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The Automated Vehicle Detection of Highway Traffic Images by Differential Morphological Profile 被引量:1
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作者 Bharti Sharma Vinod Kumar Katiyar +1 位作者 Arvind Kumar Gupta akansha singh 《Journal of Transportation Technologies》 2014年第2期150-156,共7页
Vehicle detection has been the critical part of the traffic surveillance system for many years. However, vehicle detection method is still challenging. In this paper, differential morphology closing profile is used to... Vehicle detection has been the critical part of the traffic surveillance system for many years. However, vehicle detection method is still challenging. In this paper, differential morphology closing profile is used to extract the vehicle automatically from the traffic image. Along with closing profile, some addition operation has been applied as a part of the algorithm to get the high detection and quality rate. Result demonstrated that the novel method has an excellent detection and quality percentage. We also have compared our automated detection method with other traditional image processing based methods and the results indicate that our proposed method provides better results than traditional image processing based methods. 展开更多
关键词 AVERAGE Filter DIFFERENTIAL Morphology CLOSING PROFILE Structure Element THRESHOLDING Vehicle Detection
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Diagnosis of COVID-19 from Chest X-Ray Images Using Wavelets-Based Depthwise Convolution Network 被引量:2
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作者 Krishna Kant singh akansha singh 《Big Data Mining and Analytics》 EI 2021年第2期84-93,共10页
Coronavirus disease 2019 also known as COVID-19 has become a pandemic. The disease is caused by a beta coronavirus called Severe Acute Respiratory Syndrome Coronavirus 2(SARS-Co V-2). The severity of the disease can b... Coronavirus disease 2019 also known as COVID-19 has become a pandemic. The disease is caused by a beta coronavirus called Severe Acute Respiratory Syndrome Coronavirus 2(SARS-Co V-2). The severity of the disease can be understood by the massive number of deaths and affected patients globally. If the diagnosis is fast-paced, the disease can be controlled in a better manner. Laboratory tests are available for diagnosis, but they are bounded by available testing kits and time. The use of radiological examinations that comprise Computed Tomography(CT) can be used for the diagnosis of the disease. Specifically, chest X-Ray images can be analysed to identify the presence of COVID-19 in a patient. In this paper, an automated method for the diagnosis of COVID-19 from the chest X-Ray images is proposed. The method presents an improved depthwise convolution neural network for analysing the chest X-Ray images. Wavelet decomposition is applied to integrate multiresolution analysis in the network. The frequency sub-bands obtained from the input images are fed in the network for identifying the disease.The network is designed to predict the class of the input image as normal, viral pneumonia, and COVID-19. The predicted output from the model is combined with Grad-CAM visualization for diagnosis. A comparative study with the existing methods is also performed. The metrics like accuracy, sensitivity, and F1-measure are calculated for performance evaluation. The performance of the proposed method is better than the existing methodologies and thus can be used for the effective diagnosis of the disease. 展开更多
关键词 CORONAVIRUS COVID-19 deep learning convolution neural network X-Ray images
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Call for Papers Special Issue on Deep Learning and Evolutionary Computation for Satellite Imagery
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作者 Krishna Kant singh Soumyabrata Dev +1 位作者 akansha singh Seungmin Rho 《Big Data Mining and Analytics》 EI 2022年第1期79-79,共1页
Satellite images are humungous sources of data that require efficient methods for knowledge discovery.The increased availability of earth data from satellite images has immense opportunities in various fields.However,... Satellite images are humungous sources of data that require efficient methods for knowledge discovery.The increased availability of earth data from satellite images has immense opportunities in various fields.However,the volume and heterogeneity of data poses serious computational challenges.The development of efficient techniques has the potential of discovering hidden information from these images.This knowledge can be used in various activities related to planning,monitoring,and managing the earth resources.Deep learning are being widely used for image analysis and processing.Deep learning based models can be effectively used for mining and knowledge discovery from satellite images. 展开更多
关键词 HAS DEEP KNOWLEDGE
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