Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1056
Design and Implementation of Speckle Noise Reduction Algorithm Using 2D Ultrasound Image
Статья научная
Ultrasound is mostly used for diagnosis to deal with the specific abnormality in human body. To observe the internal organs including liver, kidneys, pancreas, thyroid gland, ovaries etc. ultrasound can be used. In diagnostic applications, 2 to 18 MHz frequencies are used. The sound wave explorations occurred through soft tissue and fluids. It bounces back as echoes from denser surfaces and creates an image. While producing ultrasound images from echo signal speckle noise is induced in a multiplicative way. Thus, speckle becomes the key challenge for ultrasound imaging. Several speckle reducing linear, non-linear and anisotropic diffusion-based methods are implemented to preserve the sharp edges of ultrasound images. Those methods contain lake of smoothing and edge preservation. However, this research proposed a combined method of adaptive filter (wiener) and anisotropic diffusion (modified Perona Malik) for speckle reduction of 2D ultrasound images by retain the important anatomical features. A comparison of all the existing methods studied based on the simulated experiment. To test the methods liver, kidney, heart and pancreas noise free images are used. Then, speckle noise is manually added with distinguished variance in between 0.02 and 0.20. Quality metrics are used to test the performance and show the improvements of the proposed method. About 71.79% structure similarity (SSIM), 66.72% root mean square error (RMSE), 56.93% signal to noise ratio (SNR), and 62.30% computational time are improved on average compared with the other methods.
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Статья научная
This paper presents one novel algorithm for minimization of non-zero coefficients of Finite Impulse response (FIR) pulse-shaping filter, mostly employed in practical digital communication system to alleviate the difficulties resulting from Inter Symbol Interference (ISI), followed by its hardware optimization on a Field Programmable Gate Array (FPGA) chip . Filter performance has been demonstrated through the inclusion of impulse response, magnitude spectrum and requirement of various hardware blocks. The supremacy of our algorithm has been substantiated by comparing its performance with other existing models of different length. From the simulation results, it has been concluded that the proposed FIR filter provides a considerable reduction in the number of non-zero coefficients without affecting its performance significantly.
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Статья научная
Energy management system for the household appliances in terms of monitoring and control is described in this paper. Monitoring the energy consumption is the initial step to reduce it. The smart device is designed and developed that will monitor the load device parameters like voltage, current, power factor, power consumption and frequency. The load device is made to turn OFF/ ON based on the status of the device through the mobile application. To get the measured data and to control the load device an Android application has developed, which uses a mobile-enabled Bluetooth to communicate with the Bluetooth low energy module, which is interfaced to the host microcontroller. Bluetooth module is used as it works well for home automation applications. The proposed device has a simple design, low power consumption, cost-effective and easy to interact with the user.
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Статья научная
This paper proposes security technique for the confidential data which is the combination of three techniques, first is image compression that is based on wavelet transformation which will compress confidential image and reduce the size of the image, second is cryptography that is based on symmetric key which will encrypt the confidential image, and third is steganography that is based on least significant bit (LSB) which will embedded encrypted information inside a cover image. Therefore the purpose of the proposed technique is the high security and quality of the reconstructed cover image.
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Design and implementation of a novel complete filter for EEG application on FPGA
Статья научная
Filter is vastly used to detect different human signal in real time. In this paper, a novel complete digital filter is proposed for the fast detection of EEG signals due to avoid the mixtures of different biomedical signals. This paper intends to design a digital complete filter based on Field Programmable Gate Array (FPGA) for the alleviation of unwanted frequency components in biomedical signals specially EEG signals. For this purpose, complete filter which is a combination of integrator filter and differentiator filter which supports both low and high noises and comparatively inexpensive than other signal processing methodologies can be used. For hardware implementation, FPGA board is used which is a combination of different logic gates which offers inexpensive and long lasting services.
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Design of Fast Fourier Transform Architecture for GF(24) with Reduced Operational Complexity
Статья научная
In this paper, the architecture for Fast Fourier Transform over Galois Field (24) is described. The method used is cyclotomic decomposition. The Cyclotomic Fast Fourier Transforms (CFFTs) are preferred due to low multiplicative complexity. The approach used is the decomposition of the arbitrary polynomial into a sum of linearized polynomials. Also, Common Subexpression Elimination (CSE) algorithm is used to reduce the additive complexity of the architecture. By using CSE algorithm, the design with reduced operational complexity has been described.
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Design of Field Irrigation Multi-purpose Control Device Based on Idle Work Compensation
Статья научная
In irrigation of a farm well, the power factor of the load is low when the motor operates, because of the long low voltage lines. Long-term use will cause a lot of energy waste. To this question, this paper presents a new type intelligent controller to save irrigation water and energy. The principle of the new type field irrigation intelligent controller that based on AT89LV52 single chip is introduced in this paper. The integration of the low pressure electrical energy reactive compensation control and IC card prepaid multi-user three-phase watt-hour meter management is realized in the system. The energy conservation control, using the low pressure electrical energy reactive compensation, and prepaid multi-user three-phase watt-hour meter are used to realize the saving water management. Who inserts the card who irrigates. The design of hardware circuit, software flow, and experiment results are presented in detail. The results of testing and preproduction in Zibo Billion Electron Co., Ltd show that the design technique of integration controller is novel, and the system has the characteristics of controlling efficiency by reactive compensation, accurate measurement of electrical energy, flexible power consumption management with IC card prepaid multi-users, saving water , low cost, and so on. Therefore, it is especially suitable for power consumption and water resources management in rural well irrigation. The new type intelligent controller can effectively reduce the cost of irrigation and promote rural economic development.
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Design of Type 2- interval fuzzy PID controller for CSTR
Статья научная
This paper proposes Type 2- Interval Fuzzy Proportional–Integral–Derivative (T2IFPID) controller for a non-linear system. Type 2- Interval fuzzy logic controller (T2IFLC) is self-possessed in such a way that it is an autonomous process. To decipher the influence, the impression of uncertainty on the controller execution to two different types of curves are outlined i.e. aggressive control curve and smoother control curve. Popov-Lyapunov approach is used to define the stability of the framework.
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Design of a Novel Shape Signature by Farthest Point Angle for Object Recognition
Статья научная
An overview of state of art in computerized object recognition techniques regarding digital images is revised. Advantages of shape based techniques are discussed. Importance of "Fourier Descriptor" (FD) for the shape based object representation is described. A survey for the available shape signature assignment methods with Fourier descriptors is presented. Details for the design of shape signature containing the crucial information of corners of the object are depicted. A novel shape signature is designed basing on the Farthest Point Angle (FPA) which corresponds to the contour point. FPA signature considers the computation of the angle between the line drawn from each contour point and the line drawn from the farthest corner point. Histogram for each 15o angle conceiving the information of the object is constructed. FPA signature is evaluated for three standard databases; viz., two in Kimia {K-99, K-216} and one in MPEG CE-1 Set B. The performance of the present FPA method estimated through recognition rate, time and degree of matching and is found to be higher.
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Design of a Video Summarization Scheme in the Wavelet Domain Using Statistical Feature Extraction
Статья научная
The marine researchers analyze the behaviors of fish in the sea by manually viewing the full video for their research activity. Searching events of interest from a video database is a time consuming and tedious process. Video summary refers to representing the whole video using few frames. The objective of this work is to design and develop a statistical video summarization to perform the automatic detection of events of interest in underwater video. In this proposed work, a video is partitioned into adjacent and non-overlapping datacubes. Then, the video frames are transformed into wavelet sub-bands and the standard deviation between two consecutive frames is computed. Pixels of interest in frames are identified using threshold values. Key frames are identified using Local Maxima and Local Minima. The proposed work effectively detects even the movement of small water bodies such as crabs which is not detected using the existing methods. Finally, this paper presents the experimental results of proposed method and existing methods in terms of metrics that measure the valid of the work.
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Статья научная
In recent years, near threshold computing is becoming a promising solution to achieve minimum energy consumption. In this paper, the Dynamic Threshold body MOS (DTMOS) technique is assessed in the context of 10T full subtractor circuit designed to operate in the near threshold region. The performance parameters – Energy, power, area, delay, and EDP were computed and compared with the conventional CMOS (C-CMOS) Full subtractor. The simulations were performed using cadence 90 nm technology with Ultra Low Voltage (ULV) of 0.3V. The results have been shown that the proposed 10T full subtractor circuit with DTMOS scheme achieves more than 18% savings in delay, 26% savings in energy consumption and 39% savings in EDP in comparison with the conventional CMOS configuration and other hybrid counterparts.
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Detecting Video Inter-Frame Forgeries Based on Convolutional Neural Network Model
Статья научная
In the era of information extension today, videos are easily captured and made viral in a short time, and video tampering has become more comfortable due to editing software. So, the authenticity of videos becomes more essential. Video inter-frame forgeries are the most common type of video forgery methods, which are difficult to detect by the naked eye. Until now, some algorithms have been suggested for detecting inter-frame forgeries based on handicraft features, but the accuracy and processing speed of those algorithms are still challenging. In this paper, we are going to put forward a video forgery detection method for detecting video inter-frame forgeries based on convolutional neural network (CNN) models by retraining the available CNN model trained on ImageNet dataset. The proposed method based on state-the-art CNN models, which are retrained to exploit spatial-temporal relationships in a video to detect inter-frame forgeries robustly and we have also proposed a confidence score instead of the raw output score based on these networks for increasing accuracy of the proposed method. Through the experiments, the detection accuracy of the proposed method is 99.17%. This result has shown that the proposed method has significantly higher efficiency and accuracy than other recent methods.
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Статья научная
The extraction of heart sound component from a composite signal of heart and lung is a quite challenging task in phonocardiogram signal analysis the first heart sound (S1) and the second heart sound (S2), produced by the closing of the atrioventricular valves and the closing of the semilunar valves, respectively, are the fundamental sounds of the heart. To accomplish this task a novel framework with intrinsic time scale decomposition (ITD) is designed. The capture of the PCG signal frequently hides the detection of the third heart sound (S3), which is necessary to identify cardiac failures. To separate S3, ITD method is deployed to enable signal decomposition into certain levels. Next, by applying smoothed pseudo-Wigner Ville distribution (SWVD) with reassignment, the location of S3 is detected. The proposed method is performed on 36 combinations consists of 144 cardiac cycles containing S3 obtained from different online databases. In comparison to existing approaches, the proposed work separates the S3 from other heart and lung sounds and the proposed method obtained the detection accuracy of S3 as 95.4%, which proves the superiority with other methods.
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Detection and classification of signage’s from random mobile videos using local binary patterns
Статья научная
The Traffic-Sign detection and recognition plays significant role in the design of autonomous driverless cars for navigation purpose as well as to assist a driver for alerting and educating him about the tracked signage on the road side. The main objective of this paper is to highlight an automatic process of detection of Region Of Interest (ROI) which marks or isolates signage’s from color video streams and performs classification of automatically detected signage’s based on support vector machine (SVM) classifiers trained over Local Binary Pattern (LBP) features. The training dataset was captured through 13 mega pixel mobile camera in different illumination and light conditions and due to randomness the data base complexity is very high. The robustness of the proposed system is measured on the bases its of capability of automatic detection and classification of ROI in a given video stream and backed with a comprehensive result analysis presented in this piece of work.
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Detection of Rows in Agricultural Crop Images Acquired by Remote Sensing from a UAV
Статья научная
Detection of rows in crops planted as rows is fundamental to site specific management of agricultural farms. Unmanned Aerial Vehicles are increasingly being used for agriculture applications. Images acquired using Low altitude remote sensing is analysed. In this paper we propose the detection of rows in an open field tomato crop by analyzing images acquired using remote sensing from an Unmanned Aerial Vehicle. The Unmanned Aerial Vehicle used is a quadcopter fitted with an optical sensor. The optical sensor used is a vision spectrum camera. Spectral-spatial methods are applied in processing the images. K-Means clustering is used for spectral clustering. Clustering result is further improved by using spatial methods. Mathematical morphology and geometric shape operations of Shape Index and Density Index are used for spatial segmentation. Six images acquired at different altitudes are analysed to validate the robustness of the proposed method. Performance of row detection is analysed using confusion matrix. The results are comparable for the diverse image sets analyzed.
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Статья научная
An Iterative method of individual nameplate detection using color images acquired from a high position is proposed for guidance of nighttime vehicles and other similar purposes. Segmentation is a very critical and difficult stage to accomplish in computer aided detection systems. Fundamentally the method contains iterative automatic thresholding and selecting the best threshold value which is applied to the original or enhanced dark night images. The main focus of the iteration based threshold to distinguish the image of the background and foreground. This method was tested on an actual outdoor vehicle images and results obtained from automatic thresholding of the experimental images are showing the validity of the method.
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Detection of different brain diseases from EEG signals using hidden markov model
Статья научная
The brain imaging device, Electroencephalography (EEG) provides several advantages over other brain signals like Functional Near-infrared Spectroscopy (fNIRS) and Functional Magnetic Resonance Imaging (fMRI). It is non-invasive and easily applicable. EEG provides high temporal resolution with a low setup cost. EEG signals of several subjects which record electric potential caused by neurons firing in the brain are undergone a Hidden Markov Model (HMM) classification technique. We are particularly interested to detect the brain diseases from EEG signals by an HMM probabilistic model. This HMM model is built with a given initial probability matrix of five different states, namely, epilepsy, seizure, dementia, stroke and normality. The transition probability matrix is updated after each iteration of parameter estimation using Baum-Welch algorithm (B-W algorithm).
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Статья научная
In this study; values obtained through the analysis of blood samples, taken under laboratory conditions, from patients diagnosed with fibromyalgia syndrome and healthy subjects and the sympathetic skin response parameters were used. With the aim of classifying verbal pain scale, which is one of the psychological test scores used for fibromyalgia syndrome diagnosis; relation between the sympathetic skin response effect on other test data and the verbal pain scale were reviewed by using different conditions of available data. Within this framework, three different algorithms were used for classification with high accuracy rates. These algorithms are: Multi-Layer Feed-Forward Neural Networks, Probabilistic Neural Network and Radial Basis Function Neural Network. For Multi-Layer Feed-Forward Neural Networks classification algorithm, classification was done with three different training algorithms, Levenberg-Marquardt back propagation, Resilient back propagation and the Scaled conjugate gradient back propagation and the results were compared elaborately. Based on the results, by using all variables the following accuracy rates were obtained: 68.2% accuracy with Levenberg-Marquardt training algorithm, 77.3% accuracy with the Resilient back propagation training algorithm, and 68.18% accuracy with the Scaled conjugate gradient training algorithm. These success rates show that there is a relationship between verbal pain scale, sympathetic skin response and other test data.
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Determination of osteoarthritis using histogram of oriented gradients and multiclass SVM
Статья научная
Knee Osteoarthritis is most ordinary kind of joint inflammation, which often occurs in one or both the knee joints. Osteoarthritis is additionally called as 'wear and tear' process of joint that results in dynamic disintegration of articular cartilage. Cartilage is smooth substantial layer that ensures movement to occur effortlessly. In Osteoarthritis, the cartilage is inclined towards the destruction as it loses elasticity and becomes brittle. Osteoarthritis is regularly investigated from radiographic evaluation after clinical examination. In any case, a visual evaluation made by the restorative physician depends on experience that varies subjectively and is profoundly reliant on their experience. Subsequently, in order to make diagnostic process more systematic and reliable, evolution of imaging based analysis for early recognition of Osteoarthritis is required. The objective of this study is to develop a machine vision approach for investigation of Knee Osteoarthritis using region based and active shape model. The computation involves histogram of oriented gradient (HOG) method. The processed HOG elements are computed using multiclass SVM for evaluating Osteoarthritis based on Kellgren and Lawrence (KL) grading system. The classification rate of 97.96% for Grade-0, 92.85% for Grade-1, 86.20% for Grade-2, 100% for Grade-3 & Grade-4 is obtained. The results are promising and competitive which are validated by the medical experts.
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Development of Algorithm to Reduce Shadow on Digital Image
Статья научная
In this paper, two shadow reduction algorithms have been proposed and implemented using CIE Lab color space. The task of performing shadow reduction is done by executing shadow detection, shadow removal and lastly shadow edge correction in a sequential order. The first proposed algorithm is implemented based on pixel illumination and color information meanwhile the second algorithm is carried out via thresholding of one or more CIE Lab color space channels. The outputs from both proposed algorithms are compared in terms of shadow detection accuracy and required processing period. The proposed methods shown some promising results.
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