Multi Resolution Research Articles

A Comparative Analysis of Fusion Techniques Based on Multi Resolution Transforms

Medical images play an important role in detection of abnormalities. A medical image like CT or MR does not at times give all the required data required for detection at a given time. Multi spectral imaging plays a significant role in projecting a clear picture of an abnormality. When the data available at hand is a MR and CT image of a particular area, the MR image may give details which are not provided by the CT and vice versa. Hence, an optimal approach would be to fuse the two images to get the combined details of both the image without degrading the image quality. Image fusion provides optimal results when done on a multi resolution platform. A detailed comparative study of the wavelet transform for image fusion using various techniques has been presented in this paper.

A Hybridized Disparity Computation Fusing Disparity Space Image and Multi Resolution Image Segmentation

D information is getting more importance nowadays.There are several ways to get 3D information.One way to get 3D information involves the laser based depth estimators, which is a very costly process. Other way is to extract 3D information from 2D stereo image pairs by using stereo matching techniques. Stereo matching is a vast area of research. It can be classified into area (window) based approach, feature based approach, and optimization based approach. Area based approach generally generates dense disparity map with low accuracy and low computation time. Feature based method produces highly accurate and sparse disparity map with low computation time.Optimization based method produces dense disparity map with high accuracy and high execution time.By keeping these things in mind, we proposed a new hybrid way for disparity computation. The proposed method consists of three steps. Theyare disparityestimation, image segmentation, and disparity refinement. Since the ultimate goal of stereo matching is to obtain dense disparity map with high accuracy and low execution time, we select optimization based approach for disparity estimation step and for image segmentation step we select multi resolution image segmentation. At the end, disparity refinement is done by combining the result of both the previous steps.As there are several optimization techniques, we choose disparity space image (DSI) baseddynamic programming (DP).We tested the proposed method on several stereo pairs and found that method produced reasonably good quality results. Index Terms—Stereo matching, DSI, DP.

An Efficient Feature Extraction Method Based on Entropy for Power Quality Disturbance

This study explores the applicability of entropy defined as thermodynamic state variable introduced by German Physicists Rudolf clausius and also presents the concepts and application of said state variable as a measure of system disorganization. Later an entropy-based feature Analysis method for power quality disturbance analysis has been proposed. Feature extraction of a disturbed power signal provides information that helps to detect the responsible fault for power quality disturbance. A precise and faster feature extraction tool helps power engineers to monitor and maintain power disturbances more efficiently. Firstly, the decomposition coefficients are obtained by applying 10-level wavelet multi resolution analysis to the signals (normal, sag, swell, outage, harmonic and sag with harmonic and swell with harmonic) generated by using the parametric equations. Secondly, a combined feature vector is obtained from standard deviation of these features after distinctive features for each signal are extracted by applying the energy, the Shannon entropy and the log-energy entropy methods to decomposition coefficients. Finally the entropy methods detect the different types of power quality disturbance.

A Multiresolution Transform Based Detection of Throat Abnormalities

Medical image processing is gaining rapid significance as it plays a vital role in early detection and processing for getting intimate details for further treatment which has led to complete cure of fatal diseases which were once considered incurable. State of the art medical diagnosis equipments incorporated with signal processors have made the diagnosis quite fast and accurate. A multi resolution based detection of abnormality in the throat has been proposed in this paper which is based on a multi resolution approximation. Comparison of extracted feature points and certain other parameters between the reference and the image under study have been made use of in detection of the abnormality. The performance measures indicate a relatively good performance in terms of the detection rate and the image quality after processing when compared with existing techniques.

Multiresolution representation for curves based on ternary subdivision schemes

Subdivision offers a way to increase the resolution of models, while reverse subdivision possesses the opposite ability. Combining the two theories could realize the multiresolution (MR) representation of models. Based on two ternary subdivision schemes, we present the trial and refined filters and an algorithm to realize MR representation for curves, which has some difference compared with the work relating to binary schemes. And the filters yield biorthogonal wavelet systems which are the underlying theory fundament of curves MR. By experiments and numerical calculations, we demonstrate that by using the ternary methods one can accomplish the MR representation for curves and the low-resolution results obtained by reverse subdivision can approximate the original curves well. Besides, ternary methods need smaller number of decomposition times than binary methods to get low-resolution results at similar levels of resolution for the same original curve.

FPGA Based Multi Resolution Graphics Controller

FPGA Based Multi Resolution Graphics Controller

False ventricular tachycardia alarm suppression in the ICU based on the discrete wavelet transform in the ECG signal

Over the past few years, reducing the number of false positive cardiac monitor alarms (FA) in the intensive care unit (ICU) has become an issue of the utmost importance. In our work, we developed a robust methodology that, without the need for additional non-ECG waveforms, suppresses false positive ventricular tachycardia (VT) alarms without resulting in false negative alarms. Our approach is based on features extracted from the ECG signal 20 seconds prior to a triggered alarm. We applied a multi resolution wavelet transform to the ECG data 20seconds prior to the alarm trigger, extracted features from appropriately chosen scales and combined them across all available leads. These representations are presented to a L1-regularized logistic regression classifier. Results are shown in two datasets of physiological waveforms with manually assessed cardiac monitor alarms: the MIMIC II dataset, where we achieved a false alarm (FA) suppression of 21% with zero true alarm (TA) suppression; and a dataset compiled by UCSF and General Electric, where a 36% FA suppression was achieved with a zero TA suppression. The methodology described in this work could be implemented to reduce the number of false monitor alarms in other arrhythmias.

MULTI SCALE TIME SERIES PREDICTION FOR INTRUSION DETECTION

We propose an anomaly-based network intrusion detection system, which analyzes traffic features to det ect anomalies. The proposed system can be used both in online as well as off-line mode for detecting devia tions from the expected behavior. Although our approach uses network packet or flow data, it is general enou gh to be adaptable for use with any other network vari able, which may be used as a signal for anomaly detection. It differs from most existing approaches in its use of wavelet transform for generating dif ferent time scales for a signal and using these scales as an input to a two-stage neural network predictor. T he predictor predicts the expected signal value and la bels considerable deviations from this value as anomalies. The primary contribution of our work would be to empirically evaluate the effectiveness of multi resolution analysis as an input to neural net work prediction engine specifically for the purpose of intrusion detection. The role of Intrusion Detectio n Systems (IDSs), as special-purpose devices to detect anomalies and attacks in a network, is becom ing more important. First, anomaly-based methods cannot achieve an outstanding performance without a comprehensive labeled and up-to-date training set with all different attack types, which is very costly and time-consuming to create if not impossib le. Second, efficient and effective fusion of several d etection technologies becomes a big challenge for building an operational hybrid intrusion detection system. Keyword: Anomaly Detection, Two-Stage Neural Network Predictor, Multi-Resolution Analysis

English

Usually the images taken by digital cameras are different from the original true color image in terms of color and brightness of the scene captured. Various factors like non uniform illumination, color of ambient light, low or high contrast, and over exposure or under exposure of some specific regions may leads to poor contrast which is annoying to human visual system. Thus, enhancement methods are required for color images. In this paper An image fusion is used to improve the performance of DC coefficient scaling method in the compressed DCT domain for enhancing the contrast of colour images. Using the multi resolution wavelet fusion preserves the brightness of the enhanced image. This improves the entropy of standard DCT based DC coefficient scaling method. For comparing the performance along with SNR and the absolute standard deviation difference is also used as parameters. The methods are tested on various true colour images from different environment

Low Cost Embedded System for Voltage Sag Analysis

paper describes the development of low cost embedded system for detecting and analysing the voltage sags in the ac supply. The proposed system have the capabilities of real time acquisition of voltage signal using an Embedded Controller and display its parameters in a Graphical User Interface in the Central Server (PC).The features of the signal are extracted using Discrete Wavelet Transform with Multi Resolution Analysis. The voltage sags are created in an experimental setup using voltage injection method. The real time voltages are sensed by Hall Effect Transducer and digitalised by mbed microcontroller (LPC1768 ARM Cortex-M3).The real time samples are then serially transmitted to the Central Server. A standalone application is developed in Central Server using Visual C Sharp for real time visualisation, processing and analysis of the signal.

Reconstruction of Wavelet Coefficients

A comparative study of different methods of reconstruction of wavelet coefficients is presented. The following are the different techniques for the reconstruction of wavelet coefficients. To start with, we show how to design and construct Daubechies four coefficient wavelet system which are orthogonal and compactly supported wavelets. Then we outline the multi resolution analysis of wavelets using Mallat transform. Multi resolution analysis can be illustrated by the decomposition and reconstruction of wavelet system using Laplacian Pyramid. To construct wavelet systems with finite support and regularity using orthonormal and interpolariting units, only multicomponent wavelets are possible. When image function is expressed in terms of scaling functions and wavelet functions of higher resolution, we need to consider only few wavelet coefficients and wavelet coefficients are dominant only near edges. The wavelet coefficient near edges can be estimated using wavelet transform maxima and statistical inference of coefficients using Markov tree model. An alternate method is the reconstruction of wavelet coefficients using total variation minimisation. If we use thresholding, so that when we neglect wavelet coefficients having values less than a threshold value there is ringing across edges. Hence we propose an improved algorithm of reconstruction of wavelet coefficients using zero padding and cycle spinning. PSNR of images with wavelet based interpolation and denoising by cycle spinning is moderately high.

An Efficient Multi Resolution Filter Bank Based on DA Based Multiplication

An Efficient Multi Resolution Filter Bank Based on DA Based Multiplication

The Research of Dual Quarternary Pseudoframes for Hardy Space and Applications in Engineering Materials

Materials science is an interdisciplinary field applying the properties of matter to various areas of science and engineering.. In this work, the notion of the quarternary generalized multiresol- ution structure (TGMS) of subspace is proposed. The characteristics of quarternary affine pseudoframes for subspaces is investigated. Construction of a GMS of Paley-Wiener subspace of is studied. The pyramid decomposition scheme is obtained based on such a TGMS and a sufficient condition for its existence is provided. A constructive method for affine frames of based on a TGMS is established.

Moving object detection method based on complementary multi resolution background models

A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion, thermal and dynamic background sequences. Two groups of complementary Gaussian mixture models were used. The ghost and real static object could be classified by comparing the similarity of the edge images further. In each group, the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise. The computational color model was also used to depress illustration variations and light shadows. The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods. Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences. Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences, respectively. The proposed method shows a relatively good performance, especially for the intermittent object motion sequences.

Discrimination of Power Quality Distorted Signals Based on Time-frequency Analysis and Probabilistic Neural Network

Recognition and classification of Power Quality Distorted Signals (PQDSs) in power systems is an essential duty. One of the noteworthy issues in Power Quality Analysis (PQA) is identification of distorted signals using an efficient scheme. This paper recommends a Time–Frequency Analysis (TFA), for extracting features, so-called hybrid approach, using incorporation of Multi Resolution Analysis (MRA) and Generalized S Transform (GST). Moreover, the proposed scheme is noticed to quality of features and ranking them in order to find the best combination with lower dimension. A new efficient feature ranking method namely Orthogonal Forward Selection (OFS) is applied for selection of the best subset features. Probabilistic Neural Network (PNN) as classifier is considered. An extensive series of simple and complex PQDSs are simulated to verify of suggested detection scheme. Also, sensitivity of the proposed method under different conditions of noise has been investigated. The obtained outcomes are compared with those obtained using other methods in previous research to assess the performance.

Megaflood analysis through channel networks of the Athabasca Valles, Mars based on multi-resolution stereo DTMs and 2D hydrodynamic modeling

Stereo analysis of in-orbital imagery provides valuable topographic data for scientific research over planetary surfaces especially for the interpretation of potential fluvial activity. The focus of research into planetary fluvial activity has been shifting toward quantitative modeling with various spatial resolution DTMs from visual interpretation with ortho images. Thus in this study, we tested the application of hydraulic analysis with multi resolution Martian DTMs, which were constructed following the approaches of Kim and Muller (2009). Planet. Space Sci. 57 (14), 2095. Subsequently, a two-dimensional hydraulic model was introduced to conduct flow simulation using the extracted 1.2–150m resolution DTMs. As a result, it was found that the simulated water flows coincided with what might be water eroded geomorphic features over target areas. Moreover, the information acquired from the modeling, such as water depth along the time line, flow direction and travel time, is proving of great value for the interpretation of surface characteristics. Results highlighted the importance of DTM quality for simulating fluvial channel hydraulics across planetary surfaces.

Quasi-biennial oscillation in GPS VTEC measurements

The quasi-biennial oscillation, QBO, a well known periodicity in the equatorial stratospheric zonal winds, is also found in ionospheric parameters and in solar and geomagnetic activity indices. Many authors speculated about the link between the QBO in solar and geomagnetic activity and the QBO in atmospheric parameters. In this work we analyze the presence of the QBO in the ionosphere using the Vertical Total Electron Content (VTEC) values obtained from Global Navigation Satellite System (GNSS) measurements during the period 1999–2012. In particular, we used IONEX files, i.e. the International GNSS Service (IGS) ionospheric products. IONEX provide VTEC values around the world at 2-h intervals. From these data we compute global and zonal averages of VTEC at different local times at mid and equatorial geomagnetic latitudes. VTEC and Extreme Ultra Violet (EUV) solar flux time series are analyzed using a wavelet multi resolution analysis. In all cases the QBO is detected among other expected periodicities.Since the main source of variation of free electrons in the ionospheric F2 region, where the maximum concentration occurs, is solar EUV radiation, we conclude that the most important cause attributable to QBO in the ionosphere is the quasi-biennial variability of EUV solar flux. Both QBOs are observed to vary in phase.The percentage of the total signal of the ionospheric QBO at the local times of the maximum photoionization is around 15% independent of magnetic latitude while the respective percentage of the total energy for the EUV solar flux is about 18%.

Palmprint Compression using Wavelet based Contourlet Transform and SPIHT

Wavelets are capable of approximating images with sharp discontinuities but not good at the approximation of images with smooth contours. Hence contourlet transform was introduced. It produces an efficient multi resolution and directional decomposition of an image using a laplacian pyramidal decomposition and a directional filter bank. It has good approximation capability for smooth 2D signals. A nonredundant version of this transform, Wavelet Based Contourlet Transform (WBCT) is appropriate for image compression. In this work, the use of WBCT for the compression of palmprint database and its performance are evaluated. The experimental results show that this coding is competitive to the wavelet based compression technique and provides visually superior quality of reconstruction for the mentioned images.

Automatic CT Brain Image Segmentation Using Two Level Multiresolution Mixture Model of EM

Tissue classification in computed tomography (CT) brain images is an important issue in the analysis of several brain dementias. A combination of different approaches for the segmentation of brain images is presented in this paper. A multi resolution algorithm is proposed along with scaled versions using Gaussian filter and wavelet analysis that extends expectation maximization (EM) algorithm. It is found that it is less sensitive to noise and got more accurate image segmentation than traditional EM. Moreover the algorithm has been applied on 20 sets of CT of the human brain and compared with other works. The segmentation results show the advantages of the proposed work have achieved more promising results and the results have been tested with Doctors.

La rééducation de la maladie de Parkinson : actualités

Stereo analysis of in-orbital imagery provides valuable topographic data for scientific research over planetary surfaces especially for the interpretation of potential fluvial activity. The focus of research into planetary fluvial activity has been shifting toward quantitative modeling with various spatial resolution DTMs from visual interpretation with ortho images. Thus in this study, we tested the application of hydraulic analysis with multi resolution Martian DTMs, which were constructed following the approaches of Kim and Muller (2009). Planet. Space Sci. 57 (14), 2095. Subsequently, a two-dimensional hydraulic model was introduced to conduct flow simulation using the extracted 1.2–150 m resolution DTMs. As a result, it was found that the simulated water flows coincided with what might be water eroded geomorphic features over target areas. Moreover, the information acquired from the modeling, such as water depth along the time line, flow direction and travel time, is proving of great value for the interpretation of surface characteristics. Results highlighted the importance of DTM quality for simulating fluvial channel hydraulics across planetary surfaces.