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检索条件"任意字段=Conference on Real-Time Image Processing 2007"

共 25832 条记录，以下是91-100 订阅

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real time Performance Evaluation of Deep Learning Algorithms in image Recognition under the Pytorch Framework

Real Time Performance Evaluation of Deep Learning Algorithms...

引用

2024 International conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Lu, Di Liu, Shengzhi Xinjiang College of Science & Technology Korla China

ISBN: (纸本)9798350360660

With the rapid development of artificial intelligence technology, deep learning has become one of the key technologies in the field of image recognition. PyTorch has become the preferred framework for researchers due to its flexibility, efficiency, and ease of use. This study focuses on the real-time performance of deep learning algorithms in image recognition under the PyTorch framework, and its effectiveness is verified through system experiments. The experiment revealed that the algorithm under this framework exhibits excellent real-time performance in image recognition tasks, with an average frame rate of up to 59.46 FPS and an image processing delay as low as 59.44 milliseconds, fully meeting the demand for efficient processing in a wide range of application scenarios. This discovery demonstrates the powerful potential and practicality of PyTorch in the field of image recognition. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Dental Bitewing Radiograph Datasets: A Preprocessing Approach for AI Detection and Diagnoses

Enhancing Dental Bitewing Radiograph Datasets: A Preprocessi...

引用

conference on real-time processing of image, Depth, and Video Information

作者： Al Nassan, Wafaa Bonny, Talal Al-Shabi, Mohammad Univ Sharjah Coll Comp & Informat Sharjah U Arab Emirates Univ Sharjah Coll Engn Sharjah U Arab Emirates

ISBN: (纸本)9781510673199;9781510673182

Background: The evolution of AI applications in dental imaging, covering caries detection, anatomical structure segmentation, and pathology identification, highlights the importance of high-quality datasets for effective detection models. This paper focuses on optimizing dataset quality for real-time AI-based dental bitewing radiograph detection. Methods: We systematically analyze preprocessing methods suitable for dental bitewing radiographs, covering image enhancement, noise reduction, and contrast adjustment. These techniques are strategically chosen to address common challenges in dental radiograph images, including variations in lighting, contrast disparities, and noise fluctuations. We employ optimized algorithms to meet real-time constraints, ensuring efficient model training and inference. Results: Our study assesses the impact of each preprocessing step on dataset quality and its influence on AI model performance. Practical recommendations are provided to empower researchers and practitioners in creating datasets optimized for dental bitewing radiograph detection tasks, aiming to improve AI model accuracy while adhering to real-time requirements. In addition, a comparative analysis is conducted, evaluating datasets enhanced using conventional methods against the ResNet18 model for the segmentation of bitewing dental images. Conclusion: This paper serves as a valuable guide for the dental imaging community, offering insights into preprocessing steps that elevate dataset quality for AI-driven dental bitewing radiograph detection. By emphasizing the relevance of real-time performance and providing a comparison with conventional enhancements on the ResNet18 model, we contribute to advancing early diagnosis and enhancing oral healthcare outcomes.

关键词： image processing real-time bitewing dental images Resnet 18

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Application of Wireless Network Visual Communication Technology in Fuzzy image Target Classification Detection 2

Application of Wireless Network Visual Communication Technol...

引用

2nd IEEE International conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Zhang, Yuhao Hanyang University Visual Design Major Graduate School of Design Seoul Special City Korea Republic of

ISBN: (纸本)9798350317558

Fuzzy image target classification detection plays an important role in image processing. Traditional classification detection methods are easily affected by environmental and equipment factors, and there are certain limitations in classification accuracy and real-time performance. In order to improve the performance of fuzzy image target classification detection and image processing, this paper combines wireless network visual communication technology and utilizes multi node collaborative processing to achieve fuzzy image information processing. To verify its effectiveness, this article compares it with traditional deep learning (DL) methods. In the experimental analysis, it was found that compared to the DL method, the average accuracy of fuzzy image classification under wireless network visual communication technology improved by 9.46%. The conclusion indicates that wireless network visual communication technology can help improve the real-time performance and classification effect of blurred image targets, and can promote the intelligent development of image processing technology. © 2024 IEEE.

关键词： Blur image Feature Recognition image Classification Visual Communication Wireless Network

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Edge deployed satellite image classification with TinEViT a X-Cube-AI compatible efficient vision transformer

Edge deployed satellite image classification with TinEViT a ...

引用

conference on real-time image processing and Deep Learning

作者： Halford, Gavin Depoian, Arthur C., II Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673878;9781510673861

Lower resolutions and a lack of distinguishing features in large satellite imagery datasets make identification tasks challenging for traditional image classification models. Vision Transformers (ViT) address these issues by creating deeper spatial relationships between image features. Self attention mechanisms are applied to better understand not only what features correspond to which classification profile, but how the features correspond to each other within each separate category. These models, integral to computer vision machine learning systems, depend on extensive datasets and rigorous training to develop highly accurate yet computationally demanding systems. Deploying such models in the field can present significant challenges on resource constrained devices. This paper introduces a novel approach to address these constraints by optimizing an efficient Vision Transformer (TinEVit) for real-time satellite image classification that is compatible with ST Microelectronics AI integration tool, X-Cube-AI.

关键词： ViT STM-32 X-Cube-AI Satellite image Classification Efficient AI EuroSAT

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LIGHTWEIGHT NETWORK TOWARDS real-time image DENOISING ON MOBILE DEVICES 30

LIGHTWEIGHT NETWORK TOWARDS REAL-TIME IMAGE DENOISING ON MOB...

引用

30th IEEE International conference on image processing (ICIP)

作者： Liu, Zhuoqun Jin, Meiguang Chen, Ying Liu, Huaida Yang, Canqian Xiong, Hongkai Alibaba Grp Hangzhou Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9781728198354

Deep convolutional neural networks have achieved great progress in image denoising tasks. However, their complicated architectures and heavy computational cost hinder their deployments on mobile devices. Some recent efforts in designing lightweight denoising networks focus on reducing either FLOPs (floating-point operations) or the number of parameters. However, these metrics are not directly correlated with the on-device latency. In this paper, we identify the real bottlenecks that affect the CNN-based models' run-time performance on mobile devices: memory access cost and NPU-incompatible operations, and build the model based on these. To further improve the denoising performance, the mobile-friendly attention module MFA and the model reparameterization module RepConv are proposed, which enjoy both low latency and excellent denoising performance. To this end, we propose a mobile-friendly denoising network, namely MFDNet. The experiments show that MFDNet achieves state-of-the-art performance on real-world denoising benchmarks SIDD and DND under real-time latency on mobile devices. The code and pre-trained models will be released.

关键词： image Denoising Mobile-friendly Network Design

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A Fast image Dehazing Using Encoder–Decoder Deep Neural Network

A Fast Image Dehazing Using Encoder–Decoder Deep Neural Net...

引用

International conference on Advances in Signal processing and Communication Engineering, ICASPACE 2023

作者： Gurjar, Prakhar Pavan Kumar, Balla Kumar, Arvind National Institute of Technology Kurukshetra India

ISBN: (纸本)9789819705610

The image quality is degraded in bad weather situations such as haze or fog. This problem can affect image processing applications such as computer vision, security, and some other real-time image processing systems. Hence, image dehazing is essential for these applications to improve their performance. There are many dehazing algorithms that are implemented earlier using the atmospheric light-scattering model and enhancement-based techniques. However, these algorithms are complex and consume more execution time to perform dehazing, which isn’t fit for real-time image processing applications. To overcome this drawback, an encoder–decoder deep neural network (EDDNN) is designed in this manuscript for fast image dehazing purposes. The proposed EDDNN contains a total of four layers, they are input, encoder, decoder, and output layers. The proposed EDDNN is trained and tested with the most popular dataset called realistic single image dehazing (RESIDE). The proposed EDDNN is fast in execution that suits real-time image processing systems (RTIPS) and also effectively eliminates the haze effect from the image. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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real-time Semantic Segmentation for Autonomous Scale Cars using Mixed real and Synthetic Data 10

Real-Time Semantic Segmentation for Autonomous Scale Cars us...

引用

10th International conference on Mechatronics and Robotics Engineering (ICMRE)

作者： Ehrenfeuchter, Steffen Corlito, Roberto Marchthaler, Reiner Enzweiler, Markus Esslingen Univ Appl Sci Inst Intelligent Syst Esslingen Germany

ISBN: (纸本)9798350394283;9798350394276

This paper presents a real-time semantic segmentation framework for camera-based environment perception of objects and infrastructure elements in autonomous scale cars. It is specifically targeted towards student competitions such as the Carolo Cup or the Bosch Future Mobility Challenge. To reduce pixel-wise manual annotation efforts, our framework involves a mixture of both synthetic and real image data, carefully tuned towards the unique requirements of the given scenario. real images are acquired from a 1:10 scale vehicle equipped with a single monocular camera and are manually annotated. Synthetic image data with automatic pixel-wise annotation is obtained via a custom Unity-based simulation pipeline. We evaluate various mixed real-synthetic data strategies to train different state-of-the-art deep neural networks with a focus on both segmentation performance and real-time capability using an NVIDIA Jetson AGX Xavier platform as in-vehicle test bed. Our experimental results show a significant improvement in semantic segmentation performance of the mixed real-synthetic data approach at real-time speeds of approximately 60 FPS on the target platform.

关键词： Mobile Robotics Autonomous Systems Robot Intelligence and Learning Signal and image processing

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Lossy Coding for Spatially Adaptive Conditioning in Semantic image Communication

Lossy Coding for Spatially Adaptive Conditioning in Semantic...

引用

2024 conference on Visual Communications and image processing

作者： Eteke, Cem Griessel, Alexander Kellerer, Wolfgang Steinbach, Eckehard Tech Univ Munich Sch Computat Informat & Technol Chair Media Technol D-80333 Munich Germany Tech Univ Munich Sch Computat Informat & Technol Chair Commun Networks D-80333 Munich Germany Tech Univ Munich Sch Computat Informat & Technol Munich Inst Robot & Machine Intelligence D-80333 Munich Germany

ISBN: (纸本)9798331529543;9798331529550

The increasing demand for high-quality, real-time visual communication and the growing user expectations, coupled with limited network resources, necessitate novel approaches to semantic image communication. This paper presents a method to enhance semantic image communication that combines a novel lossy semantic encoding approach with spatially adaptive semantic image synthesis models. By developing a model-agnostic training augmentation strategy, our approach substantially reduces susceptibility to distortion introduced during encoding, effectively eliminating the need for lossless semantic encoding. Comprehensive evaluation across two spatially adaptive conditioning methods and three popular datasets indicates that this approach enhances semantic image communication at very low bit rate regimes.

关键词： semantic communication lossy semantic coding semantic augmentation image synthesis

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EdgeRelight360: Text-Conditioned 360-Degree HDR image Generation for real-time On-Device Video Portrait Relighting

EdgeRelight360: Text-Conditioned 360-Degree HDR Image Genera...

引用

IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lin, Min-Hui Reddy, Mahesh Berger, Guillaume Sarkis, Michel Porikli, Fatih Bi, Ning Qualcomm AI Res San Diego CA 92121 USA

ISBN: (纸本)9798350365474

In this paper, we present EdgeRelight360, an approach for real-time video portrait relighting on mobile devices, utilizing text-conditioned generation of 360-degree high dynamic range image (HDRI) maps. Our method proposes a diffusion-based text-to-360-degree image generation in the HDR domain, taking advantage of the HDR10 standard. This technique facilitates the generation of high-quality, realistic lighting conditions from textual descriptions, offering flexibility and control in portrait video relighting task. Unlike the previous relighting frameworks, our proposed system performs video relighting directly on-device, enabling real-time inference with real 360-degree HDRI maps. This on-device processing ensures both privacy and guarantees low runtime, providing an immediate response to changes in lighting conditions or user inputs. Our approach paves the way for new possibilities in real-time video applications, including video conferencing, gaming, and augmented reality, by allowing dynamic, text-based control of lighting conditions.

关键词： Video conferencing

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An interior design framework utilizing image processing and virtual reality technologies 2

An interior design framework utilizing image processing and ...

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2nd International conference on Physics, Photonics, and Optical Engineering, ICPPOE 2023

作者： Lei, Lei Chongqing Three Gorges Vocational College Chongqing China

ISBN: (纸本)9781510674684

With the rapid development of science and technology, Virtual reality (VR) technology based on image processing shows great potential and innovation in the field of interior design. The purpose of this paper is to explore the application of VR technology based on image processing in interior design system and analyze its potential impact on interior design field. Through the in-depth study of real-time rendering, dynamic layout adjustment, virtual and real space integration and other functions, we show the outstanding achievements of this technology in improving design efficiency, optimizing user experience and promoting design innovation. In the aspect of technological innovation, this paper emphasizes the influence of the continuous progress of image processing technology on the real-time rendering of interior design system. Through an example, we show that users can perceive the design effect in real time in the virtual environment and realize a more intuitive and immersive design experience. In addition, through dynamic layout adjustment, users can modify the indoor layout in real time through gesture recognition technology, thus participating in the design process more flexibly. The integration of virtual and real space is another key research point, and the deep integration of virtual furniture and design elements with real space is realized through VR technology. This enables users to personally experience the design effect in the actual environment and provides designers and customers with more realistic visual feedback. The research of VR technology based on image processing in interior design system not only provides technical innovation, but also brings more efficient, smarter and more interactive design experience to the interior design field, injecting new impetus into digital transformation and intelligent development. © COPYRIGHT 2024 SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Virtual reality

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