Local Binary pattern (LBP) is a non-parametric descriptor that is used to study various local structures of an image. It is considered as simple and efficient texture operator for image analysis in challenging real-ti...
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The proceedings contain 48 papers. The topics discussed include: overview: 3D video from capture to display;speech emotion recognition using minimum extracted features;information technology as a strategic resource;el...
ISBN:
(纸本)9781538691885
The proceedings contain 48 papers. The topics discussed include: overview: 3D video from capture to display;speech emotion recognition using minimum extracted features;information technology as a strategic resource;electronic management in construction projects;image compression using contourlet transform;mammography image segmentation based on fuzzy morphological operations;enhancement of state estimation power system based hybrid algorithm;e-learning readiness assessment of medical students in University Of Fallujah;design and implementation of wireless controllers for oil tank using internet of things techniques;and recent trends in distributed online stream processing platform for big data: survey.
This paper focuses on solving a relevant and pressing safety issue on intercity roads. Two approaches were considered for solving the problem of traffic signs recognition;the approaches involved neural networks to ana...
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This paper focuses on solving a relevant and pressing safety issue on intercity roads. Two approaches were considered for solving the problem of traffic signs recognition;the approaches involved neural networks to analyze images obtained from a camera in the real-time mode. The first approach is based on a sequential imageprocessing. At the initial stage, with the help of color filters and morphological operations (dilatation and erosion), the area containing the traffic sign is located on the image, then the selected and scaled fragment of the image is analyzed using a feedforward neural network to determine the meaning of the found traffic sign. learning of the neural network in this approach is carried out using a backpropagation method. The second approach involves convolution neural networks at both stages, i.e. when searching and selecting the area of the image containing the traffic sign, and when determining its meaning. learning of the neural network in the second approach is carried out using the intersection over union function and a loss function. For neural networks to learn and the proposed algorithms to be tested, a series of videos from a dash cam were used that were shot under various weather and illumination conditions. As a result, the proposed approaches for traffic signs recognition were analyzed and compared by key indicators such as recognition rate percentage and the complexity of neural networks' learning process.
Brain morphometry derived from structural magnetic resonance imaging is a widely used quantitative biomarker in neuroimaging studies. In this paper, we investigate its usefulness for the Neurocognitive Prediction Chal...
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In this modem era, monitoring of industrial parameters like temperature reading of the respective machines, voltage supply to the equipment, inside environment of the industry, pressure level, inventory monitoring and...
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In this modem era, monitoring of industrial parameters like temperature reading of the respective machines, voltage supply to the equipment, inside environment of the industry, pressure level, inventory monitoring and management so on. Change in reading of the values can be a massive effect. Not only parameters like these but also the position of objects placed are also important to monitor. For an example, consider and oil or fuel drums that are placed one upon other in a room. Monitoring of these parameters has been changed day by day since past few years. Here we are using the software technologies like imageprocessing, machinelearning to monitor some of the industrial parameters. And also big data techniques to analyse the various sensor values for the prediction of future values or to know about the behaviour of the respective machine. This can also be extended to use machinelearning techniques to automate the complete system. The hardware part here is controlled by Arduino mega which acts like a centralized processing unit for the whole part system. The system is stable and also it is an effective way to monitor. This technique can bring new safety measures for small scale industries too. (C) 2018 The Authors. Published by Elsevier Ltd.
Among the various reason behind Non-technical losses in smart grid, losses due to electricity theft have become major apprehension in power system industries. A significant amount of consumption of electricity in a fr...
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ISBN:
(纸本)9781728134468
Among the various reason behind Non-technical losses in smart grid, losses due to electricity theft have become major apprehension in power system industries. A significant amount of consumption of electricity in a fraudulent manner decrease the supply quality, increase generation load and the real consumers have to pay excessive electricity bill that finally affects in overall economic condition. The advance metering infrastructure (AMI) has the ability to monitor the consumption detail of every consumer, record the consumption pattern, billing them as well as find any types of abnormalities. The communication capabilities of smart grid have facilitated the utilities to store their consumers' consumption patterns. With this database it is possible to formulate a theft detection model by machinelearning algorithm by analyzing the recorded data of smart meter. In this paper, Support vector machine (SVM), one of the prominent machinelearning classifiers applied with principle component analysis to train the data collected from smart meter and calculate the prediction accuracy with the test data. Here the principle component analysis reduces the dimension of the data to make the further processing less complex. After that, by using grid-search method the best-suited meta-parameters for SVM has been selected where the almost 90% accuracy rate is achieved. The classification results with respect to different meta-parameters of SVM (regularization parameter C and y) have presented. Moreover, the obtained result specifies that the applied techniques possesses higher accuracy and less false positive rate for real time consequences.
imageprocessing requires sophisticated platform because it is usually very expensive in terms of memory space and computational time. Consequentely, it is important to adopt economical solutions to replace traditiona...
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imageprocessing requires sophisticated platform because it is usually very expensive in terms of memory space and computational time. Consequentely, it is important to adopt economical solutions to replace traditional systems. These considerations led us to use cloud computing to meet large-scale data processing requirements. Meanwhile, this approach provides rapid access to on-demand services with high availability and scalability. Therefore, using cloud services instead of inhouse applications would undoubtedly help healthcare organizations outsource computations to an external party, thereby minimizing operating expenses. Nevertheless, strong data protection against both untrusted clouds and unauthorized users is required to prevent malicious data disclosure. Today, various frameworks are developed to enable users to store and process their data using cloud computing. In genaral, they are built up using cryptosystems, distributed systems and sometimes a combination of both. In particular, homomorphic cryptosystems, Service-Oriented Architecture (SOA), Secure Multi-party Computation (SMC) and Secret Share Schemes (SSS) are the major security mechanisms for almost all existing implementations. The main problem in the process of massive data analysis over cloud using these techniques is the computational costs associated with imageprocessing tasks. The first and foremost challenge is to prevent unauthorized access to medical records and personal health information. In this regard, we propose a novel approach based on machnine learing techniques to secure data processing in cloud environement. Typically, we use Support Vector machines (SVM) and Fuzzy C-means Clustering (FCM) to classify image pixels more efficiently. Additionally, we incorporate a further level, the CloudSec module, into the conventional two layered architecture to reduce the risk of the potential disclosure of medical information. We perform two sets of experiments to evaluate the proposed techniq
Detection of abnormalities in lung CT scans has always remained limited to experiences of a radiologist and capability of devices used for scanning. Again, the tremendous increase in CT data has increased the demand o...
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Active contour models have been extensively applied to imageprocessing and computer vision. In this paper, we present a novel adaptive method combines the advantages of the SBGFRLS model and GAC model. It can segment...
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ISBN:
(纸本)9783030033989;9783030033972
Active contour models have been extensively applied to imageprocessing and computer vision. In this paper, we present a novel adaptive method combines the advantages of the SBGFRLS model and GAC model. It can segment images in presence of low contrast, noise, weak edge and intensity inhomogeneity. Firstly, a region term is introduced. It can be seen as the global information part of our model and it is available for images with low gray values. Secondly, Legendre polynomials are employed in the local statistical information part to approximate region intensity and then our model can deal with images with intensity inhomogeneity or weak edges. Thirdly, a correction term is selected to improve the performance of curve evolution. Synthetic and real images are tested and Dice similarity coefficients of different models are compared in this paper. Experiments show that our model can obtain better segmental results.
Proposed basic elements of neuron networks – selective neurons and selective perceptron. Shown the efficiency of the use of these elements of neuron networks for many known applications of neuron networks: image reco...
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