Abstract: Intelligibility is an essential characteristic of a speech
signal, which is used to help in the understanding of information in
speech signal. Background noise in the environment can deteriorate
the intelligibility of a recorded speech. In this paper, we presented a
simple variance subtracted - variable level discrete wavelet transform,
which improve the intelligibility of speech. The proposed algorithm
does not require an explicit estimation of noise, i.e., prior knowledge
of the noise; hence, it is easy to implement, and it reduces the
computational burden. The proposed algorithm decides a separate
decomposition level for each frame based on signal dominant and
dominant noise criteria. The performance of the proposed algorithm
is evaluated with speech intelligibility measure (STOI), and results
obtained are compared with Universal Discrete Wavelet Transform
(DWT) thresholding and Minimum Mean Square Error (MMSE)
methods. The experimental results revealed that the proposed scheme
outperformed competing methods
Abstract: Uncontrolled growth of abnormal cells in the lung in the form of tumor can be either benign (non-cancerous) or malignant (cancerous). Patients with Lung Cancer (LC) have an average of five years life span expectancy provided diagnosis, detection and prediction, which reduces many treatment options to risk of invasive surgery increasing survival rate. Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI) for earlier detection of cancer are common. Gaussian filter along with median filter used for smoothing and noise removal, Histogram Equalization (HE) for image enhancement gives the best results without inviting further opinions. Lung cavities are extracted and the background portion other than two lung cavities is completely removed with right and left lungs segmented separately. Region properties measurements area, perimeter, diameter, centroid and eccentricity measured for the tumor segmented image, while texture is characterized by Gray-Level Co-occurrence Matrix (GLCM) functions, feature extraction provides Region of Interest (ROI) given as input to classifier. Two levels of classifications, K-Nearest Neighbor (KNN) is used for determining patient condition as normal or abnormal, while Artificial Neural Networks (ANN) is used for identifying the cancer stage is employed. Discrete Wavelet Transform (DWT) algorithm is used for the main feature extraction leading to best efficiency. The developed technology finds encouraging results for real time information and on line detection for future research.
Abstract: This study presented hybrid pre-processing approach along with a conceptual model to enhance the accuracy of river discharge prediction. In order to achieve this goal, Ensemble Empirical Mode Decomposition algorithm (EEMD), Discrete Wavelet Transform (DWT) and Mutual Information (MI) were employed as a hybrid pre-processing approach conjugated to Least Square Support Vector Machine (LSSVM). A conceptual strategy namely multi-station model was developed to forecast the Souris River discharge more accurately. The strategy used herein was capable of covering uncertainties and complexities of river discharge modeling. DWT and EEMD was coupled, and the feature selection was performed for decomposed sub-series using MI to be employed in multi-station model. In the proposed feature selection method, some useless sub-series were omitted to achieve better performance. Results approved efficiency of the proposed DWT-EEMD-MI approach to improve accuracy of multi-station modeling strategies.
Abstract: Computacional recognition of sign languages aims to
allow a greater social and digital inclusion of deaf people through
interpretation of their language by computer. This article presents
a model of recognition of two of global parameters from sign
languages; hand configurations and hand movements. Hand motion
is captured through an infrared technology and its joints are built
into a virtual three-dimensional space. A Multilayer Perceptron
Neural Network (MLP) was used to classify hand configurations and
Dynamic Time Warping (DWT) recognizes hand motion. Beyond
of the method of sign recognition, we provide a dataset of
hand configurations and motion capture built with help of fluent
professionals in sign languages. Despite this technology can be
used to translate any sign from any signs dictionary, Brazilian
Sign Language (Libras) was used as case study. Finally, the model
presented in this paper achieved a recognition rate of 80.4%.
Abstract: In this paper, we propose a blind and robust audio watermarking scheme based on spread spectrum in Discrete Wavelet Transform (DWT) domain. Watermarks are embedded in the low-frequency coefficients, which is less audible. The key idea is dividing the audio signal into small frames, and magnitude of the 6th level of DWT approximation coefficients is modifying based upon the Direct Sequence Spread Spectrum (DSSS) technique. Also, the psychoacoustic model for enhancing in imperceptibility, as well as Savitsky-Golay filter for increasing accuracy in extraction, is used. The experimental results illustrate high robustness against most common attacks, i.e. Gaussian noise addition, Low pass filter, Resampling, Requantizing, MP3 compression, without significant perceptual distortion (ODG is higher than -1). The proposed scheme has about 83 bps data payload.
Abstract: Over communication networks, images can be easily copied and distributed in an illegal way. The copyright protection for authors and owners is necessary. Therefore, the digital watermarking techniques play an important role as a valid solution for authority problems. Digital image watermarking techniques are used to hide watermarks into images to achieve copyright protection and prevent its illegal copy. Watermarks need to be robust to attacks and maintain data quality. Therefore, we discussed in this paper two approaches for image watermarking, first is based on Particle Swarm Optimization (PSO) and the second approach is based on Genetic Algorithm (GA). Discrete wavelet transformation (DWT) is used with the two approaches separately for embedding process to cover image transformation. Each of PSO and GA is based on co-relation coefficient to detect the high energy coefficient watermark bit in the original image and then hide the watermark in original image. Many experiments were conducted for the two approaches with different values of PSO and GA parameters. From experiments, PSO approach got better results with PSNR equal 53, MSE equal 0.0039. Whereas GA approach got PSNR equal 50.5 and MSE equal 0.0048 when using population size equal to 100, number of iterations equal to 150 and 3×3 block. According to the results, we can note that small block size can affect the quality of image watermarking based PSO/GA because small block size can increase the search area of the watermarking image. Better PSO results were obtained when using swarm size equal to 100.
Abstract: In this paper, a hybrid blind digital watermarking system using Discrete Wavelet Transform (DWT) and Contourlet Transform (CT) has been implemented and tested. The implemented combined digital watermarking system has been tested against five common types of image attacks. The performance evaluation shows improved results in terms of imperceptibility, robustness, and high tolerance against these attacks; accordingly, the system is very effective and applicable.
Abstract: Nowadays, the vigorous popularity of video processing techniques has resulted in an explosive growth of multimedia data illegal use. So, watermarking security has received much more attention. The purpose of this paper is to explore some watermarking techniques in order to observe their specificities and select the finest methods to apply in digital cinema domain against movie piracy by creating an invisible watermark that includes the date, time and the place where the hacking was done. We have studied three principal watermarking techniques in the frequency domain: Spread spectrum, Wavelet transform domain and finally the digital cinema watermarking transform domain. In this paper, a detailed technique is presented where embedding is performed using direct sequence spread spectrum technique in DWT transform domain. Experiment results shows that the algorithm provides high robustness and good imperceptibility.
Abstract: This paper presents an efficient method of electrocardiogram signal denoising based on a hybrid approach. Two techniques are brought together to create an efficient denoising process. The first is an Adaptive Dual Threshold Filter (ADTF) and the second is the Discrete Wavelet Transform (DWT). The presented approach is based on three steps of denoising, the DWT decomposition, the ADTF step and the highest peaks correction step. This paper presents some application of the approach on some electrocardiogram signals of the MIT-BIH database. The results of these applications are promising compared to other recently published techniques.
Abstract: Digital Watermarking is a procedure to prevent the unauthorized access and modification of personal data. It assures that the communication between two parties remains secure and their communication should be undetected. This paper investigates the consequence of the watermark strength of the grayscale image using a Discrete Wavelet Transformation (DWT) additive technique. In this method, the gray scale host image is divided into four sub bands: LL (Low-Low), HL (High-Low), LH (Low-High), HH (High-High) and the watermark is inserted in an LL sub band using DWT technique. As the image is divided into four sub bands, a watermark of equal size of the LL sub band has been inserted and the results are discussed. LL represents the average component of the host image which contains the maximum information of the image. Two kinds of experiments are performed. In the first, the same watermark is embedded in different images and in the later on the strength of the watermark varies by a factor of s i.e. (s=10, 20, 30, 40, 50) and it is inserted in the same image.
Abstract: One of the most important challenging factors in
medical images is nominated as noise. Image denoising refers to the
improvement of a digital medical image that has been infected by
Additive White Gaussian Noise (AWGN). The digital medical image
or video can be affected by different types of noises. They are
impulse noise, Poisson noise and AWGN. Computed tomography
(CT) images are subjects to low quality due to the noise. Quality of
CT images is dependent on absorbed dose to patients directly in such
a way that increase in absorbed radiation, consequently absorbed
dose to patients (ADP), enhances the CT images quality. In this
manner, noise reduction techniques on purpose of images quality
enhancement exposing no excess radiation to patients is one the
challenging problems for CT images processing. In this work, noise
reduction in CT images was performed using two different
directional 2 dimensional (2D) transformations; i.e., Curvelet and
Contourlet and Discrete Wavelet Transform (DWT) thresholding
methods of BayesShrink and AdaptShrink, compared to each other
and we proposed a new threshold in wavelet domain for not only
noise reduction but also edge retaining, consequently the proposed
method retains the modified coefficients significantly that result good
visual quality. Data evaluations were accomplished by using two
criterions; namely, peak signal to noise ratio (PSNR) and Structure
similarity (Ssim).
Abstract: High resolution images are always desired as they contain the more information and they can better represent the original data. So, to convert the low resolution image into high resolution interpolation is done. The quality of such high resolution image depends on the interpolation function and is assessed in terms of sharpness of image. This paper focuses on Wavelet based Interpolation Techniques in which an input image is divided into subbands. Each subband is processed separately and finally combined the processed subbandsto get the super resolution image.
Abstract: Image Processing is a structure of Signal Processing
for which the input is the image and the output is also an image or
parameter of the image. Image Resolution has been frequently
referred as an important aspect of an image. In Image Resolution
Enhancement, images are being processed in order to obtain more
enhanced resolution. To generate highly resoluted image for a low
resoluted input image with high PSNR value. Stationary Wavelet
Transform is used for Edge Detection and minimize the loss occurs
during Downsampling. Inverse Discrete Wavelet Transform is to get
highly resoluted image. Highly resoluted output is generated from the
Low resolution input with high quality. Noisy input will generate
output with low PSNR value. So Noisy resolution enhancement
technique has been used for adaptive sub-band thresholding is used.
Downsampling in each of the DWT subbands causes information loss
in the respective subbands. SWT is employed to minimize this loss.
Inverse Discrete wavelet transform (IDWT) is to convert the object
which is downsampled using DWT into a highly resoluted object.
Used Image denoising and resolution enhancement techniques will
generate image with high PSNR value. Our Proposed method will
improve Image Resolution and reached the optimized threshold.
Abstract: This paper provides a quantitative measure of the
time-varying multiunit neuronal spiking activity using an entropy
based approach. To verify the status embedded in the neuronal activity
of a population of neurons, the discrete wavelet transform (DWT) is
used to isolate the inherent spiking activity of MUA. Due to the
de-correlating property of DWT, the spiking activity would be
preserved while reducing the non-spiking component. By evaluating
the entropy of the wavelet coefficients of the de-noised MUA, a
multiresolution Shannon entropy (MRSE) of the MUA signal is
developed. The proposed entropy was tested in the analysis of both
simulated noisy MUA and actual MUA recorded from cortex in rodent
model. Simulation and experimental results demonstrate that the
dynamics of a population can be quantified by using the proposed
entropy.
Abstract: The study of the electrical signals produced by neural
activities of human brain is called Electroencephalography. In this
paper, we propose an automatic and efficient EEG signal
classification approach. The proposed approach is used to classify the
EEG signal into two classes: epileptic seizure or not. In the proposed
approach, we start with extracting the features by applying Discrete
Wavelet Transform (DWT) in order to decompose the EEG signals
into sub-bands. These features, extracted from details and
approximation coefficients of DWT sub-bands, are used as input to
Principal Component Analysis (PCA). The classification is based on
reducing the feature dimension using PCA and deriving the supportvectors
using Support Vector Machine (SVM). The experimental are
performed on real and standard dataset. A very high level of
classification accuracy is obtained in the result of classification.
Abstract: Key frame extraction methods select the most
representative frames of a video, which can be used in different areas
of video processing such as video retrieval, video summary, and video
indexing. In this paper we present a novel approach for extracting key
frames from video sequences. The frame is characterized uniquely by
his contours which are represented by the dominant blocks. These
dominant blocks are located on the contours and its near textures.
When the video frames have a noticeable changement, its dominant
blocks changed, then we can extracte a key frame. The dominant
blocks of every frame is computed, and then feature vectors are
extracted from the dominant blocks image of each frame and arranged
in a feature matrix. Singular Value Decomposition is used to calculate
sliding windows ranks of those matrices. Finally the computed ranks
are traced and then we are able to extract key frames of a video.
Experimental results show that the proposed approach is robust
against a large range of digital effects used during shot transition.
Abstract: ‘Steganalysis’ is one of the challenging and attractive interests for the researchers with the development of information hiding techniques. It is the procedure to detect the hidden information from the stego created by known steganographic algorithm. In this paper, a novel feature based image steganalysis technique is proposed. Various statistical moments have been used along with some similarity metric. The proposed steganalysis technique has been designed based on transformation in four wavelet domains, which include Haar, Daubechies, Symlets and Biorthogonal. Each domain is being subjected to various classifiers, namely K-nearest-neighbor, K* Classifier, Locally weighted learning, Naive Bayes classifier, Neural networks, Decision trees and Support vector machines. The experiments are performed on a large set of pictures which are available freely in image database. The system also predicts the different message length definitions.
Abstract: In this paper, for detection of inclined eccentricity in an induction motor, time–frequency analysis of the stator startup current is carried out. For this purpose, the discrete wavelet transform is used. Data are obtained from simulations, using winding function approach. The results show the validity of the approach for detecting the fault and discriminating with respect to other faults.
Abstract: The process in which the complementary information from multiple images is integrated to provide composite image that contains more information than the original input images is called image fusion. Medical image fusion provides useful information from multimodality medical images that provides additional information to the doctor for diagnosis of diseases in a better way. This paper represents the wavelet based medical image fusion algorithm on different multimodality medical images. In order to fuse the medical images, images are decomposed using Redundant Wavelet Transform (RWT). The high frequency coefficients are convolved with morphological operator followed by the maximum-selection (MS) rule. The low frequency coefficients are processed by MS rule. The reconstructed image is obtained by inverse RWT. The quantitative measures which includes Mean, Standard Deviation, Average Gradient, Spatial frequency, Edge based Similarity Measures are considered for evaluating the fused images. The performance of this proposed method is compared with Pixel averaging, PCA, and DWT fusion methods. When compared with conventional methods, the proposed framework provides better performance for analysis of multimodality medical images.
Abstract: A duplicated image region may be subjected to a number of attacks such as noise addition, compression, reflection, rotation, and scaling with the intention of either merely mating it to its targeted neighborhood or preventing its detection. In this paper, we present an effective and robust method of detecting duplicated regions inclusive of those affected by the various attacks. In order to reduce the dimension of the image, the proposed algorithm firstly performs discrete wavelet transform, DWT, of a suspicious image. However, unlike most existing copy move image forgery (CMIF) detection algorithms operating in the DWT domain which extract only the low frequency subband of the DWT of the suspicious image thereby leaving valuable information in the other three subbands, the proposed algorithm simultaneously extracts features from all the four subbands. The extracted features are not only more accurate representation of image regions but also robust to additive noise, JPEG compression, and affine transformation. Furthermore, principal component analysis-eigenvalue decomposition, PCA-EVD, is applied to reduce the dimension of the features. The extracted features are then sorted using the more computationally efficient Radix Sort algorithm. Finally, same affine transformation selection, SATS, a duplication verification method, is applied to detect duplicated regions. The proposed algorithm is not only fast but also more robust to attacks compared to the related CMIF detection algorithms. The experimental results show high detection rates.