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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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Indian Sign Language Recognition Using Video vision Transformer 3

Indian Sign Language Recognition Using Video Vision Transfor...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Ojaswee Sreemathy, R. Turuk, Mousami Jagdale, Jayashree Anish, Mohammad Pune Institute of Computer Technology Department of E&TC Engineering Pune India Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350354171

Sign language is a way of communication that uses hand shapes, orientation, movements, and facial expressions to express instead of spoken words like normal language. Different regions have developed their own versions of sign language. In India, Indian sign language (ISL) is the primary mode of communication for more than 5 million speech or hearing impaired people. However, the knowledge of signing and understanding the ISL is only limited to the deaf community, which can make it hard to communicate with the people who don't understand the language. Thus, effective recognition of ISL gestures is vital for seamless interaction between these impaired people and the broader society. The rise of artificial intelligence and computer vision models has provided us with many effective tools to tackle this challenge. While there has been some progress in this field, most research has focused on static signs or American Sign Language, leaving video-based recognition for ISL largely unexplored. To bridge this gap, our paper proposes an approach that utilizes Video vision Transformer (ViViT) architecture for ISL recognition. We have experimented with different layers and different numbers of attention heads to get a model that works best for the assigned task. Our architecture gives an overall accuracy of 96.69% and a top-5 accuracy of 99.55% on the sign recognition task using our own created VISL-PICT dataset. © 2024 IEEE.

关键词： Audition

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2023 3rd international conference on Electronic Information Engineering and computer Communication, EIECC 2023

2023 3rd International Conference on Electronic Information ...

引用

3rd international conference on Electronic Information Engineering and computer Communication, EIECC 2023

ISBN: (纸本)9798350359961

The proceedings contain 142 papers. The topics discussed include: DeepLabV3-SAM: a novel image segmentation method for rail transportation;review on digital signal modulation methods and recognition performance characteristics in embedded systems;dynamic point convolution-based point cloud completion network;apple target detection task in complex scenes based on Yolov5 method;adaptive backward-link resource allocation in beam hopping communication system;dynamic frequency resource allocation method based on multi-beam directional ad-hoc network;small object detection algorithm for aerial photography based on improved YOLOv5;low-light image object detection algorithm based on YOLOX;research on frequency division MIMO radar imaging system;a long-term traffic flow prediction model based on multiple features;and a collaborative control model for shop-floor material handling system based on artificial intelligence.

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Image denoising based on residual network 3

Image denoising based on residual network

引用

3rd international conference on Electronic Information Engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Ma, Lihe Wang, Yan Sha, Ning Ma, Lin School of Information Engineering Harbin University Harbin China Heilongjiang Government Affairs Big Data Center3 Harbin China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin China

ISBN: (纸本)9781510680449

Image denoising has always been a hot research topic in the field of computer vision, playing an important role in improving image quality and accuracy. In this paper, a method for image denoising based on residual network is proposed, which uses deep learning technology to improve the effect of image processing. This method can automatically learn useful features in the image and effectively remove noise in the image, achieving automatic removal of image noise. Finally, experiments were conducted on standard image datasets for evaluation. The experimental results show that the image denoising method based on residual network has better performance compared to traditional methods. It can effectively remove various types of noise, improve image quality and clarity, and has broad application prospects in image processing. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image denoising

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Comprehensive Analysis of Fruit Detection, Ripeness Assessment, and Mass Estimation Using YOLOv7, ResNet, and ViT Models 3

Comprehensive Analysis of Fruit Detection, Ripeness Assessme...

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3rd IEEE international conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Guo, Zhihan Liu, Minqi College of Computer and Information Science School of Software Southwest University Chongqing China College of Oceanic and Atmospheric Sciences Ocean University of China Qingdao China

ISBN: (纸本)9798350380989

China became the world's largest apple producer, accounting for over 50% of the global planting area and output. The increasing urbanization has led to a decline in agricultural workers and higher labor costs, prompting the inevitable trend of employing picking robots for apple harvesting, supported by the widespread use of computer vision technology in agricultural automation. This paper employs a comprehensive approach using YOLOv7, ResNet, and ViT models for fruit detection, ripeness assessment, and quality estimation. Initially, the YOLOv7 model processes input images to generate detection boxes identifying apples and further quantifies the number of apples in each image. Subsequently, based on the detection boxes from the YOLOv7 model, color analysis is performed to calculate fruit ripeness, presenting the results in the form of a histogram for a more intuitive understanding of ripeness distribution. Next, by calculating the red pixel area of identified apples, the study estimates fruit quality and presents the results as a histogram, illustrating the distribution of fruits of varying quality. Finally, the ResNet and ViT models are used for image classification, comparing their accuracy on a validation set to evaluate their performance differences in fruit image classification tasks. This integrated process provides a comprehensive and accurate analysis for fruit detection and assessment, offering robust support for subsequent research in fruit quality analysis and classification. © 2024 IEEE.

关键词： computer vision Detection boxes ViT YOLOv7

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An Improvement of Vehicle and Passerby Recognition Based on YOLO-V3 Algorithm 3

An Improvement of Vehicle and Passerby Recognition Based on ...

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3rd international conference on Artificial Intelligence and computer Engineering, ICAICE 2022

作者： Ling, Tianyang Tian, Shuo Gao, Songyuheng Xing, Zhixue Lai, Jianshao Li, Zhenzhai School of Information and Communication Guilin University of Electronic Technology China

ISBN: (纸本)9781510663473

In order to reduce the incidence of traffic accidents, the use of computer vision to identify vehicles and passers-by in the process of driving can achieve the effect of assisting driving. This paper mainly introduces the performance improvement brought by the introduction of the SPP module in YOLO-V3 for object recognition. Model training is performed on the VOC dataset based on YOLO-V3-SPP. Finally, 300 photos were used to test the accuracy of the algorithm. The results show that the recognition accuracy of YOLO-V3-SPP for vehicles and pedestrians can reach 94.19% and 90.68%, and the accuracy of YOLO-V3 is improved by nearly ten under the same equipment. percentage point. The research on this technology can effectively reduce the probability of traffic accidents and provide reference value for the future driving safety warning field. © 2023 SPIE.

关键词： computer vision

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A Systematic Review: How computer vision is Transforming Agriculture in Economic Growth 3rd

A Systematic Review: How Computer Vision is Transforming Agr...

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3rd international conference on Artificial Intelligence and Knowledge Processing (AIKP)

作者： Karanam, Santoshachandra Rao Kumar, A. B. Pradeep Yandrapati, Prakash Babu Tangudu, Naresh Peddada, Nagamani Bollipelly, PruthviRaj Goud GITAM Deemed Univ CSE Dept Hyderabad India Aditya Inst Technol & Management CSD & MCA Dept Tekkali Srikakulam India Anurag Univ IT Dept Hyderabad India

ISBN: (纸本)9783031686160;9783031686177

Recent years have seen the emergence of computer vision, a subfield of artificial intelligence (AI), as a technology that has the potential to revolutionize agricultural practices. This might have an impact on many agricultural practices and crop management techniques. This is due to the fact that computer vision can examine pictures and identify patterns of data. This article provides a summary of the uses of computer vision in agriculture as well as the consequences such applications have had. The issues of precision agriculture, disease diagnosis, crop monitoring, and yield computation may be overcome with the use of computer vision technologies such as image recognition, object detection, and pattern analysis. Specifically, it investigates the ways in which these strategies are useful. In addition to this, it investigates the benefits, drawbacks, and possible future applications of computer vision in agriculture, with a particular emphasis on the potential for the sector to improve its levels of productivity, sustainability, and profitability. According to this in-depth analysis, computer vision has revolutionized the agricultural industry and contributed significantly to economic growth. In order to evaluate the financial impacts that computer vision has had on the agricultural industry, this study looks at a wide range of academic papers, publications, and reports. The findings highlight the advancements, benefits, challenges, and future opportunities presented by computer vision technology in the areas of crop monitoring, precision farming, animal management, and harvesting. According to the evaluation, computer vision has the potential to improve farming in terms of productivity, resource allocation, cost reduction, and sustainable practices.

关键词： computer vision Artificial Intelligence Economic Growth Agriculture Management Agricultural Automation Machine Learning

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Secure Communication in Resource-Constrained IoT Environments using MQTT Protocol: A Review 3

Secure Communication in Resource-Constrained IoT Environment...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Kaganurmath, Sharadadevi Cholli, Nagaraj G. Department of Computer Science Engineering Global Academy of Technology Bengaluru Visvesvaraya Technological University India Department of Information Science Engineering RV College of Engineering Bengaluru Visvesvaraya Technological University India

ISBN: (纸本)9798331532420

The Internet of Things (IoT) has revolutionized our lives by connecting a vast array of devices. The Message Queuing Telemetry Transport (MQTT) protocol plays a crucial role in enabling efficient communication between these devices. However, the resource-constrained nature of many IoT devices presents significant security challenges. This research review comprehensively addresses the security management of resource-constrained IoT devices utilizing the MQTT protocol. It explores the unique security threats faced by IoT devices, including those related to computation, memory, and energy constraints. The paper examines various security measures applicable to MQTT, such as authentication, encryption, and access control mechanisms, tailored to the specific needs of resource-constrained devices. This comprehensive review provides valuable insights into the security challenges and solutions for IoT devices utilizing the MQTT protocol, guiding researchers, developers, and stakeholders in enhancing the security and reliability of IoT ecosystems. © 2024 IEEE.

关键词： Telemetering

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2022 3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

2022 3rd International Conference on Computer Vision, Image ...

引用

3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

ISBN: (纸本)9781665459112

The proceedings contain 253 papers. The topics discussed include: testing the feasibility of quantizing the progress of stroke patients’ rehabilitation with a computer vision method;recognition of surface defects of aluminum profiles based on convolutional neural network;a novel generative adversarial network simulating the complementary structure of DNA genetic information;an improved lightweight method of convolutional neural network for face recognition;a calculation method for line-of-sight stable attitude of networked optical transceiver based on depth feedforward neural network;a grapevine virus disease detection method based on convolution neural network;a predict method of measuring equipment operation performance based on improved local weighted partial least squares;research on multi-scale network computer modeling based on mobile vendor management;and identification method of dress pattern drawing based on machine vision algorithm.

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Diabetic Foot Ulcer Unsupervised Segmentation with vision Transformers Attention 4th

Diabetic Foot Ulcer Unsupervised Segmentation with Vision T...

引用

4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Brodzicki, Andrzej Kucharski, Dariusz Moniak, Bartlomiej Kostuch, Aleksander Noworolnik, Filip Anna, W. Jaworek-Korjakowska, Joanna Department of Automatic Control and Robotics AGH University of Kraków Kraków Poland Center of Excellence in Artificial Intelligence AGH University of Kraków Kraków Poland

ISBN: (纸本)9783031808708

The Diabetic Foot Ulcer Challenge is a global computer vision competition organised to raise awareness of the complications caused by diabetes and motivate researchers to develop solutions that might help in the treatment. In 2024, the challenge focused on self-supervised learning methods and synthetic data, as the process of collecting and labelling medical data is a tedious one. In this research, we present a novel, two-stage method for self-supervised DFU segmentation. It is based on a vision Transformer attention from classification pre-task, followed by U-Net weakly supervised segmentation refinement. Our solution took 3rd place on the challenge leaderboard, with a 0.4796 Dice Score on the test set. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distribution transformers

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Uniqueness control of Student Reports Based on MinHash Algorithm and Metrics for Picking Source Documents 3

Uniqueness Control of Student Reports Based on MinHash Algor...

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3rd IEEE international conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024

作者： Shevlyakov, Roman V. Maliavko, Aleksandr A. Novosibirsk State Technical University Dept. of Computer Engineering Novosibirsk Russia

ISBN: (纸本)9798331516321

The success of the educational process is largely due to the independence of the students' processing of the studied information. The results of this process should be in the form of a report for review and evaluation by the teacher. Unfortunately, it is quite common for students to resort to a dishonest way of preparing reports - plagiarism. Certainly, such copied papers should not be accepted by the teacher, and should be sent for reworking. However, it is far from always possible for a faculty member to quickly and easily detect the fact of plagiarism. For some types of reports, for example - graduate qualification papers, generally known plagiarism detection systems can be used, showing the percentage of uniqueness of the checked document. However, such systems are inappropriate for analyzing simpler and mass produced documents such as lab and coursework reports. To solve the problem of facilitating the work of a teacher, it is proposed to analyze ways of creating and implementing software similar to existing anti-plagiarism systems, but focused on local limited use within a set of disciplines of one teacher and providing the ability to customize the database of source documents. © 2024 IEEE.

关键词： Students

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