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This paper proposes a method for segmentation of images containing both textual and graphical data. The method uses wavelet transformation to build the feature vector and a pattern recognition technique to classify areas of a document image. Values of wavelet coefficients distribution histogram of the source images sliding window serve as elements of a feature vector. For recognition of document area...
Graph signal processing is an emerging area that has a wide variety of applications, e.g. energy networks, transportation networks and neuronal networks. Narang and Ortega (2012) proposed the critically sampled two-channel filter bank for signals on undirected graphs using spectral graph theory. The design of graph QMF (Quadrature-Mirror-Filters) by Narang and Ortega (2012) is based on the Chebyshev...
The aim of this study is to detect P-wave onset and end of electrocardiograms (ECG). This wave is important for detecting people prone to atrial fibrillation, one of the most frequent heart diseases, but the wave is very difficult to segment accurately because of its small amplitude and the very different shapes it can take. Two different methods are tested for the segmentation : the first one is...
In this paper we present a way to calculate the fusion of multi-focus images based on the linear combination of a pair of images taken by a digital camera with different levels of focus. For the linear combination, a linear function with spatial coherence is optimized to maximize the sharpness of the merged image. By the complexity and dispersity of the linear system of equations arises, the solution...
Hyperspectral imagery is characterized by high dimensionality and rich information. How to explore the nature of the high dimensional data more precisely and to find the actual distribution of features are the priorities in the research on hyperspectral remote sensing image processing. It is known that edges in imagery contain some important information regarding to the actual distribution of the...
A novel method for texture image classification was proposed by using dual-tree complex wavelets transform and support vector machines. The dual-tree complex wavelets transform was used to decompose texture image with four levels, feature vector was first used for training and later on for testing the support vector machine classifier. The experimental setup consists of twenty texture images from...
As technology advances; blur in an image remains as an ever-present issue in the image processing field. A blurred image is mathematically expressed as a convolution of a blur function with a sharp image, plus noise. Removing blur from an image has been widely researched and is still important as new images are collected. Without a reference image, identifying, measuring, and removing blur from a...
Image quality is affected by two predominant factors, noise and blur. Blur typically manifests itself as a smoothing of edges, and can be described as the convolution of an image with an unknown blur kernel. The inverse of convolution is deconvolution, a difficult process even in the absence of noise, which aims to recover the true image. Removing blur from an image has two stages: identifying or...
This paper addresses the automatic blood vessel detection problem in retinal images using matched filtering in an integrated system design platform that involves curve let transform and fuzzy c-means. Although noise is kept constant in medical CCD cameras, due to a number of factors, the contrast between the background and the blood vessels in retinal images and consequently the visual quality of...
Several researches and methods have been developed in the aim of efficiently detecting abnormalities in Electroencephalogram (EEG) time series. The aim of this work is to detect a real-time Epileptic seizure. We designed an algorithm which decomposes EEG signals of a database, normal and epileptics, by the lifted wavelet transform (LWT), in order to extract the features. To reduce the time allocated...
Recent advances in adaptive filter theory and the hardware for signal acquisition have led to the realization that purely linear algorithms are often not adequate in these domains. Nonlinearities in the input space have become apparent with today's real world problems. Algorithms that process the data must keep pace with the advances in signal acquisition. Recently kernel adaptive (online) filtering...
In order to overcome the shortcomings in traditional anomaly intrusion detection methods, such as low detection rate and high false alarm rate, this paper proposes an intrusion detection method based on wavelet kernel Least Square Support Vector Machine (LS-SVM). As a new machine learning method, SVM has been used in Intrusion Detection System (IDS) recently and achieved certain effects. While the...
This paper presents a method of image restoration for projective ground images which lie on a projection orthogonal to the camera axis. The ground images are initially transformed using homography, and then the proposed image restoration is applied. The process is performed in the dual-tree complex wavelet transform domain in conjunction with L0 reweighting and L2 minimisation (L0RL2) employed to...
Image denoising methods have been implemented in both spatial and transform domains. Each domain has its advantages and shortcomings, which can be complemented by each other. State-of-the-art methods like block-matching 3D filtering (BM3D) therefore combine both domains. However, implementation of such methods is not trivial. We offer a hybrid method that is surprisingly easy to implement and yet...
This paper is proposing Naïve Bayes classifier detection system to identify the symptom of stator winding deterioration. The proposed system is based on probabilistic classifier with strong independence assumption of each fault case. The temporary short circuit case is defined as non permanent short circuit fault with high impedance. This fault case is representing the early stage of stator insulation...
Over the last decade, computerized heart screening techniques have been increasingly receiving attention. In general, one can say that such techniques can be categorized as: with, or without the so-called Electrocardiogram (ECG) signal. Considering this latter strategy, we devote this paper with the intention to design an algorithm that provides with heart sounds known as Phonocardiograms (PGC) investigation...
Sparsity is an ubiquitous property exhibited by many natural real-world data such as images, which has been playing an important role in image and multi-media data processing. However, for many data, such as images, the sparsity pattern is not completely random, i.e., there are structures over the sparse coefficients. By exploiting this structure, we can model the data better and may further improve...
A two-dimensional quaternion Fourier transform (QFT) defined with the kernel e − i+j+k/√3 ω · x is proposed. Some fundamental properties, such as convolution theorem and Plancherel theorem are established. The wavelet transform is extended to quaternion algebra using the kernel of the QFT.
To efficiently decrease the number of false alarms in detection of infrared small targets with morphological filters, a post processing algorithm is proposed in this paper. Morphological filters such as hit-or-miss and modified top-hat transform has been used to enhance and detect infrared dim small target embedded in cluttered background. However, false alarms will be increased in real scenario with...
In this paper we present an algorithm to detect text on video frames consisting of lecture slides. We begin by performing a multi-channel wavelet transform and then merge the channel components for the high frequency sub bands to obtain a composite energy map. Thresholding the energy map results in an edge map consisting of candidate text pixels — some of these correspond to actual text and others...
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