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Automated ECG signal processing can assist in diagnosing several heart diseases. Many R peak detection methods have been studied because the accuracy of R peak detection significantly affects the quality of subsequent ECG feature extraction. Two important steps in R peak detection algorithm that draw attention over researchers are the preprocessing and thresholding stages. Among several methods, wavelet...
Atrial fibrillation (AF) is one of the most common cardiac arrhythmia and effects nearly 1–2 of every 100 persons of the population. This paper evaluates the effectiveness of Machine Learning (ML) approach to detect AF episodes. Features, determined exclusively on the basis of beat intervals, are classified with linear classifier. Performances of the proposed approach are evaluated by means of the...
In this paper, a study is conducted on combining analytical and holistic strategies for handwriting recognition. Even though the big majority of the recent high recognition rate systems adopts analytical strategies, physiological scientists suggest that the holistic strategy is the key for realizing near-human performance. In what we believe is a fresh perspective on handwriting recognition, combining...
Our focus in this work is on the practical applicability of matrix variate Fisher-Bingham model for statistical inferences via Maximum Likelihood Estimation (MLE) technique using simple Bayesian classifier. The practicability of such parametric models on high dimensional data (e.g., via manifold valued data) remained a big hurdle since long i.e., mainly due to the difficult normalising constant naturally...
Deep convolutional neural networks (DCNN's) have shown great value in approaching highly challenging problems in image classification. Based on the successes of DCNNs in scene classification and object detection and localization it is natural to consider whether they would be effective for much simpler computer vision tasks. Our work involves the application of a DCNN to the relatively simple task...
This paper introduces a generalization of the Fisher vectors to the Riemannian manifold. The proposed descriptors, called Riemannian Fisher vectors, are defined first, based on the mixture model of Riemannian Gaussian distributions. Next, their expressions are derived and they are applied in the context of texture image classification. The results are compared to those given by the recently proposed...
Many signal and image processing applications are based on the classification of covariance matrices. These latter are elements on a Riemannian manifold for which many generative models have been developed in the literature. Recently, the Riemannian Laplace distribution (RLD) has been proposed to model the within-class variability of images. In this context, the present paper proposes an application...
Biometrics are used for security purpose. It is used to recognize a person based on their unique characteristics. Among several biometrics, Fingerprint is the most widely used and acceptable biometrics. Biometric system has several advantages over traditional methods. But it can be affected by several attacks. The type 1 attack is performed at the sensor level. Differentiating a genuine biometric...
In this work, a new iris recognition algorithm based on tonal distribution of iris images is introduced. During the process of identification probability distribution functions of colored irises are generated in HSI and YCbCr color spaces. The discrimination between classes is obtained by using Kullback-Leibler divergence. In order to obtain the final decision on recognition, the multi decision on...
In this paper, a comprehensive analysis of electroencephalogram (EEG) signals is carried out in the empirical mode decomposition (EMD) domain using a publicly available benchmark EEG database. First, the intrinsic mode functions (IMF) are extracted in the EMD domain. Next, normal inverse Gaussian (NIG) probability density function (pdf) is introduced and it is investigated whether the NIG pdf can...
Multi-path fading, environmental shadowing and channel interference always result in the significant temporal and spatial variations of Received Signal Strength (RSS), and eventually lead to the low accuracy in Wireless Local Area Networks (WLAN) fingerprint based indoor localization. Motivated by this, we focus on deriving out the positioning error bound which can be applied to characterize the theoretical...
Current GPS-based devices have difficulty localizing in cases where the GPS signal is unavailable or insufficiently accurate. This paper presents an algorithm for localizing a vehicle on an arbitrary road network using vision, road curvature estimates, or a combination of both. The method uses an extension of topometric localization, which is a hybrid between topological and metric localization. The...
In this paper, we present an accelerated knowledge-driven content-based information mining system for Big Earth Observation data fusion. The tool combines, at pixel level, the unsupervised clustering results of different number of features. The features, extracted from different EO raster image types and from existing GIS vector maps, are combined, in form of a BoW, with a user given semantic concepts...
We constructed gait verification system for criminal investigation. The system is designed so that criminal investigators can use it and obtain professional gait verification results. We think the system can support criminal investigation where gait can be a clue of the perpetrator's identity. We summarize the constructed system in this paper.
Nowadays, the field of physical object security based on surface microstructures lacks common and shared data for the development, testing and fair benchmarking of new identification and authentication technologies. To our knowledge, most published results are based on proprietary data that also often lacks the necessary size for statistically significant results and conclusions. Therefore, in this...
A novel method is presented based on a statistical manifold for text-independent speaker recognition. After feature extraction, speaker recognition becomes a sequence classification problem. By discarding time information, the core task is the comparison of multiple sample sets. Each set is assumed to be governed by a probability density function (PDF). We estimate the PDFs and place the estimated...
Autonomous vehicles must be capable of localizing even in GPS denied situations. In this paper, we propose a real-time method to localize a vehicle along a route using visual imagery or range information. Our approach is an implementation of topometric localization, which combines the robustness of topological localization with the geometric accuracy of metric methods. We construct a map by navigating...
Efficient similarity search in uncertain data is a central problem in many modern applications such as biometric identification, stock market analysis, sensor networks, medical imaging, etc. In such applications, the feature vector of an object is not exactly known but is rather defined by a probability density function like a Gaussian Mixture Model (GMM). Previous work is limited to axis-parallel...
In this paper we study the problem of score normalization in biometric verification systems. Specifically, we introduce a new class of normalization techniques, which unlike the commonly used parametric score normalization techniques, such as z- or t-norm, make no assumptions regarding the shape of the underlying score distribution. The proposed class of normalization techniques first estimates the...
Mismatch in speech bandwidth between training and real operation greatly degrades the performance of automatic speech recognition (ASR) systems. Missing feature technique (MFT) is effective in handling bandwidth mismatch. However, current MFT-based methods ignore the mismatch in the filterbank channels which cover the upper and lower limit cutoff frequencies. To solve this problem, we propose to partition...
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