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检索条件"任意字段=3rd International Conference on Image Processing and Vision Engineering, IMPROVE 2023"

共 1700 条记录，以下是11-20 订阅

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Maintaining the Integrity of Online Exams using Computer vision and image processing 3

Maintaining the Integrity of Online Exams using Computer Vis...

引用

3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Nanda Kumar, K. Guthikonda, Nandini Abdullah, Mohammad Adil Sagar Imambi, S. Mohan, V. Murali Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram Guntur India

ISBN: (纸本)9781665462167

Due to the growing popularity of online learning, there is a need for efficient methods to assure the validity of online tests, especially multiple-choice exams (MCQs). The application of computer vision and picture pre-processing techniques is one strategy that has shown potential in this situation. With the use of these methods, cheating may be identified and stopped by analysing photos of the test-taker and their surroundings. In the context of online MCQ tests, this article addresses the possible uses of computer vision and picture pre-processing, including the use of facial recognition algorithms and image segmentation to detect and extract particular items or characteristics. The employment of these strategies offers enormous potential for maintaining the integrity of online education, even if there are still issues to be resolved, such as privacy concerns and the requirement for precise and trustworthy algorithms. © 2023 IEEE.

关键词： image segmentation

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Visual damage detection technology for conveyor belts in coal-fired power plants 3

Visual damage detection technology for conveyor belts in coa...

引用

3rd international conference on Energy and Power engineering, Control engineering (EPECE)

作者： Cheng, Weiliang Yin, Xinwei Wang, Yanchun North China Elect Power Univ Sch Energy Power & Mech Engn Beijing 102206 Peoples R China Datang Environm Ind Grp Co Ltd Beijing 100097 Peoples R China

ISBN: (纸本)9798350366105;9798350366099

Chinese coal-fired power plants generally use cantilever bucket wheel machines to stack and retrieve coal from their coal yards. The belt conveyor of the bucket wheel machine is the main transportation equipment for coal fuels. During the operation of a belt conveyor, the conveyor belt is prone to damage and often experiences longitudinal tearing. Once longitudinal tearing occurs, if not detected in time, it may damage the entire conveyor belt and cause huge losses to the production of coal-fired power plants. It is of great significance to develop a visual based conveyor belt damage detection technology to improve the efficiency and accuracy of fault detection for coal yard conveyor belts in coal-fired power plants, and ensure the safety of bucket wheel equipments. Long wave and medium wave infrared CCD ( Charge coupled device) cameras are used for image acquisition. After algorithmic processing of the infrared radiation intensity with the image information, it is converted into corresponding grayscale images. The traditional two-dimensional Otus image processing algorithm was optimized using the lion group algorithm, and the chaotic sequence mechanism was introduced to achieve the optimal binary image threshold segmentation. The FAST(Features from accelerated segment test) algorithm is used to discover image corners for image features and fault recognition. Based on the refinement algorithm, the longitudinal tearing defect of the images is further determined using the Hough transform. Based on this, this article proposes a vision based conveyor belt damage detection and analysis method, which overcomes the harsh environmental requirements of actual sites and adds optimization and secondary processing processes. The research results indicate that this method can effectively monitor and identify damage to conveyor belts, with recognition accuracy of over 98%, and corresponding prediction measures can be proposed to further prevent the increase of damage.

关键词： Algorithm Computer vision Belt conveyor Damage detection Bucket wheel machine

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CBAM-DnCNN: An improved Method For image Denoising 3

CBAM-DnCNN: An Improved Method For Image Denoising

引用

3rd international conference on Electronic Information engineering and Computer Science, EIECS 2023

作者： Cai, Guofeng Sichuan University School of Software Engineering Chengdu China

ISBN: (纸本)9798350316674

In modern society, with the wide application of digital images in the fields of social media, medical diagnosis, security monitoring, and automatic driving, the quality of images has become particularly critical for information transfer and decision-making. With the continuous development of artificial intelligence and computer vision, image denoising techniques are gradually applied to improve image quality. In this paper, an image denoising method based on a modified DnCNN network (CBAM-DnCNN) is proposed. Our method first convolves the noisy image to extract features, then captures the spatial structure of the image through the Convolutional Block Attention Module, and then feeds it to this DnCNN network for residual learning. In addition, the combination of BCEWithLogitsLoss and MSELoss is used as a loss function during training and the introduction of these methods can improve the quality of the results. This work provides a new method for image noise reduction and proves the effectiveness of the method in experiments. © 2023 IEEE.

关键词： Attention mechanism component DnCNN image denoising Residual learning

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vision-Based Identification of Vacant Seats in Public Transport for the Visually Impaired 3

Vision-Based Identification of Vacant Seats in Public Transp...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Pargaonkar, Jay Kulkarni, Parth Paranjpay, Soumil Shilaskar, Swati Vishwakarma Institute of Technology Dept. of Electronics & Telecommunication Pune India

ISBN: (纸本)9798350317022

The accessibility of public spaces for visually impaired individuals is a major concern. One of the challenges faced by blind people in public spaces is the difficulty in locating vacant seats. The presented paper discusses the design, development and evaluation of a computer vision based vacant seat detection system. The system has been developed using various image processing techniques such as BRIEF, Extreme Gradient Boosting, Random Forest. The system identifies features in horizontal lines of the seat to differentiate occupied seats from unoccupied seats. It utilizes various image processing and machine-learning algorithms to improve accuracy and performance. The system has been trained and tested on more than 500 images, which results in quite an impressive accuracy of 81%. The paper highlights the importance of such a system in improving the accessibility and convenience of public spaces for visually impaired individuals. © 2023 IEEE.

关键词： Computer vision Edge Detection image-processing Vacant Seat Detection

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vision Transformer for Cell Tumor image Classification 3

Vision Transformer for Cell Tumor Image Classification

引用

3rd international conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2023

作者： Li, Xiuyan Yang, Zhenyu Wang, Qi Sun, Yukuan Liu, Aidong Tiangong University School of Electronic and Information Engineering Tianjin China Tiangong University School of Electronic and Information Tianjin China Tiangong University Center for Engineering Intership and Training Tianjin China Department of Pathology Tianjin China

ISBN: (纸本)9798350302356

With the successful application of deep learning models in medical image classification tasks in recent years, especially in tumor recognition, we explore the application of vision Transformer (ViT) model in cell tumor image classification. In this paper, we improve the performance of the pre-trained vision Transformer (ViT) model on this task by fine-tuning it on the cell tumor image dataset, and compare its performance with other state-of-the-art tumor image classification models, including convolutional neural networks (CNNs). Experimental results show that compared with the convolutional model, the vision Transformer(ViT) model has better globation ability and interpretability. In addition, we conduct a detailed analysis of the attention mechanism of the vision Transformer (ViT) model, which provides important insights into the model's decision-making process. Therefore, our study shows great potential for the use of vision Transformer (ViT) in medical image analysis tasks, especially in tumor classification. © 2023 IEEE.

关键词： image classification

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Research on Flame Detection of fire-fighting robot 3

Research on Flame Detection of fire-fighting robot

引用

3rd international conference on Computer vision and Pattern Analysis, ICCPA 2023

作者： Tohti, Gulbahar Zhang, Guohui Geni, Mamtimin School of Mechanical Engineering Xinjiang University Xinjiang Urumqi830047 China Company of Baibo Electromechanical Technology Urumqi Xinjiang830011 China

ISBN: (纸本)9781510667563

Aiming at the problems of slow image processing and detection speed of flame recognition method and sensor measurement vulnerable to light environment interference, this paper studies how to improve the speed of flame recognition and overcome light environment interference. To improve the speed of flame recognition, this paper selects convolutional neural network technology, trains a classifier dedicated to flame, improves the preprocessing and post-processing methods of feature map, and develops a new flame recognition model, which greatly improves the speed of flame recognition;To overcome the interference of light environment, this paper proposes a new method by measuring the two output signals of the flame sensor in different light environments. It provides a new method for fast flame recognition of fire-fighting robot. © 2023 SPIE.

关键词： image enhancement

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Underwater image Enhancement via Multi-color Space Correction and Fusion 3

Underwater Image Enhancement via Multi-color Space Correctio...

引用

3rd international conference on Electronic Information engineering and Computer, EIECT 2023

作者： Zhou, Chenyu Chen, Bo Zhang, Ying Chen, Wensheng Pan, Binbin Ji, Jing Shenzhen University Shenzhen Key Laboratory of Advanced Machine Learning and Applications College of Mathematics and Statistics Shenzhen518060 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798350357707

Underwater image enhancement, as an important branch of image processing, has attracted the attention of many scholars in recent years. Due to selective scattering and degradation of light in water, images captured underwater are generally of low quality. To improve the accuracy of underwater image details and color, we propose a novel underwater image enhancement model, which is based on a Multi-color space fusion (MSF) strategy and assigns corresponding weights to each color channel for fusion. Qualitative and quantitative comparisons show that the proposed method can effectively remove image blur, achieve color correction, and significantly improve image sharpness. © 2023 IEEE.

关键词： Color

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Hybrid Attention vision Transformer-based Deep Learning Model for Video Caption Generation 3

Hybrid Attention Vision Transformer-based Deep Learning Mode...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Ansari, Khustar Srivastava, Priyanka Sarala Birla University Jharkhand Ranchi835103 India Bit Sindri Dhanbad Department of Computer Science and Engineering Jharkhand Sindri828123 India Sarala Birla University Computer Science and Engineering Jharkhand Ranchi835103 India

ISBN: (纸本)9798331509675

Video caption generation is an extremely challenging computer vision task that automatically generates the caption for video clips with a clear understanding of the embedded semantics. Due to the extensive use of images for conveying large volumes of information, there exists a huge demand for image analytics in information processing systems. Various techniques are designed for automatic image caption generation to solve the computer vision challenges, but highlighting the contextual information more accurately still results in a challenging task. To overcome such issues, a Hybrid Attention vision Transformer-based Convolutional Neural Network-Gated Recurrent Unit-Bidirectional Long Short-Term Memory (HAVisT-CNN-GRU-BiLSTM) is developed for video caption generation. Specifically, the vision transformer (VisT) effectively captures the global dependencies and learns the intricate relationships present in the image. Moreover, the proposed approach combines the salient information extracted from the CNN, GRU, and BiLSTM models resulting in less memory usage, high dimensional data processing, and improved convergence speed. Moreover, the hybrid attention-based vision transformer facilitates capturing the global context information and enhances computational efficiency. The experimental results demonstrate that the HAVisT-CNN-GRU-BiLSTM model achieves the efficient metric values of BLEU, METEOR, and ROUGE as 0.48, 0.28, and 0.71 respectively outperforming the other existing techniques. © 2025 IEEE.

关键词： Convolutional neural networks

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CAM: Compound attention module for improved vision models 3

CAM: Compound attention module for improved vision models

引用

2024 3rd international conference on image processing, Object Detection, and Tracking, IPODT 2024

作者： Liu, Houhong Tan, Fei Du, Zhengmiao School of Automation and Information Engineering Sichuan University of Science & Engineering Yibin China Sichuan Provincial Key Laboratory of Artificial Intelligence Yibin China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

In recent years, the attention mechanism has played a significant role in enhancing algorithm performance in deep learning-based visual tasks. Most methods focus on developing more complex attention mechanisms to improve network performance, which inevitably increases the computational complexity of the model. To balance performance and computational complexity, this paper proposes the CAM (Compound Attention Module) attention mechanism, which delivers substantial performance improvements with only a slight increase in parameters. The CAM module operates in two dimensions: space and channel. It is a lightweight, plug-and-play module with minimal computational overhead. We validate our CAM module through extensive experiments on image classification and object detection tasks using the CIFAR-100 and VisDrone2019 datasets. Experimental results demonstrate that the model consistently improves image classification and object detection performance, outperforming similar modules. © 2024 SPIE.

关键词： Machine vision

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Research on Digital Media image processing and Enhancement System for Computer Deep Learning vision 3

Research on Digital Media Image Processing and Enhancement S...

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

作者： Lin, Huiyu School of Information Engineering Guangzhou Huashang Vocational College Guangdong Guangzhou510000 China

ISBN: (纸本)9798350380989

Combining the object-oriented technology and tree view function of VB6, a graphical control method based on tree view is presented. The information about the algorithm is wrapped in these graphical controls by means of wrapping. The boundary characteristics of the original image are extracted by filtering it based on the deep belief net. The mathematical model of deep belief net and its probability distribution function are established. In this project, the method of combining neural network transformation and sparse representation is proposed to carry out multi-feature fusion of image boundaries, and sigma function is added to the output layer of deep confidence network model to reduce the complexity of iterative training process. By extracting the shape gradient feature of the image boundary, the edge discrete feature is removed and the digital image boundary enhancement is realized. Experiments show that the improved image has better boundary characteristics. Under the same sample conditions, the number of iterative training steps is less, and the image edge processing effect is better than the traditional scheme. © 2024 IEEE.

关键词： deep learning digital image edge enhancement graphic algorithm control image processing platform Visual programming

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