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检索条件"任意字段=Proceedings of the 3rd International Conference on Control and Computer Vision"

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Segmentation Of Intracerebral Haemorrhage CT-Scan Images Using N-Net Architecture 3

Segmentation Of Intracerebral Haemorrhage CT-Scan Images Usi...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Santhi, P. Maheswari, S. Deepa, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tiruchirapalli Avadi India

ISBN: (纸本)9798350369083

In recent years, image segmentation has emerged as a critical task in computer vision and various applications such as medical image processing, autonomous driving, and satellite imagery analysis. Deep learning techniques, particularly convolutional neural networks (CNNs), have played a pivotal role in revolutionizing image segmentation by offering unprecedented accuracy and efficiency. Many algorithms have used for the segmentation such as U-Net, V-Mesh, U-Net++ and etc. But, U-Net having the challenges in parameter transfer, model size and in speed. To overcome these issues, this paper explores the evolution of image segmentation techniques into ICH (Intracerebral Haemorrhage) for better diagnosis. This model is tested using ICH dataset available in Kaggle. In which, N-Net have produced the better performance in terms of precision, Dice, Recall and F1 Score. © 2024 IEEE.

关键词： Mesh generation

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MVAD: monocular vision-based autonomous driving distance perception system 3

MVAD: monocular vision-based autonomous driving distance per...

引用

3rd international conference on computer vision and Data Mining, ICCVDM 2022

作者： Wei, Xiaoyun Xiao, Chaosui Wuhan research institute of posts and telecommunic Wuhan China

ISBN: (纸本)9781510661363

In recent years, with the development of social economy and the unprecedented development of computer hardware computing power, artificial intelligence technology has developed rapidly, the era of big computing has come, and breakthroughs in deep learning algorithms have brought new opportunities for human-computer interaction and autonomous vehicles. Autonomous driving is divided into three parts: perception, planning and control. The perception part is the most widely used technical field of computer vision, and the camera, as one of the indispensable sensors in intelligent driving vehicles, provides important image information for self-driving vehicle systems. The automatic driving system can obtain the location and distance information of various vehicles and pedestrians appearing in front of the car, providing accurate road information for drivers, avoiding traffic accidents, reducing casualties and property *** paper studies the object detection task and depth estimation task based on monocular vision in the visual distance perception system of autonomous driving. The emergence of deep learning has promoted the rapid development of the field of computer vision, and the application of deep convolutional neural networks has enabled vision-based object detection and depth estimation. The estimation accuracy has been greatly improved, but the computational load of the deep convolutional neural network is very large. Since the automatic driving system needs to control the car in real time, it requires the visual perception algorithm to run in real time, and the on-board computing platform of the self-driving car is required. It belongs to the mobile computing platform, and its computing power is very limited. Therefore, this paper designs a multi-task deep convolutional neural network to detect the targets appearing in the field of view of the autonomous vehicle in real time based on monocular images, and obtain the front The distance between the target veh

关键词： Deep neural networks

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Research on Image Enhancement Processing Algorithms in computer Art Education 3

Research on Image Enhancement Processing Algorithms in Compu...

引用

3rd international conference on Optics and Machine vision, ICOMV 2024

作者： Li, Xiaojing Shanghai Donghai Vocational and Technical College Shanghai China

ISBN: (纸本)9781510680319

This research paper explores the application of image enhancement processing algorithms in the context of computer art education. The paper acknowledges the significance of high-quality images in art education and addresses the challenges in enhancing image quality due to factors like equipment performance, environmental conditions, and photographic techniques. It delves into various image enhancement methods, such as those based on physical models for dehazing and underwater image enhancement, including the dark channel prior theory and methods that do not require prior knowledge of camera parameters. The study also covers the importance of objective and precise evaluation of image quality changes before and after processing, highlighting the necessity for reliable image quality evaluation methods. It introduces the Unsupervised Low-Light Enhancement Algorithm (ULEA) based on attention mechanisms, detailing its network structure, loss functions, and the impact of these functions on low-light image enhancement processing. Through experimental research and comparative analysis, the paper demonstrates the effectiveness of ULEA in enhancing low-light images, significantly improving overall brightness and image texture layers compared to traditional and deep learning-based methods. In conclusion, the paper presents a compelling case for the practical application of advanced image enhancement algorithms in computer art education, proving their effectiveness in enhancing the visual quality of images and their applicability in a variety of conditions. © 2024 SPIE.

关键词： Image enhancement

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Enhancing Traffic control Strategies through Dynamic Simulation and Reinforcement Learning 3

Enhancing Traffic Control Strategies through Dynamic Simulat...

引用

3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Sebastian, Alona Mary Athulram, K.R. Michael, Colin Sunil, Diya Anna Preetha, K.G. Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Ernakulam India

ISBN: (纸本)9798350375190

In urban areas, traffic congestion is a major problem causing increased travel times, higher consumption of fuel, and environmental pollution. The traditional traffic control systems cannot adapt to dynamic traffic conditions, causing inefficient traffic flow and inefficient use of road infrastructure. In this paper, we propose a novel method to enhance traffic management strategies through dynamic simulation and reinforcement learning (RL). A simulation framework is presented that models the complex interactions among vehicles, traffic signals, and infrastructure in a realistic urban environment. Within this framework, RL agents are deployed to learn adaptive traffic control policies that minimize congestion and optimize traffic flow. This approach uses the ability of RL to continuously learn and adjust to shifting traffic circumstances, which results in improved efficiency and responsiveness compared to static or rule-based control systems. Finally, evaluate the effectiveness of the approach through extensive simulations and demonstrate its capability to significantly reduce congestion, decrease travel times, decrease average waiting time and enhance overall traffic management. By integrating dynamic simulation and RL-based control strategies, the proposed approach offers a guaranteed solution for addressing the challenges of urban traffic congestion and advancing the field of intelligent transportation systems. © 2024 IEEE.

关键词： Reinforcement learning

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YOLOv8 for Product Brand Recognition as Inter-Class Similarities 3

YOLOv8 for Product Brand Recognition as Inter-Class Similari...

引用

3rd international conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2023

作者： Abyasa, Jacqueline Kenardi, Marvelius Putra Audrey, Jesslyn Jovanka, Jonathan Jason Justino, Christoper Rahmania, Rissa School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327762

Smart retail has been increasing in interest due to societal shifts along with technological advancements. The fast-paced integration of artificial intelligence (AI) brings the opportunity to develop potential technology in the scope of daily activities. Smart retail implements a field of artificial intelligence that involves computer vision to detect and recognize products. This paper aims to analyze the performance of YOLOv8 for brand recognition on grocery products. However, the inter-class similarities problems are present among certain grocery products included in the dataset to further represent real-life conditions of smart retail. Firstly, an adequate dataset is internally developed with common grocery products from 20 classes that represent brands. In the dataset developed, 3095 images were used and split into training, validation, and testing in 70%, 20%, and 10%, respectively. Then, an experiment is conducted with the YOLOv8 model where the performance result showed by precision, recall, mean average precision (mAP), and confusion matrix. Results of the experiment show that the YOLOv8 model gives a mAP50 value of 0.96303. This result indicates that the YOLOv8 model's performance for object recognition opened new insights into its potential in supporting the automatic checkout process in smart retail. © 2023 IEEE.

关键词： computer vision

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Research on Deep Learning-based Driver Fatigue Driving Behavior Recognition Methods 3

Research on Deep Learning-based Driver Fatigue Driving Behav...

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3rd international conference on Big Data, Information and computer Network, BDICN 2024

作者： Sun, Miao Liu, Fan He, Haijun Yang, Zhuomin Lou, Wen Guo, Dudu School of Intelligent Manufacturing Modern Industry Xinjiang University Urumqi China Xinjiang Uygur Autonomous Region Public Security Department Traffic Police Corps Urumqi China Traffic Management Research Institute of the Ministry of Public Security Key Laboratory of Road Traffic Safety Ministry of Public Security Wuxi China School of Traffic and Transportation Engineering Xinjiang University Urumqi China Xinjian Key Laboratoryof Gn. Construction and Smart Traffic Control of Transportation Infrastructure Urumqi China

ISBN: (纸本)9798350363821

Aiming at the problem of motor vehicle drivers' fatigue driving, this paper proposes a deep learning-based fatigue driving behavior recognition method. The four parameters of PERCLOS parameter, blinking frequency, continuous eye closure time and continuous mouth opening time are used as the recognition indexes to establish a driver fatigue driving recognition model based on deep learning, and the experimental results show that the method proposed in this paper has a better recognition effect in different light environments. © 2024 IEEE.

关键词： Deep reinforcement learning

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Circuit Covering Graph Based Algorithm for Solving the Traveling Salesman Problem 3

Circuit Covering Graph Based Algorithm for Solving the Trave...

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3rd international conference on Problems of Logistics, Management and Operation in the East-West Transport Corridor, PLMO 2024

作者： Nuriyeva, Fidan Nuriyev, Urfat Dokuz Eylul University Department of Computer Science Izmir Turkey Institute of Control Systems The Ministry of Science and Education of the Republic of Azerbaijan Baku Azerbaijan Ege University Department of Mathematics Izmir Turkey

ISBN: (纸本)9798350353099

This study presents an algorithm for solving the Traveling Salesman Problem (TSP) based on Circuit Covering Graph and Insertion Heuristic methods. This algorithm offers an efficient approach to constructing a tour for the TSP, Experimental results demonstrate the effectiveness of the algorithm in solving TSP instances with large numbers of cities. © 2024 IEEE.

关键词： Traveling salesman problem

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Bio-inspired Swarm Intelligence: A Flocking Project with Group Object Recognition 3

Bio-inspired Swarm Intelligence: A Flocking Project with Gro...

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3rd international conference on Consumer Electronics and computer Engineering, ICCECE 2023

作者： Tianbo, Song Weijun, Hu Jiangfeng, Cai Weijia, Liu Quan, Yuan Kun, He Ira A. Fulton Schools of Engineering Arizona State University Tempe United States Saint Petersburg State University Faculty of Applied Mathematics and Control Processes Saint Petersburg Russia Lomonosov Moscow State University Department of Chemistry Moscow Russia Allen School of Engineering and Computing Trine University Angola United States The Open University of China Faculty of Engineering Hangzhou China College of Computing Illinois Institute of Technology Chicago United States

ISBN: (纸本)9798350331578

With the help of the adaptive gain control algorithm and the flocking SWARM algorithm with adversarial agents, the project described in this article makes the entire system communication-free while maintaining the same functionality and performance. It outlines the entirety of this biologically inspired swarm intelligence's thought process, from inception to conclusion (Flocking algorithm). The entire project is dependable and practically impenetrable under a variety of conditions. The neighbor observation for group conjecture is then strengthened by the use of the adaptive gain control technique and a partial Kalman filter. After the system has been stabilized, use the built-in camera of the SWARM drone to introduce the Object Recognition method in computer vision. As a result, the system enters the real world from the simulation level. © 2023 IEEE.

关键词： Object recognition

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Steady-state visual evoked potentials based 2D continuous control of brain-computer interface 3

Steady-state visual evoked potentials based 2D continuous co...

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3rd international conference on Intelligent Mechanical and Human-computer Interaction Technology, IHCIT 2024

作者： Huang, Dingyong Yang, Xueliang Shen, Hui College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781510683105

Two-dimensional continuous control based on brain-computer interface (BCI) systems is becoming a research hotspot. However, due to the discreteness in BCI signal decoding, achieving smooth 2D continuous control is often challenging. To address this issue, this paper proposes an innovative 2D continuous control method that converts three discrete commands into continuous ones and validates it through a real-time online BCI system. We used dry electrodes for signal acquisition and applied the Canonical Correlation Analysis (CCA) algorithm for electroencephalography (EEG) decoding. Two simulation experiments were designed: obstacle avoidance for unmanned vehicles and trajectory tracking for unmanned vehicles, to verify the effectiveness of the 2D continuous control method. By analyzing experimental data from three subjects and multiple evaluation metrics, we comprehensively and thoroughly assessed the proposed method. © 2024 SPIE.

关键词： Electroencephalography

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Application and Research of UAV Automatic Inspection and Positioning control Technology 3

Application and Research of UAV Automatic Inspection and Pos...

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3rd international conference on computer Science, Electronic Information Engineering and Intelligent control Technology, CEI 2023

作者： Cao, Hanhua Ding, Feng He, Xiu Gu, Changle Guangzhou Xinhua University Guangzhou510520 China Guangzhou Huali College Guangzhou511325 China

ISBN: (纸本)9798350372052

In order to solve the problem of excessive deflection of UAV patrol angle and make the layout of patrol nodes more in line with the actual positioning requirements, the automatic patrol positioning control technology of UAV based on machine vision is studied. According to the principle of patrol coordinate transformation, the forward kinematics equation and inverse kinematics equation of UAV are solved respectively, and the patrol nodes of UAV based on machine vision are grasped and processed. On this basis, the extracted feature points are used to estimate the numerical level of pose between frames, and combined with relevant data, the positioning authority of the control host for the UAV inspection curve is determined, and the design of automatic inspection positioning control method for UAV based on machine vision is realized. Experimental results show that, compared with the detection algorithm based on Cascade R-CNN, under the action of machine vision technology, the deflection degree of UAV patrol angle is really well controlled, which can make the layout of patrol nodes more in line with the actual positioning requirements. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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