Traffic density in roads has been increasing day by day which needs intelligent transportation system that can handle the traffic. Traffic management has become inevitable for smart cities. The enormous increase in ve...
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ISBN:
(纸本)9781728151977;9781728151960
Traffic density in roads has been increasing day by day which needs intelligent transportation system that can handle the traffic. Traffic management has become inevitable for smart cities. The enormous increase in vehicle numbers has generated more pressure to manage traffic congestion especially during peak hours. If the traffic congestion at a particular point of time can be found, then that information can be useful for managing the traffic in different lanes and change the traffic light cycle dynamically according to the vehicle count in different lanes. In recent years video surveillance and monitoring has been gaining importance. video can be analyzed which can be used to find the traffic density. Many useful information can be obtained by video processing like real time traffic density. vehicle counting can be done by detecting the object, tracking it and then finally counting the objects. Many different techniques are available for object detection and tracking. Deep learning techniques for object detection led to remarkable improvements compared to conventional imageprocessing techniques by removing the weakness in the conventional techniques. This paper provides a survey on various techniques available for vehicle detection and tracking.
An analytical solution of the problem of estimating the coordinates of the axis of a cylindrical surface and its radius by processing range images is obtained. The proposed methods and algorithms can detect fragments ...
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An analytical solution of the problem of estimating the coordinates of the axis of a cylindrical surface and its radius by processing range images is obtained. The proposed methods and algorithms can detect fragments of such surfaces in range images with the prescribed confidence probability. The method is also generalized for finding spherical surfaces in range images. The efficiency of the proposed methods and algorithms is verified by simulation using real-life range images of urban industrial scenes.
This paper focuses on developing a robust solution for the Simultaneous Localization and Mapping (SLAM) problem to increase the autonomy of Unmanned Aerial vehicles (UAv). The investigated topics are related to data f...
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image segmentation process is one of the most interesting and challenging problems in digital imageprocessing tasks. The segmentation process involves finding similar regions within an image. Many segmentation proble...
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image segmentation process is one of the most interesting and challenging problems in digital imageprocessing tasks. The segmentation process involves finding similar regions within an image. Many segmentation problems are achieved by the incorporation of clustering techniques. One of the most common technique for clustering process is the Fuzzy C-means (FCM) algorithm. However, even when FCM is one of the most popular techniques applied in image segmentation, it presents some issues such as large computational time complexity, noise sensitivity, and initial cluster centers dependency. In order to solve these problems, this paper presents a Histogram Based Fuzzy Clustering (HBFC) technique using an improved version of Firefly Algorithm (FA). In the proposed approach, the FA involves three search strategies: rough set-based population, random attraction and local search mechanism. Also, the clustering process is conducted based on gray level histograms instead of single pixels of an image. Under such circumstances, the occurrence of misclassification of pixels is reduced. A rigorous comparative study is conducted among the proposed approach and several state-of-art Nature-Inspired Optimization algorithms (NIOAs) and traditional clustering techniques. The numerical results indicate that the proposed approach outperform the well-known NIOA based clustering methods in terms of precision, robustness and quality of the segmented outputs. (C) 2021 Elsevier B.v. All rights reserved.
The practice of using the viola-Jones algorithm and its modifications to solve the problem of finding objects of interest (OI) in the image frame is analyzed. It is shown that the viola-Jones algorithm is usually used...
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This paper presents a novel methodology in measuring Foot Progression Angle (FPA) and other gait parameters, using digital imageprocessing, based on body and foot speeds. Measurements of body parts' movement spee...
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Convolutional Neural Networks (CNNs) are influencing major breakthroughs in computer vision by achieving unprecedented accuracy on tasks such as image classification, object detection, landmark detection and semantic ...
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ISBN:
(纸本)9781728133201
Convolutional Neural Networks (CNNs) are influencing major breakthroughs in computer vision by achieving unprecedented accuracy on tasks such as image classification, object detection, landmark detection and semantic segmentation. Owing to high computational complexity of most modern CNN architectures, graphical processing units (GPUs) are being utilized to achieve real-time performance albeit at a high energy cost. Consequently, Field Programmable Gate Arrays (FPGAs) based hardware accelerators are also making their way as they demonstrate GPU-like performance with significantly lower energy consumption that is well-suited for embedded vision applications. In this paper, we employ Hardware/Software Co-Design approach to accelerate Tiny YOLOv3 - an efficient CNN architecture for object detection - by designing a hardware accelerator for convolution, the most complex operation involved in the CNNs. Experimental results show significant performance gains, in the range of 3.9x to 21.3x, over previous implementations of efficient object detection algorithms.
Matrices are employed for diversified applications such as imageprocessing, control systems, video processing, radar signal processing, compressive sensing and many more. Finding inverse of a floating point large sca...
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ISBN:
(纸本)9781728196640
Matrices are employed for diversified applications such as imageprocessing, control systems, video processing, radar signal processing, compressive sensing and many more. Finding inverse of a floating point large scale matrix is considered to be computationally intensive and their hardware implementation is still a research topic. FPGA implementation of four different floating-point matrix inversion algorithms using a novel combination of high level language programming and model based design is proposed in this paper. The proposed designs can compute inverse of a floating point matrix up to a matrix size of 25x25 and can be easily scaled to large size matrices. The performance evaluation of proposed matrix inversion modules are carried out by their hardware implementation on a Zynq 7000 FPGA based ZED board and the results are reported.
The advancement in high-resolution X-ray tomography image acquisition techniques has enabled imaged-based modelling of pore-scale transport processes to better understand structural performance relationship in porous ...
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The advancement in high-resolution X-ray tomography image acquisition techniques has enabled imaged-based modelling of pore-scale transport processes to better understand structural performance relationship in porous media. The porous components in electrochemical energy storage devices such as lithium-ion batteries, fuel cell and redox flow batteries are subject to intense research to maximize performance and hence reduce the cost of energy storage systems. The image-based pore-scale modelling approaches such as direct numerical simulation (DNS) are, however, very computationally expensive and it gets infeasible to simulate a representative element volume of porous structure on a standard workstation or laptop machine. Pore network modelling (PNM) approach has been previously used to simulate large size porous domains of fuel cell and redox flow batteries at substantially lower computational cost, however, its application in lithium-ion batteries has not been attempted due to the multiphysics and transient nature of transport mechanism involved during charging and discharging process. Lithium-ion batteries are considered as the top candidate for electrochemical energy storage, so modelling their structure-performance relationship at less computational cost will enable development of efficient numerical pore network modelling framework. Therefore, this thesis aims towards developing pore network modelling framework for lithium-ion batteries to study the impact of microstructure on multiphysics transport processes occurring inside battery electrodes. The development of lithium-ion battery pore network model requires enhancements in the current implementation of pore network modelling algorithms. For example, current pore network extraction algorithms only extract a single phase from a tomography image (usually the pores). On the other hand, lithium-ion battery electrodes contain three phases, namely active material (e. g. NMC), carbon binder, and electrolyte filled v
Adversarial examples are artificially crafted to mislead deep learning systems into making wrong decisions. In the research of attack algorithms against multi-class image classifiers, an improved strategy of applying ...
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