Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1092
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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Статья научная
Method of symmetric component is used in analysis of disturbances (short circuits and disturbances) and can be verified by computer simulation and measurement. It is based on possibility of making calculations simple by separating a three-phase asymmetric system into three symmetric systems and three single-phase schemes. It is very important for three-phase electrical networks with linear parameters and the same frequency in the network. The transition of quantities (ems, voltages and currents ) from the asymmetric domain of a three-phase system to the symmetric domain is performed using transformation matrices. Expressions determined in the system of symmetric components are then superimposed on expressions corresponding to conditions of asymmetric system, and superposition is correct if electric quantities are of simple-periodic functions. The paper presents a new method based on analysis using symmetric component methods and diagnostic algorithms for the assessment of the most common disturbances in power grids. The adapted part of the MATLAB package psb.abc,part.mdl was used for method verification, and the obtained results in the form of diagrams and values of diagnostic functions arranged in the form of tables confirm the applicability of the proposed new diagnostic algorithm for analysis and assessment of steady states and disturbances in electrical networks. The proposed diagnostic algorithm enables the realization of the maximum number of diagnostic functions on the basis of which a scheme for diagnosing disorders with classical diode elements or a more modern scheme with microprocessor components can be realized.
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Digital Image Texture Classification and Detection Using Radon Transform
Статья научная
A novel and different approach for detecting texture orientation by computer was presented in this research work. Many complex real time problem example detection of size and shape of cancer cell, classification of brain image signal, classification of broken bone structure, detection and classification of remote sensing images, identification of foreign particle in universe, detection of material failure in construction design, detection and classification of textures in particularly fabrications etc where edge detection and both vertical and horizontal line detection are essential. Thus researches need to develop different algorithm for this above complex problem. It is seen from literature that conventional algorithm DCT, FFT are all highly computational load and hence impossible task to implemented in hardware. These difficulties were solved in this particular research work by applying DWT and radon transform. It was seen from the simulation result that with very high computational load the entire algorithm takes very less CPU time and proved its robustness.
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Digital Method of Automated Non-destructive Diagnostics for High-power Magnetron Resonator Blocks
Статья научная
The paper reveals the problem of the lack of standard non-destructive diagnostic methods for high-power microwave devices aimed at regeneration. The issue is understudied and requires further research. The conducted analysis of state of the art on the subject area exhibited that image processing was used to specify the examined object's target characteristics in a wide range of research. Having summarized the considered image comparison methods on the subject area of this work, the authors formulated several requirements for the selected image analysis method based on the automated non-destructive diagnosis of resonator units for high-power magnetrons. The primary requirement is using non-iterative algorithms; the second condition is a chosen method of image analysis, and the third option is the number of pixels for a processed image. It must significantly exceed the number of descriptors required for making a decision. Guided by the analysis results and based on the results of previous studies conducted by the authors, the algorithm for identifying a defect in the resonator unit of a microwave device based on the image of the frequency-azimuthal distribution for the probing field phase difference expressed by the Zernike moments is proposed. MATLAB R14a was used as a modeling environment. The descriptor vector was restricted to the Zernike moments, including the 7th order. The work is interdisciplinary and written at the intersection of technical diagnostics, microwave engineering, and digital image processing.
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Digital image watermarking using DWT and FWHT
Статья научная
Digital Image Watermarking is a process of embedding a known data into an Image. Several techniques are developed to embed a watermark into a known cover image. Digital image watermarking provides security like copyright protection, ownership, and authentication to the images. In this paper, a new robust image watermarking and the watermark extraction algorithm is proposed using DWT-FWHT transformation. The watermarking algorithm further calculates the peak-signal to noise ratio(PSNR) values on the selected images and the extraction process involves the process of correlating the extracted watermark with the original watermark for various sub-bands of discrete wavelet transformation. The digital image watermarking algorithms using discrete wavelet transformation have been identified to be more prevalent as compared to those with the other watermarking algorithms. This is due to the wavelets high spatial localization, frequency spread, and multi-resolution characteristic features which are much similar to that of the theoretical models of the human visual system.
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Discrete complex fuzzy transform based face image recognition method
Статья научная
In this paper, a novel discrete complex fuzzy transform (DCFT) and the proposed DCFT based facial image recognition method is presented. The presented DCFT consists of histogram extraction, peak points of histogram calculation and images construction. 3 real and 3 complex images are constructed using DCFT. Also, 3 angular images and 3 vector image are calculated using the real and complex images. To create real and complex images, polynomial and smith fuzzy sets are used in this paper. Briefly, 12 image are constructed using DCFT. In order to demonstrate effect of the proposed DCFT, face images data sets and local binary pattern (LBP) are used to create facial image recognition method. In this method, LBP is applied on the each DCFT image and 12 x 256 size of feature are extracted. Also, maximum pooling is applied on this feature set to obtain 256 size of feature. In the classification phase, support vector machine (SVM) and k nearest neighborhood (KNN) classifiers are used. The comparisons clearly demonstrate that the proposed DCFT is increased facial image recognition capability.
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Статья научная
Biometrics has gained significant popularity for individual identification in the last decades as a necessity of supporting especially the law enforcement and personal authentication required applications. The face is one of the distinctive biometrics that can be used to identify an individual. Henceforth, Face Recognition (FR) has attracted the great interest of the scientists and academicians. One of the most popular methods preferred for FR is extracting textual features from face images and subsequently performing classification according to these features. A substantial portion of the previous texture analysis and classification studies have based on extracting features from Gray Level Co-occurrence Matrix (GLCM). In this study, we present an alternative method that utilizes Gray Level Total Displacement Matrix (GLTDM) which holds statistical information about the Discrete Wavelet Transform (DWT) of the original face image. The approximation and three detail sub-bands of the image are first calculated. GLTDMs that are specific to these four matrices are subsequently generated. The Haralick features are extracted from those generated four GLTDMs. At the following stage, a new joint feature vector is formed using these four groups of Haralick features. Lastly, extracted features are classified by using K-NN algorithm. As demonstrated in the simulation results, the proposed approach performs promising results in the context of classification.
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Displaying Images and Their Characteristics from Websites on Users Computers
Статья научная
The subject of the research in this scientific paper are images on the websites, with special emphasis on displaying images chosen by the Web designer, along with its characteristic, on computers of various users. In addition, users can have different operating systems, different browsers, and different preferences in terms of their computers settings. An overall direction for using images and their characteristics when designing web pages, as well as some advice and opinions on the same topic are presented here. After that, several problems which arise from displaying images on the web pages of the computer of users are analyzed, for which a few solutions for the problems, as well as recommendations on which solution when to be chosen are also given in this text. A problem with a speed for loading web pages in correlation with size of images on those pages is studied as well. Then, problems with a speed for loading web pages in correlation with number of images on the page, problems with loading speed of second image on rollover, problems with a speed for loading web pages in correlation with size of background image, problems with texture in vertical bars used for background in web pages, and problems with users monitor size and background image are also analyzed. Finally, the problem with displaying the page without specifying image height and width is also considered.
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Dominant Frequency Enhancement of Speech Signal to Improve Intelligibility and Quality
Статья научная
In mobile devices, perceived speech signal deteriorates significantly in the presence of near-end noise as the signal arrives directly at the listener's ears in a noisy environment. There is an inherent need to increase the clarity and quality of the received speech signal in noisier environment. It is accomplished by incorporating speech enhancement algorithms at the receiver end. The objective is to improve the intelligibility and quality of the speech signal by dynamically enhancing the speech signal when the near-end noise dominates. This paper proposes a speech enhancement approaches by inculcating the threshold of hearing and auditory masking properties of the human ear. Incorporating the masking properties, the speech samples that are audible can be obtained. In low SNR environments, selective audible samples can be enhanced to improve the clarity of the signal rather than enhancing every loud sample. Intelligibility and quality of the enhanced speech signal are measured using Speech Intelligibility Index and Perceptual Evaluation of Speech Quality. Experimental results connote the intelligibility and quality improvement of the speech signal with the proposed method over the unprocessed far-end speech signal. This approach is efficient in overcoming the deterioration of speech signals in a noisy environment.
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Driver's Face Tracking Based on Improved CAMShift
Статья научная
The statistic shows that the number of casualty increase in every year due to road accident related to driver drowsiness. After long journey or sleepless night, vehicle driver will perform some bio-features with regard to drowsiness on them face. It is self-evident that getting location information of head in continuous monitoring and surveillance system rapidly and accurately can help prevent many accidents, and consequently save money and reduce personal suffering. In this paper, according the real situation in vehicle, an improved CAMShift approach is proposed to tracking motion of driver’s head. Results from experiment show the significant performance of proposed approach in driver’s head tracking.
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Drone Detection from Video Streams Using Image Processing Techniques and YOLOv7
Статья научная
For ensuring the safety issues, a country should establish a secure monitoring system around the most important places. Due to the huge development in unmanned aerial vehicles (UAV), drone detection is a vital part of the safety monitoring system for reducing threats from neighboring countries or terrorist groups. This paper presents a deep learning-based drone detection method. A You Only Look Once (YOLO) v7 architecture is used to train on the dataset. The training dataset consists of drone images in various environments. The trained model was tested on multiple videos of drones from YouTube. Experimental results demonstrate that the model exhibited a recall of 0.9656 and a precision of 0.9509. In addition, the performance of the model compares with the state-of-art models with YOLOv8, YOLO-NAS, Faster-RCNN architectures and it outperforms the other models by maintaining a more stable precision and recall curve.
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