International Journal of Image, Graphics and Signal Processing @ijigsp
Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1181
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
Accurate and efficient segmentation and classification are indispensable for the early diagnosis and treatment of skin cancer, a common and potentially fatal condition. Combining the UNet architecture with Auto Encoders for robust skin cancer segmentation, followed by binary cascade Convolutional Neural Networks (CNNs). In this text, we present a novel method for accurately classifying melanoma and basal cell carcinoma. Existing models are limited in their ability to achieve high precision, accuracy, and recall rates while maintaining a high Peak Signal-to-Noise Ratio (PSNR) for accurate image reconstruction, which necessitates this research. Our proposed model overcomes these limitations and performs exceptionally well on datasets: ISIC, HAM10000, PH2 Dataset, and Dermofit Image Libraries. When UNet and Auto Encoders are used, the advantages of both architectures are combined. The UNet architecture, renowned for its superior performance in image segmentation tasks, provides a solid foundation for separating skin cancer regions from surrounding tissue. The Auto Encoder component simultaneously facilitates feature extraction and image reconstruction, leading to improved representation learning and segmentation results. Utilizing the complementary capabilities of these models, our method improves the accuracy and efficiency of skin cancer segmentations. Using binary cascade CNNs for classification also improves our model's performance. The binary cascade architecture employs a hierarchical classification method that iteratively improves classification choices at each stage. This facilitates the differentiation between basal cell carcinoma, melanoma, and melanocytic nevi, resulting in highly accurate and trustworthy predictions. Extensive experiments were conducted on the ISIC, HAM10000, PH2 Dataset, and Dermofit Image Library to evaluate the performance of our proposed model. The achieved precision of 99.2%, accuracy of 98.3%, recall of 98.9%, and PSNR greater than 42dB demonstrate the superior functionality and effectiveness of our strategy. These results suggest that our model has a great deal of potential for assisting dermatologists in the early identification and classification of skin cancer, ultimately leading to improved patient outcomes. The combination of UNet with Auto Encoders and binary cascade CNNs has proven effective for segmenting and classifying skin cancer. Our proposed model outperforms current methods in terms of precision, accuracy, recall, and PSNR, demonstrating its potential to have a significant impact on the field of dermatology and aid in the early detection and treatment of skin cancers.
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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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Diabetic Kidney Disease Prediction Using Hybrid Deep Learning Model
Статья научная
Diabetic Kidney Disease (DKD) was recently identified as a significant microvascular consequence of diabetes. Many researchers are working on the classification of DKD from non-diabetic kidney disease (NDKD), but the required accuracy has not been achieved yet. This study aims to enhance diagnostic accuracy using a hybrid Deep Learning (DL) method, Convolutional Neural Network, and Long Short-Term Memory (CNN-LSTM). Clinical data on DKD were collected and preprocessed to address issues like missing values, duplicates, and outliers. Key preprocessing steps included imputation, z-score, min-max normalization, and feature encoding. Feature selection based on a correlation matrix identified the most relevant variables. Subsequently, both CNN-LSTM and Convolutional Neural Network (CNN) models were trained using processed data, with identical hyperparameters, as detailed in the methodology. Evaluation metrics such as Accuracy, Sensitivity, Specificity, Precision, F1-score, and ROC plots were employed to assess model performance. The CNN-LSTM model achieved a high Accuracy of 98%, surpassing the CNN model’s Accuracy of 96.5%. In addition to accuracy, all metrics showed that the CNN-LSTM outperformed the CNN.
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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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