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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

共 15616 条记录，以下是4391-4400 订阅

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Accurate Real-Time Traffic Light Detection Using YOLOv4

Accurate Real-Time Traffic Light Detection Using YOLOv4

引用

IEEE International conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Tai Huu - Phuong Tran Jae Wook Jeon College of Information and Communication Engineering SKKU Suwon Korea

ISBN: (数字)9781728161648

ISBN: (纸本)9781728161655

Traffic light detection has a significant role in every driver assistance system (DAS). Using the information of recognized traffic signals, a DAS provides safe navigating suggestions to drivers. In this paper, we focus on horizontal traffic light formats, which are popular in South Korea, North American and most European countries. Despite their different shapes and sizes, we group important traffic light signals into six categories: red, yellow, green, green-turn, red-turn and no signal. This paper proposes a method to localize all traffic light positions in the current scene; and identify the main ones from them. To detect and classify traffic signals, we use YOLOv4, which is the most recent deep learning framework. Next, based on the relation of detected traffic lights, our system removes irrelevant signals. Finally, with the optimized set of detected traffic lights, we identify the main signal for the whole scene. Experiments were conducted on two large datasets captured in Seoul Korea containing both highways and urban areas. The results achieved 95% accuracy at 30FPS.

关键词： Training Real-time systems Prediction algorithms image color analysis Urban areas Shape Graphics processing units

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International conference on Soft Computing and Signal processing, ICSCSP 2018

International Conference on Soft Computing and Signal Proces...

引用

International conference on Soft Computing and Signal processing, ICSCSP 2018

ISBN: (纸本)9789811335990

The proceedings contain 78 papers. The special focus in this conference is on Soft Computing and Signal processing. The topics include: Comparative analysis of clustering algorithms with heart disease datasets using data mining weka tool;A machine learning approach for web intrusion detection: MAMLS perspective;white blood cell classification using convolutional neural network;analysis of mobile environment for ensuring cyber-security in IoT-based digital forensics;payment security mechanism of intelligent mobile terminal;Hybrid neuro-fuzzy method for data analysis of brain activity using EEG signals;gait recognition using J48-based identification with knee joint movements;cyber intelligence alternatives to offset online sedition by in-website image analysis through webcrawler cyberforensics;deep convolutional neural network-based diabetic retinopathy detection in digital fundus images;analysis of early detection of emerging patterns from social media networks: A data mining techniques perspective;a framework for semantic annotation and mapping of sensor data streams based on multiple linear regression;Cyclostationarity analysis of GPS signals for spoofing detection;implementation of fingerprint-based authentication system using blockchain;NSGLTLBOLE: A modified non-dominated sorting TLBO technique using group learning and learning experience of others for multi-objective test problems;homomorphic encryption scheme for data security in cloud using compression technique;efficient query clustering technique and context well-informed document clustering;motif shape primitives on fibonacci weighted neighborhood pattern for age classification;a novel virtual tunneling protocol for underwater wireless sensor networks;garbage monitoring system using internet of things;mobile learning recommender system based on learning styles;initial centroids for K-means using nearest neighbors and feature means.

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Smart Feeder Robot for Severely Disabled Patients

Smart Feeder Robot for Severely Disabled Patients

引用

IEEE International conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Chihhsiong Shih Quanen Lai Huanran Ho WillIam Cheng-Chung Chu Tunghai University Taichung Taiwan

ISBN: (数字)9781728189154

ISBN: (纸本)9781728189161

As Taiwan enters an aging society, the number of disabled people increases accordingly, resulting in a growing burden for longterm care and family care managements. We propose an eye-hand integration system with an infrared sensor attached to the end of the arm. Not only can the accuracy of feeding be controlled, but also the position of the spoon/claw can be micro-controlled while picking up food as cleanly as possible. It is more intelligent than other feeding robot arms. It can automatically adjust the position of the head and face of users of different sizes and orientations. As long as the base of the arm is placed close to the head of the bed, feeding the arm can serve the user without special posture adjustment. At the same time, the users' head are mounted with a laser light and a camera which serves as a lighting sources on multiple dark areas on the food table. Given the lighted instructions, the robot arm is guided to different food plate. Disabled patients can then freely choose the order of eating and enjoy the quality of life. We report the details of constructing such system and presents the image processing algorithms for laser light selection operations.

关键词： Three-dimensional displays Mouth Software quality Aging Robot sensing systems Manipulators Software reliability

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International conference on Soft Computing and Signal processing, ICSCSP 2018

International Conference on Soft Computing and Signal Proces...

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International conference on Soft Computing and Signal processing, ICSCSP 2018

ISBN: (纸本)9789811333927

The proceedings contain 156 papers. The special focus in this conference is on Soft Computing and Signal processing. The topics include: Comparative analysis of clustering algorithms with heart disease datasets using data mining weka tool;A machine learning approach for web intrusion detection: MAMLS perspective;white blood cell classification using convolutional neural network;analysis of mobile environment for ensuring cyber-security in IoT-based digital forensics;payment security mechanism of intelligent mobile terminal;Hybrid neuro-fuzzy method for data analysis of brain activity using EEG signals;gait recognition using J48-based identification with knee joint movements;cyber intelligence alternatives to offset online sedition by in-website image analysis through webcrawler cyberforensics;deep convolutional neural network-based diabetic retinopathy detection in digital fundus images;analysis of early detection of emerging patterns from social media networks: A data mining techniques perspective;a framework for semantic annotation and mapping of sensor data streams based on multiple linear regression;Cyclostationarity analysis of GPS signals for spoofing detection;implementation of fingerprint-based authentication system using blockchain;NSGLTLBOLE: A modified non-dominated sorting TLBO technique using group learning and learning experience of others for multi-objective test problems;homomorphic encryption scheme for data security in cloud using compression technique;efficient query clustering technique and context well-informed document clustering;motif shape primitives on fibonacci weighted neighborhood pattern for age classification;a novel virtual tunneling protocol for underwater wireless sensor networks;garbage monitoring system using internet of things;mobile learning recommender system based on learning styles;initial centroids for K-means using nearest neighbors and feature means.

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Traffic Signal Transition Time Prediction Based on Aerial Captures during Peak Hours

Traffic Signal Transition Time Prediction Based on Aerial Ca...

引用

International conference on Control, Automation, Robotics and vision (ICARCV)

作者： Ruikang Luo Rong Su School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Owing to flexibility of Unmanned Aerial Vehicles (UAVs) and high efficiency of image processing technology, the combined systems become increasingly popular and important in the smart city operations. However, the application scenarios of this technology, especially on the traffic system prediction and multi-vehicle information extraction, still need to be explored. Besides, vehicle's detailed attributes need to be considered when building models. The smart traffic system can be broadly divided into two parts, traffic facilities (e.g. traffic signals, signs and sensors) and participants (e.g. vehicles and pedestrians). Many related works are presented about traffic parameters measurements using UAVs. In this paper, the prediction and traffic signal system analysis through different categories of vehicles' dynamic characteristics extracted from UAVs is presented. The motivation and related work is introduced. A stochastic process framework is presented for multi-vehicle speed extraction and signal transition time distributions at a signalized intersection. Detection and tracking methods/algorithms are proposed. To verify the mathematical model, the experimental data is collected at one intersection, in the city of Singapore during peak hours. After data collection, aerial images are processed to extract information. The regression method and processed parameters help to fit the required dynamic functions for different types vehicles. The estimated distributions reflect the traffic signal transition time provided by ground truths nicely. Moreover, the future research is presented on enhancing the system prediction accuracy and robustness.

关键词： Real-time systems Data collection Probability distribution Vehicle dynamics Target tracking Stochastic processes Roads

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Optimising image classification implementation of convolutional neural network algorithms to distinguish between plans and sections within the architectural, engineering and construction (AEC) industry 24

Optimising image classification implementation of convolutio...

引用

24th International conference on Computer-Aided Architectural Design Research in Asia: Intelligent and Informed, CAADRIA 2019

作者： Ng, Jennifer Mei Yee Khean, Nariddh Madden, David Fabbri, Alessandra Gardner, Nicole Haeusler, M. Hank Zavoleas, Yannis UNSW Australia Arup Engineering Sydney Australia UNSW and CAFA Visual Innovation Institute Beijing China

ISBN: (纸本)9789887891727

Modern communication between built environment professionals are governed by the effective exchange of digital models, blueprints and technical drawings. However, the increasing quantity of such digital files, in conjunction with inconsistent filing systems, increases the potential for human-error upon their look-up and retrieval. Further, current methods are manual, thus slow and resource intensive. Evidently, the architectural, engineering and construction (AEC) industry lacks an automated classification system capable of systematically identifying and categorising different drawings. To intercede, we aim to investigate artificially intelligent solutions capable of automatically identifying and retrieving a wide set of AEC files from a company’s resource library. We present a convolutional neural network (CNN) model capable of processing large sets of technical drawings - such as sections, plans and elevations - and recognise their individual patterns and features, ultimately minimising laboriousness. © 2019 and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong.

关键词： Machine learning

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Adaptive Target Detection Algorithm Based on Correlation Filtering

Adaptive Target Detection Algorithm Based on Correlation Fil...

引用

IEEE International conference on Unmanned systems (ICUS)

作者： Zexiao Xie Yan Zhang Shukai Chi Lin Zhou Ming Li Engineering Collage Ocean University of China Qingdao China

ISBN: (纸本)9781728137933

Correlation filtering is a fast and robust signal detection and processing algorithm. However, in the field of images processing, scale and rotation variation are important issues for correlation filtering algorithms. This paper proposes a detection method based on correlation filtering, which uses axis of symmetry and circumscribed rectangles to estimate the rotation and scale of the target. Firstly, using the HSV model to separate the colors to be found in the original image. Then, the axisymmetric shell intersection method is proposed to solve the symmetry axis and the circumscribed rectangle of the object. Finally, a correlation filter is used to solve all the areas that are likely to be objects. The response value, the position with the highest response value is the object position. The algorithm performs experiments on a set of artificially calibrated image sequences. Experiments show that this method can achieve better detection results when the number of training samples is small.

关键词： Correlation image color analysis Filtering Training Filtering algorithms Object detection Plugs

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Data Fusion algorithms to improve test range sensors accuracy and precision 1

Data Fusion algorithms to improve test range sensors accurac...

引用

1st International conference on Range Technology, ICORT 2019

作者： Urru, Alessandro Piras, Davide Palmas, Alessandro Nurjana Technologies Research and Development Department Elmas Italy

ISBN: (纸本)9781728113531

Data fusion technology is used in many research areas, such as image processing, automotive, robotics, defense and aerospace. The need to fuse data from different sensors reflects the demand for enhanced measurements precision. Every sensor has pro and cons, data fusion technology for tracking purposes in a test range is supposed to leverage the highest quality features of each sensor to obtain a better position estimation. This paper presents NT multi-sensor tracking algorithm, it is developed to receive target position information from different tracking sensors, radar and electro-optical, and use data fusion technology to obtain better quality data compared to the one provided by a single sensor alone. The algorithm uses a fusion technique which permits a huge performance boost: it associates a weight to every sensor calculated using Kalman filters, properly modeled, to estimate sensor measurements deviations from the estimated target position. These weights help the algorithm understanding which sensor is more reliable and to estimate the best possible position, which is very close to the real target position. Real case tracking scenarios are presented, where the algorithm is tested and compared with reference trajectory data to estimate its accuracy. © 2019 IEEE.

关键词： Real time systems

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image Enhancement Using Surveillance System for Dead Zone

Image Enhancement Using Surveillance System for Dead Zone

引用

International conference on Communication and Electronics systems (ICCES)

作者： Ayushi Kandoi Pradyumna Hegade Utkarsh Goel Venkata Harshavardhan Reddy Allu Dhanalakshmi Samiappan R. Kumar SRM Institute of Science and Technology Chennai India

ISBN: (数字)9781728112619

ISBN: (纸本)9781728112626

This paper presents a surveillance system that is capable of transmitting its geo location and the information from real time images to a central unit in no network zone. The system is further configured for using image processing and machine learning algorithms to deduce any suspicious object from the captured image. Thereby, the system is helpful in identifying threats in all kind of weather conditions on land and in oceans. The system is very efficient, cost effective and is simple in design. Once the threat is identified, the system transmits the information to the central unit through the LoRa by using certain communication protocols. Compared to existing devices, this system has the potential to reduce causalities by enhancing navigational and observational features in the surveillance sector.

关键词： Surveillance Logic gates Machine learning algorithms conferences Robots Servers image processing

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Advanced Semi-supervised Possibilistic Fuzzy C-means Clustering using Spatial-Spectral distance for Land-cover Classification

Advanced Semi-supervised Possibilistic Fuzzy C-means Cluster...

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IEEE International conference on systems, Man, and Cybernetics (SMC)

作者： Dinh-Sinh Mai Long Thanh Ngo Le-Hung Trinh Le Quy Don Tech Univ Hanoi Vietnam

ISBN: (纸本)9781538666500

With the explosion of information, characteristics of increasingly complex data, the use of traditional methods in data processing has proved ineffective. Computer applications are increasingly becoming important and essential in many areas such as biology, medicine, psychology, economics, image processing and many other disciplines. A variety of multi-spectral satellite image classification, clustering algorithms have been developed and applied to analyze the surface of the earth. In this paper, we propose a novel semi-supervised possibilistic fuzzy c-means clustering on spatial-spectral distance (SPFCM-SS) for multi-spectral image land-cover classification by the extension of the possibilistic fuzzy C-means (PFCM) algorithm, in which spectral information and spatial information of the pixels are used coupled with labelled data to increase the accuracy of clustering results when the data structure of input patterns is non-spherical and complex. Experiments were performed for multi-spectral satellite image data and clustering efficiency indexes were used to compare the performance of the proposed algorithm with other similar algorithms.

关键词： semi-supervised PFCM spatial information multi-spectral image

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