International Journal of Image, Graphics and Signal Processing @ijigsp
Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1128
Статья научная
Lung cancer is distinguished by presenting one of the highest incidences and one of the highest rates of mortality among all other types of cancers. Detecting and curing the disease in the early stages provides the patients with a high chance of survival. In order to help specialists in the search and recognition of the lung nodules in tomography images, a good number of research centers have been developed in computer-aided detection (CAD) systems for automating the procedures. This work aims at detecting lung nodules automatically through computerized tomography images. Accordingly, this article aim at presenting a method to improve the efficiency of the lung cancer diagnosis system, through proposing a region growing segmentation method to segment CT scan lung images and, then, cancer recognition by FIS (Fuzzy Inference System). The proposed method consists of three steps. The first step was pre-processing for enhancing contrast, removing noise, and pictures less corrupted by Linear-Filtering. In second step, the region growing segmentation method was used to segment the CT images. In third step, we have developed an expert system for decision making which differentiates between normal, benign, malignant or advanced abnormality findings. The FIS can be of great help in diagnosing any abnormality in the medical images. This step was done by extracting the features such as area and color (gray values) and given to the FIS as input. This system utilizes fuzzy membership functions which can be stated in the form of if-then rules for finding the type of the abnormality. Finally, the analysis step will be discussed and the accuracy of the method will be determined. Our experiments show that the average sensitivity of the proposed method is more than 95%.
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Matrix-based Kernel Method for Large-scale Data Set
Статья научная
In the computation process of many kernel methods, one of the important step is the formation of the kernel matrix. But the size of kernel matrix scales with the number of data set, it is infeasible to store and compute the kernel matrix when faced with the large-scale data set. To overcome computational and storage problem for large-scale data set, a new framework, matrix-based kernel method, is proposed. By initially dividing the large scale data set into small subsets, we could treat the autocorrelation matrix of each subset as the special computational unit. A novel polynomial-matrix kernel function is then adopted to compute the similarity between the data matrices in place of vectors. The proposed method can greatly reduce the size of kernel matrix, which makes its computation possible. The effectiveness is demonstrated by the experimental results on the artificial and real data set.
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Mechanism and Algorithm for Indirect Schema Mapping Composition
Статья научная
There are a large number of indirect schema mappings between peers in the network. To improve the efficiency of data exchange and queries, indirect mappings are needed to be composed. Direct mappings can be derived directly by the constraints defined between schemas, but not for indirect mappings’ composition. Defined the combination operations of schema elements in indirect mappings, and gave the expression of indirect mappings. Analyzed the composition of indirect mappings, and proposed a strategy, named schema element back, to solve the problem of indirect mapping composition, and gave the indirect mapping composition generation algorithm based on such strategy. Experiments showed that indirect mapping composition can improve the efficiency of data exchange, and compared with other non-full mapping composition generation algorithms, and indirect mapping composition generated by our algorithm based on schema element back strategy can completely eliminate the infection of media schema with no reduction of the composition efficiency.
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Статья научная
Medical imaging has become a vital part of the early detection, diagnosis, and treatment of many diseases. That’s why image denoising is considered as a crucial pre-processing step in medical imaging to restore the original image from its noisy circumstance without losing image features, such as edges, corners, and other sharp structures. Ultrasound (US), Computed Tomography (CT), and Magnetic Resonance (MR) are the most widely used medical imaging techniques that are often corrupted by hazardous noises, namely, speckle, salt and pepper, Poisson, and Gaussian. To remove noises from medical images, researchers have proposed several denoising methods. Each method has its assumptions, merits, and demerits. In this paper, a detailed comparative analysis of different denoising filtering techniques, for example, median, Wiener, mean, hybrid median, Gaussian, bilateral, non-local means, and anisotropic diffusion are performed based on four widely-used image quality assessment (IQA) metrics, such as Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), Mean Absolute Error (MAE), and Structural Similarity Index (SSIM). The results obtained in this present work reveal that Gaussian, median, anisotropic diffusion, and non-local means filtering methods perform extraordinarily to denoise speckle, salt and pepper, Poisson, and Gaussian noises, respectively, from all US, CT, and MR images.
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Medical Image Denoising Using Bilateral Filter
Статья научная
Medical image processing is used for the diagnosis of diseases by the physicians or radiologists. Noise is introduced to the medical images due to various factors in medical imaging. Noise corrupts the medical images and the quality of the images degrades. This degradation includes suppression of edges, structural details, blurring boundaries etc. To diagnose diseases edge and details preservation are very important. Medical image denoising can help the physicians to diagnose the diseases. Medical images include MRI, CT scan, x-ray images, ultrasound images etc. In this paper we implemented bilateral filtering for medical image denoising. Its formulation & implementation are easy but the performance of bilateral filter depends upon its parameter. Therefore for obtaining the optimum result parameter must be estimated. We have applied bilateral filtering on medical images which are corrupted by additive white Gaussian noise with different values of variances. It is a nonlinear and local technique that preserves the features while smoothing the images. It removes the additive white Gaussian noise effectively but its performance is poor in removing salt and pepper noise.
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Method of Diagnostics of Multichannel Data Transmission System
Статья научная
The redundancy of a multichannel data transmission system increases its reliability. During the operation of the system, it is necessary to diagnose and switch failed channels. To solve this problem, the set of input signals of the system is considered as a vector signal, whose scalar components are the coordinates of the vector in a given dimensional space. The diagnosis is performed using a scalar criterion, whose relative simplicity is ensured by the linearity of the signal transformations applied. To minimize the total probability of diagnostic error, the task of optimizing the tolerance on the diagnostic parameter is solved. The possibility of technical implementation of the proposed method is shown based on matrix transformations of the system's input and output signals. The system efficiency was assessed according to the "reliability-cost" criterion. Scientific novelty of the work consists in the fact that analytical expressions for matrix transformations of input and output vector signals of a multichannel data transmission system have been developed. Realization of these transformations provides diagnostics of the system according to the developed scalar criterion both in the test mode and in the mode of functioning as intended.
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Статья научная
This paper presents a natural language text description from video content activities. Here it analyzes the content of any video to identify the number of objects in that video content, what actions and activities are going on has to track and match the action model then based on that generate the grammatical correct text description in English is discussed. It uses two approaches, training, and testing. In the training, we need to maintain a database i.e. subject-verb and object are assigned to extract features of images, and the second approach called testing will automatically generate text descriptions from video content. The implemented system will translate complex video contents into text descriptions and by the duration of a one-minute video with three different object considerations. For this evaluation, a standard DB of YouTube is considered where 250 samples from 50 different domains. The overall system gives an accuracy of 93%.
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Metrological Complex for Electromagnetic Field Forming and Study of Electromagnetic Environment
Статья научная
The article is devoted to the problems of constructing electromagnetic field with given parameters and both to the study of electromagnetic environment. For solving the problems, the corresponding theoretical material is presented. The functional relationships are considered that make it possible to construct the device for generating electromagnetic field with specified parameters in circular orthogonal polarization basis. The block diagram, which can ensure the specified field forming with acceptable errors are synthesized. Measurement of radiation characteristics, including polarization characteristics, requires the appropriate orientation of the receiving antenna to the direction of wave propagation. Corresponding algorithm and antenna system for this purpose is proposed. The study of the field polarization characteristics formed using the ring antenna elements is carried out. It is shown that in the broad frequency band, the ring elements can be replaced with spiral radiators, as well as that the antenna system for electromagnetic waves reception and their subsequent decomposition in circular polarization orthogonal basis, must contain at least eight antenna elements. Applied spiral flat antenna elements ensure the low level of cross-polarization due to the matched load on the spiral end, which is one of the conditions for successful polarization analysis. Besides, a device for polarization analysis of incident electromagnetic waves and the algorithm for measurement of the effective reflection area are considered.
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Статья научная
Human age recognition from face image relies highly on a reasonable aging description. Considering the disparate and complex face-aging variation of each person, aging description needs to be defined carefully with detailed local information. However, aging description relies highly on the appropriate definition of different aging-affiliated textures. Wrinkles are considered as the most discernible textures in this regard owing to their significant visual appearance in human aging. Most of the existing image-descriptors, however, fail short to preserve diverse variations of wrinkles, such as a) characterizing stronger and smoother wrinkles, appropriately, b) distinguishing wrinkles from non-wrinkle patterns, and c) characterizing the proper texture-structures of the pixels belonging to the same wrinkle. In this paper, we address these issues by presenting a new local descriptor, Local Edge-Prototypic Pattern (LEPP) with the notion that LEPP preserves different variations of wrinkle-patterns appropriately in representing the aging description. In the coding, LEPP sets prototypic restrictions for each neighboring pixel using their relation with center pixel when they belong to an inlying-edge, and utilize such restrictions, afterwards, to prioritize specific neighbors showing significant edge-signature. This strategy appropriately encodes the inlying edge structure of aging-affiliated textures and simultaneously, avoids featureless texture. We visualize the stability of LEPP in terms of its robustness under noise. Our experiments show that LEPP preserves discernible aging variations yielding better accuracies than the state-of-the-art methods in popular age-group datasets.
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Minutiae Distances and Orientation Fields Based Thumbprint Identification of Identical Twins
Статья научная
The twins are classified into two categories namely fraternal and identical twins. Fraternal twins differ in face structures and DNA sequences but, identical twins have the same face structure and share same DNA sequence. Therefore, it is difficult to identify identical twins on the basis of their faces and DNA sequences. In this research paper, we have introduced a new approach for identifying identical twins on the basis of minutiae coordinates, orientation angles, and minutiae distances of their thumbprint images. We tested the proposed method on thumbprint images of an identical twin pair generated by using Incept H3 T&A Terminal and fifty pairs of identical twins of FVC04, and FVC06 datasets. We have found that the proposed approach is superior, and robust in comparison to existing techniques in terms of accuracy, efficiency, and storage space.
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Mobile-Based Skin Disease Diagnosis System Using Convolutional Neural Networks (CNN)
Статья научная
This paper presents a design and development of an Artificial Intelligence (AI) based mobile application to detect the type of skin disease. Skin diseases are a serious hazard to everyone throughout the world. However, it is difficult to make accurate skin diseases diagnosis. In this work, Deep learning algorithms Convolution Neural Networks (CNN) is proposed to classify skin diseases on the HAM10000 dataset. An extensive review of research articles on object identification methods and a comparison of their relative qualities were given to find a method that would work well for detecting skin diseases. The CNN-based technique was recognized as the best method for identifying skin diseases. A mobile application, on the other hand, is built for quick and accurate action. By looking at an image of the afflicted area at the beginning of a skin illness, it assists patients and dermatologists in determining the kind of disease present. Its resilience in detecting the impacted region considerably faster with nearly 2x fewer computations than the standard MobileNet model results in low computing efforts. This study revealed that MobileNet with transfer learning yielding an accuracy of about 85% is the most suitable model for automatic skin disease identification. According to these findings, the suggested approach can assist general practitioners in quickly and accurately diagnosing skin diseases using the smart phone.
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Статья научная
In this paper, we propose a synthesis method for synthesizing the reconfigurable multiple patterns with the minimum number of antenna elements based on the state space model. The proposed method is to obtain the common element locations for the multiple patterns using fewer antenna elements within desired performance bounds. The proposed approach introduces the state-space method to represent the multiple patterns and then uses the multiple pattern data to construct a combined Hankel matrix which is used to estimate the model parameters from which the number of elements and the common element locations can be extracted. Numerical results show the effectiveness of the proposed methods.
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Modeling and Simulating Mutual Testing in Complex Systems by Using Petri Nets
Статья научная
The paper tackles the problem of performing mutual testing in complex systems. It is assumed that units of complex systems can execute tests on each other. Tests among system units are part of system diagnosis that can be carried out both before and during system operation. The paper considers the case when tests are executed during system operation. Modelling and simulating mutual tests will allow evaluation of the efficiency of using joint testing in the system. In the paper, the models that use Petri Nets were considered. These models were used for simulating the execution of tests among system units. Two methods for performing such simulations were evaluated and compared. Recommendations for choosing a more appropriate way were made. Simulation results have revealed minor model deficiencies and possible implementation of mutual testing in complex systems. Improvement of the model was suggested and assessed. A recommendation for increasing the efficiency of system diagnosis based on joint testing was made.
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Modeling and Simulation of integrated steering and braking control for vehicle active safety system
Статья научная
Active chassis systems like braking, steering, suspension and propulsion systems are increasingly entering the market. In addition to their basic functions, these systems may be used for functions of integrated vehicle dynamics control. An experimental platform which aims to study the integration control of steering and braking is designed due to the research requirement of vehicle active safety control strategy in this paper. A test vehicle which is equipped with the systems of steer-by-wire and brake-bywire is provided and the Autobox, combined with Matlab/simulink and MSCCarsim, is used to fulfill the RCP (Rapid Control Prototyping) and HIL (Hardware-in-loop). The seven-freedom vehicle model is constructed first and the approach of vehicle parameters estimation based on the Extended Kalman Filter (EKF) is proposed. Testing the vehicle state through the sensor has its own disadvantage that the cost is high and easily affected by environment outside. To find a actual method of receiving the vehicle state using the ready-made sensors in vehicle, the researchers put forward various estimation method, of which have advantages and disadvantages. Based on the above, this paper applies the EKF to estimate the vehicle state, making the actual estimation come true. The primary control methods and controller designment is carried out to prove the validation of the platform.
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Статья научная
Speech quality of VoIP system is degraded due different network layer problems such as packet loss delay and jitter and external noise. This paper compares the quality of speech signal that is implemented on digital signal processor using G.729 audio data compression algorithm with the ITU-T G.711 PCM coder implemented using modified digital filtering algorithm. PESQ (ITU-T P.862, Perceptual Evaluation of Speech Quality) is used to evaluate the performance. The results indicate that our proposed architecture has better performance.
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Статья научная
The article is devoted to the modified multidimensional Kalman filter with Chebyshev points development to solve the task of diagnosing and parring off failures in the measurement channels of complex dynamic objects automatic control system, which will provide a more accurate and reliable assessment of system state in the presence of outliers in the data. An implementation of the proposed modified multidimensional Kalman filter with Chebyshev points is proposed in the form of a modified recurrent neural network containing a failure diagnostics layer, a failure parry layer, a filtering and smoothing layer, and a results aggregation layer. This structure of the modified recurrent neural network made it possible to solve the main problems of the method of diagnosing and parring off failures of the measuring channels of complex dynamic objects automatic control system, such as diagnosing failures with an accuracy of 0.99802, fending off failures with an accuracy of 0.99796, and assessing the state of the system with an accuracy of 0.99798. It is proposed to use a modified loss function of a recurrent neural network as a general loss function for diagnostics, fault restoring and system state assessment, which makes it possible to avoid retraining when there are a large number of parameters or insufficient data. It has been experimentally proven that the loss function remains stable on both the training and validation data sets for 1000 training epochs and does not go beyond –2.5 % to +2.5 %, which indicates a low-risk overtraining or undertraining of the model. It has been experimentally confirmed that the use of a modified recurrent neural network in solving the task of diagnosing and parring off failures of the measuring channels of complex dynamic objects automatic control system is appropriate in comparison with a radial basis functions neural network and a multidimensional Kalman filter without a neural network implementation, based on metrics such as the root mean square deviation, mean absolute error, mean absolute percentage error, coefficient of determination for the accuracy of reproducing previous data, and coefficient of determination for the accuracy of predicting future values. For example, the value of the standard deviation of the modified recurrent neural network is 0.00226, which is 1.65 times less than the radial basis function neural network and 2.20 times less than the multidimensional Kalman filter without a neural network implementation.
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Статья научная
To prevent the loss of the yield of food crops and to attain sustainable agricultural growth, accurate detection of plant disease at an early stage is crucial. However, the extraction of crucial features from infected plant leaves to differentiate the properties associated with different diseases is a complex task, as the diseases exhibit huge variations, which insists on the need for developing precise disease detection. Hence in this research, the early detection of plant disease is performed by utilizing a Modified political optimization adapted deep Neural Network (MPO-adapted deep NN) model, in which the continuous learning capability of the deep NN classifier helps in the deeper analysis of the information in the image and identifies the plant disease more accurately. Identification of the plant disease posse’s challenges due to complexities present in the image and the neural networks effectively dwells with the complex relationships and the non-linear characteristics of the network help in achieving adaptability and makes the system more suitable for real-time applications. The main contribution relies on the modified political optimization algorithm that efficiently tunes the parameters of the deep NN classifier to analyze the disease patterns effectively and provides disease detection with high accuracy. Further, the Adaptive K-means algorithm is utilized for the effective segmentation of diseased parts, and the Grey level co-occurrence matrix (GLCM) features are extracted in the method that enhances the accuracy of the detection. When compared to the existing techniques, the MPO-adapted deep NN model attains high accuracy, sensitivity, and specificity values of 98.95%, 97.45%, and 98.95% for cotton leaf, 94.47%, 94.58%, 94.54% for cotton root, 99.10%, 99.10%, 99.10% for cotton stem, respectively concerning the k-fold. Analysis demonstrating the superiority of the research's metrics values measurement. When compared to existing methods, detecting the disease in cotton stems is very effective.
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Modified Sparseness Controlled IPNLMS Algorithm Based on l_1, l_2 and l_∞ Norms
Статья научная
In the context of Acoustic Echo Cancellation (AEC), sparseness level of acoustic impulse response (AIR) varies greatly in mobile environments. The modified sparseness-controlled Improved PNLMS (MSC-IPNLMS) algorithm proposed in this paper, exploits the sparseness measure of AIR using l1, l2 and l∞ norms. The MSC-IPNLMS is the modified version of SC-IPNLMS which uses sparseness measure based on l1 and l2 norms. Sparseness measure using l1, l2 and l∞ norms is the good representation of both sparse and dense impulse response, where as the measure which uses l1 and l2 norms is the good representation of sparse impulse response only. The MSC-IPNLMS is based on IPNLMS which allocates adaptation step size gain in proportion to the magnitude of estimated filter weights. By estimating the sparseness of the AIR, the proposed MSC-IPNLMS algorithm assigns the gains for each step size such that the proportionate term of the IPNLMS will be given higher weighting for sparse impulse responses. For a less sparse impulse response, a higher weighting will be given to the NLMS term. Simulation results, with input as white Gaussian noise (WGN), show the improved performance over the SC-IPNLMS algorithm in both sparse and dense AIR.
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Modified Streaming Format for Direct Access Triangular Data Structures
Статья научная
We define in this paper an extended solution to improve an Out-of-Core data structure which is the streaming format, by adding new information allowing to reduce file access cost, reducing the neighborhood access delay to constant time. The original streaming format is conceived to manipulate huge triangular meshes. It assumes that the whole mesh cannot be loaded entirely into the main memory. That's why the authors did not include the neighborhood in the file structure. However, almost all of the applications need the neighborhood information in the triangular structures. Using the original streaming format does not allow us to extract the neighborhood information easily. By adding the neighbor indices to the file in the same way as the original format, we can benefit from the streaming format, and at the same time, guarantee a constant time access to the neighborhood. We have adapted our new structure so that it can allow us to apply our direct access algorithm to different parts of the structure without having to go through the entire file.
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Morphological Multiscale Stationary Wavelet Transform based Texture Segmentation
Статья научная
Image segmentation is an important step in several computer vision applications. The segmentation of images into homogeneous and meaningful regions is a fundamental technique for image analysis. Textures occupy a vital role in a wide range of computer vision research fields; from microscopic images to images sent down to earth by satellites, from the analysis of multi-spectral scan images to outdoor scenes, all consist of texture. Although several methods have been proposed, less work has been done in developing suitable techniques for segmentation of texture images. After a careful and in-depth survey on wavelet transforms, the present study found that efficient numerical solutions in the signal processing applications can be found using Stationary Wavelet Transform (SWT). SWT is redundant, linear and shift invariant, that’s why it gives a better approximation than the DWT. In this paper a novel texture segmentation method based on “SWT and Textural Properties” is proposed. Multi scale SWT with Textural Properties and morphological treatment is used in the present study to detect fine edges from texture images for a fine segmentation.
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