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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

共 3306 条记录，以下是411-420 订阅

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A Survey of applications of vision Transformer and its Variants

A Survey of Applications of Vision Transformer and its Varia...

引用

Intelligent Data and Security (IDS), IEEE International conference on

作者： Chuang Wu Tingqin He Hunan Key Laboratory for Service computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology

ISBN: (数字)9798350389487

ISBN: (纸本)9798350389494

The Transformer architecture, renowned for its efficacy in natural language processing, encounters unique hur-dles when applied to computer vision. In response, the vision Transformer (vi $T$ ) emerges as a successful adaptation for image classification tasks. While viT exhibits tremendous potential in revolutionizing computer vision, addressing its inherent chal-lenges and limitations stands as a critical endeavor. This compre-hensive survey meticulously scrutinizes the drawbacks associated with viT, proposing bespoke adaptations tailored to specific applications while showcasing their remarkable performance across diverse visual tasks. Moreover, it delves into the evolution of viT adaptations across various visual domains, elucidating four promising directions for future research and development in this dynamic field.

关键词： Surveys Computer vision visualization Computational modeling Transformers Real-time systems Data models

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Future of Fashion: AI-Powered virtual Dressing for E-Commerce applications

Future of Fashion: AI-Powered Virtual Dressing for E-Commerc...

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2024 International conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Nitasha, Er. Kumari, Sneha Kumar, Aditya Bhardwaj, Rizul Maddheshiya, vinita Khan, Altamas Chandigarh University Department of Computer Science and Engineering Punjab Mohali India

ISBN: (纸本)9798350376470

This paper introduces an innovative AI-powered virtual Dressing technology designed to revolutionize the way consumers shop for clothing online. Utilizing advanced computer vision techniques and machine learning models, our system offers a seamless virtual dressing experience. The process begins with high-resolution image capture, followed by dynamic resizing for optimal processing. A sophisticated cloth segmentation method is employed to accurately identify and separate the clothing item from the background. Utilizing the models for cloth segmentation and semantic segmentation, combined with posing coordinates, our system ensures precise body posture and garment alignment. The removal of background elements is achieved through semantic segmentation masks, enhancing the realism of the virtual experience. The final virtual dressing visualization supports both scenarios of retaining or omitting the background, based on user preference. To assess consumer interest in such technology, we conducted a survey which revealed that a vast majority of online shoppers would consider buying clothing if they could see a virtual picture of themselves in the attire beforehand. This finding underscores the potential of our AI -powered virtual Dressing technology to not only enhance the online shopping experience by providing a realistic visualization of clothing fit but also significantly reduce return rates due to size or fit dissatisfaction. Our experiments demonstrate the system's efficacy in creating realistic and customizable virtual dressing experiences, showcasing its potential to become an integral part of future e-commerce platforms. © 2024 IEEE.

关键词： Semantic Segmentation

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Maritime image Stabilization: A Comprehensive Review of Techniques and Challenges

Maritime Image Stabilization: A Comprehensive Review of Tech...

引用

Electronic Technology and Information Science (ICETIS), International conference on

作者： Enping Wei Yong Chai Tan vin Cent Tai Yanan Hao Xiaodong Zhang Tian Zhang Centre for Modelling and Simulation Faculty of Engineering Built Environment and Information Technology SEGI University Kuala Lumpur Malaysia

ISBN: (数字)9798350388343

ISBN: (纸本)9798350388350

image stabilization plays a crucial role in providing accurate and reliable visual information for machine vision applications. In maritime applications, such as unmanned ship navigation, where six degrees of freedom (DOF) motion and harsh maritime conditions prevail, the efficacy of image stabilization technology is vital for robust image processing algorithms. This paper offers a comprehensive review of image stabilization techniques tailored for maritime environments, developed over the past two decades. We analyzed a total of 39 research articles on the subject, sourced from Web-of-Science, SCOPUS, and the Engineering Index databases, discussing potential research directions to address the limitations of current image stabilization methods, with special consideration for the unique requirements of ship-borne cameras. It provides an up-to-date overview of the techniques, limitations, and algorithms of ship-borne cameras for maritime applications, identifying current knowledge gaps and areas requiring further research. This review aims to guide the development of new technologies and methods to improve the performance of image stabilization systems in maritime contexts.

关键词： vibrations visualization Reviews Target recognition machine vision Stability criteria Object detection

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Authentication and Verification in Human-Robot Cooperative Robotic Cells using Stereo vision and Gesture Control

Authentication and Verification in Human-Robot Cooperative R...

引用

International image processing, applications and Systems conference (IPAS)

作者： Gábor Kovács Tamás Szirányi Machine Perception Research Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

The integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose a novel approach to address these challenges by using stereo vision and gesture control in cooperative robotic cells. Our system enables seamless authentication of operators and real-time verification of task execution, ensuring compliance with established protocols and safety *** features of our system include its gesture-based operation with gesture recognition algorithms, allowing operators to interact with robotic systems intuitively and efficiently. By leveraging stereo vision, our system accurately tracks the operators’ movement within the workspace, facilitating precise task execution and object *** present a detailed description of our system architecture, experimental configuration, and real-world performance assessment. Our results demonstrate the effectiveness and feasibility of our approach in enhancing operational efficiency, ensuring quality, and improving the overall user experience in industrial automation.

关键词： Automation Service robots Tracking Human-robot interaction Authentication Systems architecture User experience Real-time systems Stereo vision Safety

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Incremental Learning Model Based on Ensemble Learning

Incremental Learning Model Based on Ensemble Learning

引用

image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Tong Zhang Weiqiang Wu College of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Currently, deep learning neural networks have shown remarkable performance in the field of image classification. However, existing classification networks are typically applied within closed environments, where all collected samples are batch-trained to produce classification results. In practical applications, the categories of samples are not static but dynamically increase over time. With the continual addition of new classes, the current classification methods exhibit limited continual learning capabilities, making them unsuitable for applications with stringent real-time requirements. To address this issue, this paper investigates an incremental learning algorithm based on ensemble transfer learning to handle classification training for newly added categories. This algorithm leverages the strengths of ensemble transfer learning to enhance the network's ability to continually learn new categories, enabling fast and accurate classification of new samples. In this paper, the proposed algorithm is applied to the CUB-200-2011 dataset for class-incremental experiments and compared with other deep learning models. Experimental results demonstrate that the incremental learning classification algorithm based on integrated transfer outperforms other models.

关键词： Deep learning Training Incremental learning machine learning algorithms Accuracy Computational modeling Transfer learning Neural networks Classification algorithms image classification

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Design and Implementation of machine vision Experiment Platform for virtual Production Line

Design and Implementation of Machine Vision Experiment Platf...

引用

virtual Reality (ICVR), IEEE International conference on

作者： Jinfang Li Mingtong He Jiancong Su Boyang Wang Zhenxian Li School of Electromechanical Engineering Guangdong University of Technology Guangzhou China

To meet the needs of teaching and practical applications in machine vision technology, a virtual reality-based machine vision experimental platform has been designed and developed. Unity3D was utilized as the development engine, and image processing technology was integrated to achieve the construction of virtual production line scenes, simulation of vision component parameter adjustments, and image acquisition. The platform features a graphical programming interface for visualizing image processing algorithms, which can be used to perform visual debugging of vision stations with a virtual robot system driven by software PLC. This machine vision experimental platform ensures the consistency between simulation and actual engineering processes, and enables students to explore different vision schemes on an industrial production line, thereby avoiding constraints on location, time, and equipment in related experiments.

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PCASNet: Polarized Cross-scale Attention Self-attention Network for Lightweight Ultrasound Medical image Segmentation

PCASNet: Polarized Cross-scale Attention Self-attention Netw...

引用

image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Qingxue Zhao Di Wu Jun Tian College Of Software Nankai University Tianjin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

U-Net and its extensions have achieved significant success in medical image segmentation but face limitations in capturing global context and transferring cross-scale features. To address these challenges, we propose a lightweight network, PCASNet, which integrates the Polarized Cross-scale Attention Module (PCAS Module) and the Dynamic Multi-scale Attention Gate (DMAG). The PCAS Module enhances global context modeling by fusing features from distant spatial positions, while the DMAG improves segmentation performance by filtering redundant features and emphasizing critical information, thereby strengthening global information modeling and feature selection capabilities. Experiments on breast and thyroid ultrasound datasets demonstrate that PCASNet outperforms traditional image segmentation algorithms in both accuracy and efficiency, highlighting its potential for applications in ultrasound medical imaging.

关键词： image segmentation Ultrasonic imaging Computational modeling Computer architecture Logic gates Convolutional neural networks Context modeling Biomedical imaging Principal component analysis Thyroid

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LFRNet: Low-Light Face Super-Resolution With Light Frequency Representation

LFRNet: Low-Light Face Super-Resolution With Light Frequency...

引用

Algorithm, image processing and machine vision (AIPMV), International conference on

作者： Bingxin Zha Huijie Zhu Haolin Zhang Shengying Yang School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Huzhou Zhongke Fanzai Electric Power Technology Development Co. Ltd Huzhou China Arm Technology (China) Co. Ltd Shanghai China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

In the field of computer vision, the task of facial super-resolution (FSR) is crucial for applications such as surveillance and photo restoration. However, factors such as noise and artifacts in real-world scenarios severely degrade image quality. Although existing methods use geometric priors and facial heatmap localization to improve FSR performance, these priors are often inaccurate under low-light conditions, affecting the results. This paper proposes an innovative low-light facial enhancement network aimed at integrating FSR and low-light enhancement tasks. We designed a Light Frequency Inference Block (LFIB) to capture and refine brightness and texture features at different frequencies, combining it with Transformer modules in an Encoder-Decoder architecture. The LFIB module separates degraded images into low-frequency and high-frequency brightness features and employs spatial cross-attention to capture facial texture details, effectively addressing the degradation issues in facial images. Experimental results demonstrate that our method excels in the low-light facial super-resolution task, outperforming existing methods on various metrics. Additionally, it shows good generalization capabilities in real-world scenarios, confirming its potential for practical applications.

关键词： Measurement image quality visualization Superresolution Brightness Computer architecture Transformers Feature extraction image restoration Faces

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COviD-19 Infection Percentage Prediction via Boosted Hierarchical vision Transformer 21st

COVID-19 Infection Percentage Prediction via Boosted Hierarc...

引用

21st International conference on image Analysis and processing (ICIAP)

作者： Hsu, Chih-Chung Dai, Sheng-Jay Chen, Shao-Ning Natl Cheng Kung Univ 1 Univ Rd Tainan Taiwan

ISBN: (纸本)9783031133244;9783031133237

A better backbone network usually benefits the performance of various computer vision applications. This paper aims to introduce an effective solution for infection percentage estimation of COviD-19 for the computed tomography (CT) scans. We first adopt the state-of-the-art backbone, Hierarchical visual Transformer, as the backbone to extract the effective and semantic feature representation from the CT scans. Then, the non-linear classification and the regression heads are proposed to estimate the infection scores of COviD-19 symptoms of CT scans with the GELU activation function. We claim that multi-tasking learning is beneficial for better feature representation learning for the infection score prediction. Moreover, the maximum-rectangle cropping strategy is also proposed to obtain the region of interest (ROI) to boost the effectiveness of the infection percentage estimation of COviD-19. The experiments demonstrated that the proposed method is effective and efficient.

关键词： COviD-19 Computed tomography Deep learning Transformer

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A TinyML based Residual Binarized Neural Network for real-time image Classification 6

A TinyML based Residual Binarized Neural Network for real-ti...

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6th International conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Srinivasan, C. Sridhar, P. Priya, V. Hari Swathi, S. Saveetha Engineering College Department of Ece Tamilnadu Chennai India Sri Ramakrishna Engineering College Department of Ece Tamilnadu Coimbatore India

ISBN: (纸本)9781665482714

image processing is an important requirement in IoT applications such as robotics, augmented reality, computer vision, Industry 4.0 etc. The capabilities of IoT devices for image processing are limited to sensing the environment, processing and communicating the results. Tiny machine learning (TinyML) is a new paradigm that takes advantage of the IoT to deploy deep learning models to perform complex tasks in resource constrained embedded devices. image classification is an important task in IoT to interpret images of a particular scene or class. Currently, this task is performed in embedded devices using Binarized Neural Networks (BNNs), which can be converted to a set of weights using a one-hot encoding process. These networks integrated with hardware accelerators can be trained to perform image processing tasks in real-time. This paper proposes a BNN for image classification based on residual learning paradigm, called Tiny-BNN which exploits the skip connections to reduce information loss, and improve the training time and accuracy. Experimental results show that the model achieves a classification accuracy of 90.1 % and 91.6% on the on CIFAR-10 and MNIST datasets respectively. © 2022 IEEE.

关键词： image classification

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