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In this paper, we proposed a novel algorithm for endoscopic image matching. The algorithm consists of two main components, log-ratio descriptor and probabilistic matching criterion. Log-ratio descriptor is developed by using selected pair of grayscale intensity information that surround the keypoint. The spatial distribution of the pairs follow approximately normal distribution. Then, probabilistic...
The present contribution deals with the statistical tool of Independent Component Analysis (ICA). The focus is on Fas-tICA, arguably the most popular algorithm in the domain of ICA. Despite its success, it is observed that FastICA occasionally yields outcomes that do not correspond to any solutions of ICA. These outcomes are called spurious solutions. In this work, we give a thorough and rigorous...
We consider the problem of selecting an optimal mask for an image manifold, i.e., choosing a subset of the dimensions of the image space that preserves the manifold structure present in the original data. Such masking implements a form of compressed sensing that reduces power consumption in emerging imaging sensor platforms. Our goal is for the manifold learned from masked images to resemble the manifold...
This paper deals with adaptive Constant Modulus Algorithm (CMA) for the blind separation of communication signals. Ikhlef et al. proposed in 2010 an efficient block implementation of the CMA using Givens rotations. We introduce herein a fast adaptive implementation of this method which exploits recent developments on whitening techniques together with appropriate updating of the used statistics and...
Recent derivations have shown that the full Bayes random finite set filter incorporates a linear combination of multi-Bernoulli distributions. The full filter is intractable as the number of terms in the linear combination grows exponentially with the number of targets; this is the problem of data association. A highly desirable approximation would be to find the multi-Bernoulli distribution that...
In this paper, we consider a two-hop wireless system consisting of one source, one destination, one eavesdropper and multiple amplify-and-forward relays. A time-division duplex scheme is proposed that aims at minimizing the secrecy rate outage by selecting an optimal relay from the set of candidate relays. In the first time slot, the source transmits the information bearing signal, meanwhile the destination...
This contribution deals with the generalized symmetric FastICA algorithm in the domain of Independent Component Analysis (ICA). The generalized symmetric version of FastICA has the potential to achieve the optimal separation performance by allowing the usage of different nonlinearity functions in its parallel implementations of one-unit FastICA. In spite of this appealing property, a rigorous study...
Sequential Monte Carlo (SMC) methods are not only a popular tool in the analysis of state-space models, but offer a powerful alternative to Markov chain Monte Carlo (MCMC) in situations where static Bayesian inference must be performed via simulation. In this paper, we propose a recycling scheme of all past simulated particles in the SMC sampler in order to reduce the variance of the final estimator...
Subspace clustering is a useful tool for analyzing large complex data, but in many relevant applications missing data are common. Existing theoretical analysis of this problem shows that subspace clustering from incomplete data is possible, but that analysis requires the number of samples (i.e., partially observed vectors) to be super-polynomial in the dimension d. Such huge sample sizes are unnecessary...
In this demonstration, a GPU-accelerated implementation of the second-order statistics version of the TRINICON (Triple-N independent component analysis for convolutive mixtures) blind source separation (BSS) algorithm is presented. The single-precision arithmetic separation performance is compared with a double-precision CPU (Central Processing Unit) reference implementation to establish the effect...
The challenge of localizing number of concurrent acoustic sources in reverberant enclosures is addressed in this paper. We formulate the localization task as a maximum likelihood (ML) parameter estimation problem, and develop a distributed expectation-maximization (DEM) procedure, based on the Incremental EM (IEM) framework. The algorithm enables localization of the speakers without a center point...
Dictionary learning algorithms based upon matrices/vectors have been used for signal classification by incorporating different constraints such as sparsity, discrimination promoting terms or by learning a classifier along with the dictionary. However, because of the limitations of matrix based dictionary learning algorithms in capturing the underlying subspaces of the data presented in the literature,...
In this paper, we consider the problem of sequential nonlinear regression and introduce an efficient learning algorithm using context trees. Specifically, the regressor space is partitioned and the resulting regions are represented by a context tree. In each region, we assign an independent regression algorithm and the outputs of the all possible nonlinear models defined on the context tree are adaptively...
SIFT (Scale Invariant Feature Transform) is an algorithm commonly used in object recognition applications. The main advantage of SIFT algorithm is its scale and orientation invariance. Processes in SIFT algorithm require high processing power. But these processes can be parallelized which allow high speed increase in algorithm performance. In this report, we discuss the work performed on parallelizing...
In this study, eye motion that captured by webcam is classified in real time. Eyes are detected with using Viola-Jones face detection algorithm. Iris and its center are detected with using Circle Hough Transform on single eye. Eye corners and region are determined OpenCV functions. And finally; The eye motion has been categorized in a way that it looks at down, up, left and right sides according to...
In literature, various linear and nonlinear model structures are defined to identify the systems. Linear models such as Finite Impulse Response (FIR), Infinite Impulse Response (IIR) and Autoregressive (AR) are used in the situations that the input-output relation is signified through linear equivalence. However because of the nonlinear structure of the systems in real life, nonlinear models are developed...
Recently, many adaptive filtering proposals that discuss the sparsity of the system have been appeared. These proposals are, mainly, based on the least-mean-square (LMS) algorithm. In this paper we propose two algorithms that exploit the sparsity of the system and based on the mixed norm LMS (MN-LMS) algorithm. The first algorithm is proposed by adding l1-norm penalty to the cost function of the MN-LMS...
In crowd surveillance systems, it is important to select the proper analysis algorithm considering the properties of the video content. The inappropriate algorithm selection may result in performance degradation and generation of false alarms. An important feature of crowd videos is the density of the crowd. While object detection and tracking based algorithms are feasible for low density crowds,...
Finding roots of words is widely used in document classification and text mining. Computational methods of text similarity are intensely utilized on the English words and successful outcomes are obtained. On the other hand, applying the aforementioned methods on the Turkish words did not give the similar success. In this study, a novel similarity computation algorithm is developed. By using this algorithm...
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