Improving the Computational Cost for Copied Region Detection in Forensic Images

2016 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Tu Huynh-Kha ◽  
Thuong Le-Tien ◽  
Synh Ha ◽  
Khoa Huynh-Van
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Research Work ◽  
Computational Cost ◽  
Correlation Coefficients ◽  
Zernike Moments ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

scholarly journals General model for best feature extraction of EEG using discrete wavelet transform wavelet family and differential evolution

2020 ◽  
Vol 16 (3) ◽  
pp. 155014772091100 ◽  
Author(s):  
Ahmad al-Qerem ◽  
Faten Kharbat ◽  
Shadi Nashwan ◽  
Staish Ashraf ◽  
khairi blaou
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Differential Evolution ◽  
Discrete Wavelet Transform ◽  
Research Work ◽  
Discrete Wavelet ◽  
Proposed Model ◽  
Feature Extraction And Selection ◽  
Selection Step ◽  
Wavelet Family

Wavelet family and differential evolution are proposed for categorization of epilepsy cases based on electroencephalogram (EEG) signals. Discrete wavelet transform is widely used in feature extraction step because it efficiently works in this field, as confirmed by the results of previous studies. The feature selection step is used to minimize dimensionality by excluding irrelevant features. This step is conducted using differential evolution. This article presents an efficient model for EEG classification by considering feature extraction and selection. Seven different types of common wavelets were tested in our research work. These are Discrete Meyer (dmey), Reverse biorthogonal (rbio), Biorthogonal (bior), Daubechies (db), Symlets (sym), Coiflets (coif), and Haar (Haar). Several kinds of discrete wavelet transform are used to produce a wide variety of features. Afterwards, we use differential evolution to choose appropriate features that will achieve the best performance of signal classification. For classification step, we have used Bonn databases to build the classifiers and test their performance. The results prove the effectiveness of the proposed model.

WAVELET-BASED BASE LINE WANDERING REMOVAL AND R PEAK AND QRS COMPLEX DETECTION

2007 ◽  
Vol 05 (06) ◽  
pp. 927-939 ◽  
Author(s):  
R. SHANTHA SELVA KUMARI ◽  
V. SADASIVAM
Keyword(s):  
Wavelet Transform ◽  
False Negative ◽  
Automated System ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Base Line ◽  
Subjective Analysis ◽  
Positive Prediction ◽  
Time Frequency

Wavelet transform has emerged as a powerful tool for time-frequency analysis and signal coding favored for the interrogation of complex non-stationary signals such as the ECG signal. Measurement of timing intervals of ECG signal by automated system is highly superior to its subjective analysis. The timing interval is found from the onset and offset of the wave components of the ECG signal. Since the Daubechies wavelet is similar to the shape of the ECG signal, better detection is achieved. Discrete Wavelet Transform is easier to implement, provides multiresolution and also reduces the computational time, and thus, is used. In the pre-processing step, the base line wandering is removed from the ECG signal. Then the R peak and the QRS complexes are detected. Twenty five records from the MIT-BIH arrhythmia database are used to evaluate the proposed method. Sensitivity and positive prediction are used as performance measures. This method is very simple and detects all the R peaks (sensitivity = 100% and positive prediction = 99.86%). That is, false positive detection is very negligible and false negative detection is zero. The performance of the proposed method is better than other methods that exist in the literature.

Wavelet‐transform‐based prestack multiscale Kirchhoff migration

Geophysics ◽  
2004 ◽  
Vol 69 (6) ◽  
pp. 1505-1512 ◽  
Author(s):  
Zhou Yu ◽  
George A. McMechan ◽  
Phil D. Anno ◽  
John F. Ferguson
Keyword(s):  
Wavelet Transform ◽  
Wavelet Decomposition ◽  
Computational Cost ◽  
Synthetic Data ◽  
Phase Distortion ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Kirchhoff Migration ◽  
Individual Scale ◽  
The Individual

We propose a Kirchhoff‐style algorithm that migrates coefficients obtained by wavelet decomposition of seismic traces over time. Wavelet‐based prestack multiscale Kirchhoff migration involves four steps: wavelet decomposition of the seismic data, thresholding of the resulting wavelet coefficients, multiscale Kirchhoff migration, and image reconstruction from the multiscale images. The migration procedure applied to each wavelet scale is the same as conventional Kirchhoff migration but operates on wavelet coefficients. Since only the wavelet coefficients are migrated, the cost of wavelet‐based migration is reduced compared to that of conventional Kirchhoff migration. Kirchhoff migration of wavelet‐decomposed data, followed by wavelet reconstruction, is kinematically equivalent to and yields similar migrated signal shapes and amplitudes as conventional Kirchhoff migration when data at all wavelet scales are included. The decimation in the conventional discrete pyramid wavelet decomposition introduces a translation‐variant phase distortion in the wavelet domain. This phase distortion is overcome by using a stationary wavelet‐transform rather than the conventional discrete wavelet‐transform of the data to be migrated. A wavelet reconstruction operator produces a single composite broadband migrated space‐domain image from multiscale images. Multiscale images correspond to responses in different frequency windows, and migrating the data at each scale has a different cost. Migrating some, or only one, of the individual scale data sets considerably reduces the computational cost of the migration. Successful 2D tests are shown for migrations of synthetic data for a point‐diffractor model, a multilayer model, and the Marmousi model.

scholarly journals Sub-band selection approach to artifact suppression from electroencephalography signal using hybrid wavelet transform

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142199226
Author(s):  
Jannatul Ferdous ◽  
Sujan Ali ◽  
Ekramul Hamid ◽  
Khademul Islam Molla
Keyword(s):  
Wavelet Transform ◽  
Performance Metrics ◽  
Wavelet Packet ◽  
Reference Signal ◽  
Computational Cost ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Finite Set ◽  
Artifact Suppression

This article presents a hybrid wavelet-based algorithm to suppress the ocular artifacts from electroencephalography (EEG) signals. The hybrid wavelet transform (HWT) method is designed by the combination of discrete wavelet decomposition and wavelet packet transform. The artifact suppression is performed by the selection of sub-bands obtained by HWT. Fractional Gaussian noise (fGn) is used as the reference signal to select the sub-bands containing the artifacts. The multichannel EEG signal is decomposed HWT into a finite set of sub-bands. The energies of the sub-bands are compared to that of the fGn to the desired sub-band signals. The EEG signal is reconstructed by the selected sub-bands consisting of EEG. The experiments are conducted for both simulated and real EEG signals to study the performance of the proposed algorithm. The results are compared with recently developed algorithms of artifact suppression. It is found that the proposed method performs better than the methods compared in terms of performance metrics and computational cost.

scholarly journals Power Quality Classification of disturbances using Discrete Wavelet Packet Transform (DWPT) with Adaptive Neuro-Fuzzy System

2021 ◽  
Vol 12 (3) ◽  
pp. 4892-4903
Author(s):  
K.RamaMohana Reddy Et. al.
Keyword(s):  
Wavelet Transform ◽  
Power Quality ◽  
Convergence Time ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Classification Methods ◽  
Discrete Wavelet Packet Transform ◽  
Neuro Fuzzy

With the development of the technologies, the demand for good quality of electric power is increasing day by day. In Distributed Generation Systems (DGs), the quality of power can cause serious problems such as sensitive equipment's malfunction, the temperature riseof machines. Therefore, detection of power quality events in the power system is more important to take further actions. The existing power quality events classification methods have high computational time with low accuracy. In order to overcome this problem, this paper presents Discrete Packet Wavelet Transform-Kalman filter based Adaptive Neuro-Fuzzy approach for identification and classification of PQ events. The proposed method classifies the events with better classification accuracy, less convergence time and low in error prediction. The results show that the proposed method has better performance compared with the existing classification methods. The proposed method is Implemented and tested using MATLAB and it provides more accuracy when compared to the existing systems such as Discrete Wavelet Transform based Fuzzy Logic Adaptive System and Fourier Transform based Artificial neural networks etc..

scholarly journals Content Based Image Retrieval using Transform Domain Features and Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 3008-3011
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Distance Measures ◽  
Content Based Image Retrieval ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Transform Domain ◽  
Integer Wavelet ◽  
Mean Variance ◽  
Efficient Recognition

In this paper, Content Based Image Retrieval using Transform domain features and algorithms has been implemented. The image can be decomposed by Discrete Wavelet Transform (DWT) to extract the features based on DC coefficients. Each sub-image is calculated by mean, variance and standard deviation to get more efficient recognition. The database image also applied in the domain of Stationary Wavelet Transform (SWT) and Integer Wavelet Transform (IWT) by using different distance measures. The proposed algorithm is the combination of DWT, SWT and IWT has been implemented using COREL database. This proposed method has more efficient recognition and less computational time over existing methods

Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform

2014 ◽  
Vol 31 ◽  
pp. 13-27 ◽  
Author(s):  
Sajad Farokhi ◽  
Siti Mariyam Shamsuddin ◽  
U.U. Sheikh ◽  
Jan Flusser ◽  
Mohammad Khansari ◽  
...  
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Near Infrared ◽  
Zernike Moments ◽  
Discrete Wavelet

scholarly journals Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction Motor

2020 ◽  
Vol 24 (3) ◽  
pp. 417-424
Author(s):  
A.I. Abdullateef ◽  
O.S. Fagbolagun ◽  
M.F. Sanusi ◽  
M.F. Akorede ◽  
M.A. Afolayan
Keyword(s):  
Wavelet Transform ◽  
Induction Motor ◽  
Euclidean Distance ◽  
Induction Motors ◽  
Short Circuit ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Machine Failure ◽  
Wavelet Energy

Induction motors are the backbone of the industries because they are easy to operate, rugged, economical and reliable. However, they are subjected to stator’s faults which damage the windings and consequently lead to machine failure and loss of revenue. Early detection and  classification of these faults are important for the effective operation of induction motors. Stators faults detection and classification based on  wavelet Transform was carried out in this study. The feature extraction of the acquired data was achieved using lifting decomposition and reconstruction scheme while Euclidean distance of the Wavelet energy was used to classify the faults. The Wavelet energies increased for all three conditions monitored, normal condition, inter-turn fault and phase-to-phase fault, as the frequency band of the signal decreases from D1 to A3. The deviations in the Euclidean Distance of the current of the Wavelet energy obtained for the phase-to-phase faults are 99.1909, 99.8239 and 87.9750 for phases A and B, A and C, B and C respectively. While that of the inter-turn faults in phases A, B and C are 77.5572, 61.6389 and 62.5581 respectively. Based on the Euclidean distances of the faults, Df and normal current signals, three classification points were set: K1 = 0.60 x 102, K2 = 0.80 x 102 and K3 = 1.00 x 102. For K2 ≥ Df ≥ K1 inter-turn faults is identified and for K3 ≥ Df ≥ K2 phase to phase fault identified. This will improve the induction motors stator’s fault diagnosis. Keywords: induction motor, stator fault classification, data acquisition system, Discrete Wavelet Transform

Application of discrete wavelet transform in seismic nonlinear analysis of soil–structure interaction problems

2021 ◽  
pp. 875529302098802
Author(s):  
Reza Kamgar ◽  
Reihaneh Tavakoli ◽  
Peyman Rahgozar ◽  
Robert Jankowski
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Nonlinear Analysis ◽  
Soil Structure ◽  
Soil Structure Interaction ◽  
Peak Ground Velocity ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Seismic Responses ◽  
Structure Interaction

Simulation of soil–structure interaction (SSI) effects is a time-consuming and costly process. However, ignoring the influence of SSI on structural response may lead to inaccurate results, especially in the case of seismic nonlinear analysis. In this article, wavelet transform methodology has been utilized for investigation of the seismic response of soil–structure systems. For this purpose, different story outrigger-braced buildings resting on two different types of soil have been considered for SSI analysis. For each SSI system, several seismic records, with different values of peak ground acceleration (PGA) and peak ground velocity (PGV), have been first decomposed into approximate and detailed signals using a discrete wavelet transform. Then, seismic responses of the SSI systems subjected to the approximate signal have been evaluated. The results of the study show that, for earthquakes with low PGA/PGV ratio, the error percentage of all the parameters is smaller than 5% for the first level, and the error index is below 10% for the third level. As the PGA/PGV ratio of an earthquake increases, the concordance of approximate results with the main results decreases. However, even for the earthquakes with the PGA/PGV ratio higher than 1.2 g s/m, the first-level approximation can be used to predict seismic responses with at least 90% accuracy while significantly reducing the computational time.

scholarly journals An Efficient Compound Image Compression Using Optimal Discrete Wavelet Transform and Run Length Encoding Techniques

2019 ◽  
Vol 28 (1) ◽  
pp. 87-101 ◽  
Author(s):  
Priya Vasanth Sundara Rajan ◽  
A. Lenin Fred
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Discrete Wavelet Transform ◽  
Optimization Technique ◽  
Research Work ◽  
Discrete Wavelet ◽  
Compression Process ◽  
Run Length ◽  
Compound Images ◽  
Compound Image

Abstract Reduction in file size leads to reduction in the number of bits required to store it. When data is compressed, it must be decompressed into its original form bit for bit. Compound images are defined as images that contain a combination of text, natural (photo) images and graphic images. Here, compression is the process of reducing the amount of data required to represent information. Image compression is done on the basis of various loss and lossless compression algorithms. This research work deals with the preprocessing and transformations used to compress a compound image to produce a high compression ratio (CR), less compression time and so on. In the compression process the images are considered for preprocessing and discrete wavelet transform with adaptive particle swarm optimization process. The purpose of this optimization technique is to optimize the wavelet coefficient in Harr wavelet for improving the CR value. In the image compression process, run length coding is used to compress the compound images. Based on this technique, it produces minimum CR and less computation time of compound images.

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