relation between the increase of PM2.5 concentration and the decrease of solar...
This paper introduces a new sampling strategy and shows that superior performance can be obtained for a range of sampling based robotic motion planners, used in scenarios with low task variance, as found in many vision guided pick and place operations. The strategy uses kernel density estimation to identify regions with high probability of containing configurations being part of feasible solutions,...
As an automatic tracking system, the shipboard Automatic Identification System (AIS) has been widely adopted to identify and locate the vessels by electronically exchanging data with other nearby ships. With the development of computer technology, AIS-based visualization of vessel traffic has attracted increasing attention during the past several years. The vessel density visualization can be used...
Graphics Processing Units (GPUs) have become a prevalent platform for high throughput general purpose computing. The peak computational throughput of GPUs has been steadily increasing with each technology node by scaling the number of cores on the chip. Although this vastly improves the performance of several compute-intensive applications, our experiments show that some applications can achieve peak...
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