This study focuses on both vehicle kinematic parameters (speed and acceleration) and behavior parameters (critical interval and follow-up time) of drivers at turbo-roundabouts. Empirical evaluations of such parameters...
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This study focuses on both vehicle kinematic parameters (speed and acceleration) and behavior parameters (critical interval and follow-up time) of drivers at turbo-roundabouts. Empirical evaluations of such parameters can be helpful in calibrating traffic microsimulation models or assigning behavior parameters to closed-form capacity models in turbo-roundabouts (gap-acceptance capacity models) and are also related to evaluation of vehicles pollutant emissions. The research was based on traffic process observed in the first turbo-roundabout implemented in the city of Maribor in Slovenia. In 2016 a great number of traffic samples were taken with high-frame-rate video recordings [>50 frames per second (fps)]. All vehicle trajectories were obtained with the methods and algorithms typical of the digital imageprocessing technique (DIP) by filtering the discrete signal of vehicle trajectories f(t)with wavelet analysis. The research results showed that vehicle speeds on entry lanes are rather moderate (below 25 km/h, 15 m prior to the Yield line), whereas accelerations usually have values inferior to 2 m/s(2) on arm lanes and to 1.5 m/s(2) on ring lanes. The critical intervals t(c) [distributed according to an Erlang probability distribution function (PDF)] and follow-up headways t(f) (distributed according to an inverse Gaussian PDF) have, on the other hand, values in ranges of t(c) = 4.03-5.48 s and t(f) = 2.52-2.71 s, respectively, according to the right-or left-turn lane and to the major or minor entry in question. (C) 2018 American Society of Civil Engineers.
Drowsiness of a person is major cause for accidents and to avoid accidents alerting person at right time is very necessary. Yawning is one of the signs, which indicates whether the person is drowsy or not. Most of the...
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The accurate segmentation of pulmonary nodules is an important preprocessing step in computer-aided diagnoses of lung cancers. However, the existing segmentation methods may cause the problem of edge leakage and canno...
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The accurate segmentation of pulmonary nodules is an important preprocessing step in computer-aided diagnoses of lung cancers. However, the existing segmentation methods may cause the problem of edge leakage and cannot segment juxta-vascular pulmonary nodules accurately. To address this problem, a novel automatic segmentation method based on an LBF active contour model with information entropy and joint vector is proposed in this paper. Our method extracts the interest area of pulmonary nodules by a standard uptake value (SUV) in Positron Emission Tomography (PET) images, and automatic threshold iteration is used to construct an initial contour roughly. The SUV information entropy and the gray-value joint vector of Positron Emission Tomography-Computed Tomography (PET-CT) images are calculated to drive the evolution of contour curve. At the edge of pulmonary nodules, evolution will be stopped and accurate results of pulmonary nodule segmentation can be obtained. Experimental results show that our method can achieve 92.35% average dice similarity coefficient, 2.19 mm Hausdorff distance, and 3.33% false positive with the manual segmentation results. Compared with the existing methods, our proposed method that segments juxta-vascular pulmonary nodules in PET-CT images is more accurate and efficient.
Template matching is a basic and crucial process for imageprocessing. In this paper, a hybrid method of stochastic fractal search (SFS) and lateral inhibition (LI) is proposed to solve complicated template matching p...
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Template matching is a basic and crucial process for imageprocessing. In this paper, a hybrid method of stochastic fractal search (SFS) and lateral inhibition (LI) is proposed to solve complicated template matching problems. The proposed template matching technique is called LI-SFS. SFS is a new metaheuristic algorithm inspired by random fractals. Furthermore, lateral inhibition mechanism has been verified to have good effects on image edge extraction and image enhancement. In this work, lateral inhibition is employed for image preprocessing. LI-SFS takes both the advantages of SFS and lateral inhibition which leads to better performance. Our simulation results show that LI-SFS is more effective and robust for this template matching mission than other algorithms based on LI.
Digital imageprocessingsystems are complex, being usually composed of different computer vision libraries. Algorithm implementations cannot be directly used in conjunction with algorithms developed using other compu...
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Digital imageprocessingsystems are complex, being usually composed of different computer vision libraries. Algorithm implementations cannot be directly used in conjunction with algorithms developed using other computer vision libraries. This paper formulates a software solution by proposing a processor with the capability of handling different types of imageprocessingalgorithms, which allow the end users to install new imageprocessingalgorithms from any library. This approach has other functionalities like capability to process one or more images, manage multiple processing jobs simultaneously and maintain the manner in which an image was processed for later use. It is a computational efficient and promising technique to handle variety of imageprocessingalgorithms. To promote the reusability and adaptation of the package for new types of analysis, a feature of sustainability is established. The framework is integrated and tested on a medical imaging application, and the software is made freely available for the reader. Future work involves introducing the capability to connect to another instance of processing service with better performance. Copyright (c) 2015 John Wiley & Sons, Ltd.
Additive noise is one among the prominent types of noises which degrades the quality of images. A very large number of algorithms, in spatial, frequency and wavelet domain have been proposed to enhance images corrupte...
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ISBN:
(纸本)9789811054273;9789811054266
Additive noise is one among the prominent types of noises which degrades the quality of images. A very large number of algorithms, in spatial, frequency and wavelet domain have been proposed to enhance images corrupted with additive noise. All the methods suggested have their own advantages as well as disadvantages. With the availability of parallel processing capability, in low end workstations and systems, fusion of two or more de-noising methods has become a topic of interest. In this paper, we have implemented one of the recent contributions to mean filter - a fuzzy filter. Also, as a complementary filter, the basic Non Local Means filter is implemented. Experiments were carried out by fusing the results obtained through the two filters. The results obtained establish the merit of the fusion approach.
The algorithms used in imageprocessing modes differ from each other both at home and abroad. This paper firstly provides a brief analysis of filtering algorithms for imageprocessing in embedded real-time systems, co...
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The algorithms used in imageprocessing modes differ from each other both at home and abroad. This paper firstly provides a brief analysis of filtering algorithms for imageprocessing in embedded real-time systems, combined with a comparison of their denoising methods. It then puts forward a kind of Kalman filtering algorithm for effective detection. By adopting the methods mainly including literature research, comparative analysis and statistics, this research makes a comparison between this and FAST, SURF and Harris algorithms. Finally, it turns out that all these algorithms are similar in detecting and processingimage noise, although the proposed motion filtering algorithm based on Kalman has more obvious advantages and outstanding performance in image restoration, registration, edge detection and compression, and thus has strong feasibility.
With the help of advanced image acquisition and processing technology, the vision-based measurement methods have been broadly applied to implement the structural monitoring and condition identification of civil engine...
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With the help of advanced image acquisition and processing technology, the vision-based measurement methods have been broadly applied to implement the structural monitoring and condition identification of civil engineering structures. Many noncontact approaches enabled by different digital imageprocessingalgorithms are developed to overcome the problems in conventional structural dynamic displacement measurement. This paper presents three kinds of imageprocessingalgorithms for structural dynamic displacement measurement, i.e., the grayscale pattern matching (GPM) algorithm, the color pattern matching (CPM) algorithm, and the mean shift tracking (MST) algorithm. A vision-based system programmed with the three imageprocessingalgorithms is developed for multi-point structural dynamic displacement measurement. The dynamic displacement time histories of multiple vision points are simultaneously measured by the vision-based system and the magnetostrictive displacement sensor (MDS) during the laboratory shaking table tests of a three-story steel frame model. The comparative analysis results indicate that the developed vision-based system exhibits excellent performance in structural dynamic displacement measurement by use of the three different imageprocessingalgorithms. The field application experiments are also carried out on an arch bridge for the measurement of displacement influence lines during the loading tests to validate the effectiveness of the vision-based system.
The proceedings contain 40 papers. The special focus in this conference is on Information Hiding, Secret Sharing, Speech Signal processing, Communication Protocols, Techniques, Encryption and Authentication Methods. T...
ISBN:
(纸本)9783319502083
The proceedings contain 40 papers. The special focus in this conference is on Information Hiding, Secret Sharing, Speech Signal processing, Communication Protocols, Techniques, Encryption and Authentication Methods. The topics include: A revisit to LSB substitution based data hiding for embedding more information;behavior steganography in social network;robust steganography using texture synthesis;a quantization-based image watermarking scheme using vector dot product;high-capacity robust watermarking approach for protecting ownership right;a data hiding method based on multi-predictor and pixel value ordering;a large payload webpage data embedding method using CSS attributes modification;the study of steganographic algorithms based on pixel value difference;digital audio watermarking robust against locality sensitive hashing;copyright protection method based on the main feature of digital images;a study on tailor-made speech synthesis based on deep neural networks;an improved 5-2 channel downmix algorithm for 3D audio reproduction;investigation on the head-related modulation transfer function for monaural DOA;temporal characteristics of perceived reality of multimodal contents;research on frequency automatically switching technology for china highway traffic radio;an automatic decoding method for Morse signal based on clustering algorithm;a novel digital rights management mechanism on peer-to-peer streaming system;a framework for supporting application level interoperability between IPv4 and IPv6;a new image encryption instant communication method based on matrix transformation and a three-party password authenticated key exchange protocol resistant to stolen smart card attacks.
The proceedings contain 30 papers. The special focus in this conference is on Applications of Machine Learning, Cloud Computing, Transportation, Multi-Robot systems and Uncertain systems. The topics include: Maximum l...
ISBN:
(纸本)9783319489438
The proceedings contain 30 papers. The special focus in this conference is on Applications of Machine Learning, Cloud Computing, Transportation, Multi-Robot systems and Uncertain systems. The topics include: Maximum likelihood estimation and optimal coordinates;relation recognition problems and algebraic approach to their solution;prediction of power load demand using modified dynamic weighted majority method;estimating cluster population;evaluation of particle swarm optimisation for medical image segmentation;automated processing of micro-ct scans using descriptor-based registration of 3d images;topic modeling based on frequent sequences graphs;Gaussian process regression with categorical inputs for predicting the blood glucose level;automated information extraction and classification of matrix-based questionnaire data;evaluating raft in docker on kubernetes;performance evaluation of MPTCP transmission of large data objects in computing cloud;a decentralized system for load balancing of containerized microservices in the cloud;layered reconfigurable architecture for autonomous cooperative UAV computing systems;a practical verification of protocol and data format negotiation methods in ComSS platform;reactive dynamic assignment for a bi-dimensional traffic flow model;comparing signal setting design methods through emission and fuel consumption performance indicators;GSOM traffic flow models for networks with information;designing mass-customized service subject to public grid-like network constraints;sensing feedback for the control of multi-joint prosthetic hand;spatio-temporal clustering and forecasting method for free-floating bike sharing systems;comparison of algorithms for constrained multi-robot task allocation and a joint problem of track closure planning and train run rescheduling with detours.
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