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

共 1699 条记录，以下是241-250 订阅

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Investigating the Effects of image Restoration Algorithms on image Quality in Digital image processing 3

Investigating the Effects of Image Restoration Algorithms on...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Dhabliya, Dharmesh Saini, Rajat Yadav, Dhyan Geetha, G. Singh, Baldev Sripriya, T. Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350319125

This study examines the effect of various image recovery algorithms on photo great in digital photo processing. Especially, the consequences on the visual quality of the photographs are studied through diverse metrics along with contrast, brightness, sharpness, and color saturation. Additionally, the algorithms are compared to every other as a way to decide which algorithm produces the quality effects. The studies will use images from public picture databases and effects can be gathered the use of photo first-rate evaluation software. Moreover, diverse noise and distortion distortions are applied to the photos to be able to investigate the impact of the distinctive algorithms on them. By means of determining variations in photograph first-rate, beneficial insights at the performance of numerous image recuperation algorithms can be obtained. In conclusion, this studies will offer a complete evaluation of the performance of diverse picture recovery algorithms utilized in digital photograph processing, losing light on which algorithms satisfactory enhance the visual nice of snap shots. © 2023 IEEE.

关键词： Complete Losing processing Satisfactory Visual

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The application of super-resolution image recognition technology in large-scale vehicle-To-vehicle communication 3

The application of super-resolution image recognition techno...

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3rd international conference on Advanced Algorithms and Signal image processing, AASIP 2023

作者： He, Shan Tang, Wenjun Gao, Yanfeng Li, Xun Shenzhen Power Supply Bureau Co Ltd. Shenzhen518000 China China Southern Power Grid Electric Vehicle Service Co Ltd. Shenzhen518000 China

ISBN: (纸本)9781510668522

With the increasing number of vehicles on the road, traffic congestion and safety issues have become more and more prominent. Super-resolution image recognition technology is a effective solution that can be used for image analysis and recognition in large-scale vehicle-To-vehicle communication. This paper introduces the basic principles and application scenarios of super-resolution image recognition technology and elaborates on its application in large-scale vehicle-To-vehicle communication. Experimental results show that this technology can effectively improve the accuracy and efficiency of image recognition and has broad application prospects. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image recognition

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Bone image Analysis For Early Detection of Osteoporosis 3

Bone Image Analysis For Early Detection of Osteoporosis

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Rekha, B.S.S.Kiran Reddy, A.Madhumitha Vaishnavi, Ch. Sreethiskesh, A. Babu, Chunduri Madhu B v Raju Institute of Technology Dept. of Computer Science Engineering Medak Telangana Narsapur India

ISBN: (纸本)9798350319125

Numerous studies believe that bone mineral density (BMD) is the only technique that fundamentally reveals bone strength, despite the fact that the latter provides important information regarding the risk of osteoporotic fracture. Analyzing macro- and micro-structural characteristics quantitatively may improve beyond simply estimating bone density. Bone structural information can be obtained using non-interfering, non-carnage procedures. According to many research, bone mineral densitometry (BMD), which also provides important information on osteoporosis, primarily measures bone strength. Numerous works in the field of photo-domain computer vision have introduced efficient strategies that benefit from different viewpoints on the same instance in other domains such as self-supervised and semi-supervised learning. In order to tackle these problems we are using a simple yet efficient method termed as multi-view ct network or mvctnet along with multimodal data fusing technique called Early Fusion. Utilizing cutting-edge methods and technologies will improve image analysis in multi-view CT scanning for osteoporosis detection, increasing the precision and effectiveness of osteoporosis detection. © 2023 IEEE.

关键词： Bone imaging Bone mineral density Bone quality Bone structure Computed tomography (CT) Dual X-ray absorptiometry radiography Early Fusion Technique image Fusing Machine learning Micro computed tomography Multimodal data fusion Osteoporosis Quantitative computed tomography

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Enhanced Efficiency and Scalability in WIM Systems using Smart Sensors and Centralized processing 3

Enhanced Efficiency and Scalability in WIM Systems using Sma...

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3rd IEEE international conference on Electronic engineering, ICEEM 2023

作者： Abosreea, Mohamed S. Elshenawy, Lamiaa M. Hashim, Ibrahim H. Faculty of Electronic Engineering Menoufia University Industrial Electronics and Control Engineering Department Egypt Faculty of Engineering Menoufia University Civil Engineering Department Egypt

ISBN: (纸本)9798350323511

Intelligent transportation systems (ITS) strive to offer innovative services encompassing various modes of transport and traffic management. In ITS, the availability of traffic flow load information, including vehicle weight and spatial-temporal data, holds great significance. The Weigh-in-motion (WIM) system has emerged as a solution to preserve road pavement, extend infrastructure lifespan, identify offending vehicles, and reduce detrimental effects such as emissions, vibrations, noise, and fuel consumption. This paper presents the development of a WIM system that utilizes smart sensors to capture vehicle dimensions, weight, and speed. Additionally, image processing techniques are employed for license plate number recognition through a central processing approach. Furthermore, a cloud-based vehicle tracking system is employed to enhance traffic safety and management by obtaining spatial-temporal information. © 2023 IEEE.

关键词： Intelligent systems

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Full-scale feature fusion dark-light image enhancement network 3

Full-scale feature fusion dark-light image enhancement netwo...

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3rd international conference on Advanced Algorithms and Signal image processing, AASIP 2023

作者： Huang, Tingting Zeng, Shangyou School of Electronic and Information Engineering School of Integrated Circuits Guangxi Normal University Guilin Guangxi541001 China

ISBN: (纸本)9781510668522

Aiming at the problems of narrow dynamic range, serious noise and color deviation in dark light images, a dark light image enhancement network based on supervised learning is proposed. The enhancement process is divided into three steps: layer decomposition, reflection recovery, and illumination adjustment. Combined with Retinex theory, three-layer Unet++ is designed to decompose the image into reflection component and illumination component. The reflection recovery firstly combines the light-guided attention mask to assign new weights to the reflection components, and then sends them to the full-scale U-shaped network for image denoising and detail restoration. Illumination adjustment adjusts the illumination adaptively. This paper is validated on several datasets with improved subjective and objective evaluation results, which can effectively suppress noise and distortion problems, and significantly improve image brightness and quality. © COPYRIGHT SPIE.

关键词： image enhancement

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Smart Road Damage Survey System Using Machine vision and IoT Technology 3

Smart Road Damage Survey System Using Machine Vision and IoT...

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3rd international conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Mutijarsa, Kusprasapta Alunjati, Figo Agil Hidayat, Fadhil Kurnia, Ranna Arsyad, Zulkifli School of Electrical Engineering and Informatics Bandung Indonesia National Institute of Technology Department of Civil Engineering Bandung Indonesia Bandung State Polytechnic Department of Computer and Informatics Engineering Bandung Indonesia

ISBN: (纸本)9798350394559

Road is an essential transportation infrastructure to move people and goods to support economic prosperity. The efficiency, safety, security, and comfort of people have significant consequences by damage and defects on the road surface. A good-condition road ensures the land traffic runs well. This research is a collaboration between the Bandung Institute of Technology and the Department of Public Works and Spatial Planning of West Java Province to develop a smart system for road damage surveys. The system was built by leveraging machine vision and IoT technologies to improve the performance of the road survey activities. The previous road damage detection, classification method, and pavement condition index (IKP) computation were performed semi-automatically. The proposed survey system developed in this work uses the Canny edge Detection algorithm and MobileNet V2 architecture to detect road defects automatically. The pavement condition index computation also improved by using the interpolation reduction factor. The system is designed into a four-layer architecture that consists of several functions and modules, i.e., the sensor system that includes a vision sensor, GPS and odometry, a hardware embedded system using a microcontroller, and machine learning to process the road condition image automatically, and a dashboard application to reveal the road condition on map. The performance resulted in accuracy, precision, recall, and F-score of the road damage detection are 54.5%, 24.2%, 78.9%, and 37%, respectively. © 2023 IEEE.

关键词： Computer vision

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A Parallel Sea-Land Segmentation Method Based on improved Unet Model 3

A Parallel Sea-Land Segmentation Method Based on Improved Un...

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

作者： Zhang, Xuebo Jiang, Min Li, Jinli School of Space Information Space Engineering University Beijing China 96944 troops Chinese People's Liberation Army Beijing China

ISBN: (纸本)9781510667563

Pixel-level sea-land segmentation on high-resolution remote sensing images is a basic task in remote sensing applications and is of great significance for coastline extraction and near-shore marine target detection. This paper proposes an improved Unet model for sea-land segmentation on high-resolution remote sensing images. This model outperforms UNet++ and other models in sea-land segmentation accuracy when applied to the HRSC2016-SL dataset. Based on this model, a parallel sea-land segmentation processing algorithm was developed, whose parallel efficiency reached 49.5% on 16K*14K remote sensing images. To complete this study, a parallel processing system for sea-land segmentation was developed, which achieved distributed and single-machine multi-core parallel sea-land segmentation tasks on high-resolution remote sensing images. © 2023 SPIE.

关键词： image segmentation

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UAV Intelligent Forest Inspection System Based on Computer vision 3

UAV Intelligent Forest Inspection System Based on Computer V...

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3rd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Wu, Di Yuan, Xue Guan, Licong Beijing Jiaotong University Beijing China

ISBN: (纸本)9781665472784

The forestry inspection adopted in China at this stage is still mainly based on manual inspection, which is not only inefficient, but also labor-intensive and has high safety risks for the inspectors. Therefore, this paper proposes a UAV intelligent forest inspection system based on computer vision. However, the application of this UAV intelligent forestry inspection system in complex scenarios has the problems of low model detection accuracy and slow detection speed. To address the problem of low model detection accuracy due to unbalanced data categories and inaccurate labeling, this paper mainly proposes a data cleaning algorithm based on image operation to improve the quality of data labeling, thus improving the object detection accuracy. To address the problem of slow detection speed, this paper proposes a light-weight structure improvement algorithm based on SOLOv2, which uses grouped convolution to improve the detection speed on the basis of ensuring the object detection accuracy, and uses TensorRT for hardware acceleration. Experiments show that the detection accuracy of the improved model is improved by 1.7%, and the reasoning speed is improved by 14.2 frames. © 2023 IEEE.

关键词： Inspection

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Comparison of Large-Scale Pre-Trained Models based ViT, Swin Transformer and ConvNeXt 3

Comparison of Large-Scale Pre-Trained Models based ViT, Swin...

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3rd international conference on Artificial Intelligence and Computer engineering, ICAICE 2022

作者： Yu, Jiapeng Faculty of Science and Technology Department of Data Science Beijing Normal University-Hong Kong Baptist University United International College 2000 Jintong Road Tangjiawan Guangdong Province Zhuhai China

ISBN: (纸本)9781510663473

In the field of computer vision, deep learning has developed tremendously, large-scale preforming has received increasing attention from experts and researchers. Different training models often have large performance gaps in training speed and accuracy when performing large-scale pre-training. In this case, choosing the appropriate model for large-scale pre-training is particularly important. This experiment uses the same image data set and the same hardware conditions to construct the image classification model respectively in the three mainstream image recognition large-scale pre-training models, vision Transformer (VIT), Swin-Transformer and ConvNeXt, try to analyze the advantages and disadvantages of each model by experimental results. It is observed that vision Transformer has the fastest running speed in computer vision classification experiments, but its accuracy is not as good as the other two models, Swin-Transformer has the slowest speed and average accuracy, ConvNeXt has the highest accuracy, but its speed is mediocre. The results of this experiment have some reference significance for future model selection for large-scale pre-training tasks in computer vision, this can decrease training time and improve training accuracy to some extent. © 2023 SPIE.

关键词： Computer vision

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Vegetation Coverage and Urban Amenity Mapping Using Computer vision and Machine Learning

Vegetation Coverage and Urban Amenity Mapping Using Computer...

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3rd international conference on image processing and vision engineering, improve 2023

作者： Karkut, Nicholas Kiriluk, Alexey Zhang, Zihao Zhu, Zhigang Computer Science Department The City College of New York - CUNY New YorkNY10031 United States Graduate Landscape Architecture Program The City College of New York - CUNY New YorkNY10031 United States PhD Program in Computer Science The Graduate Center - CUNY New YorkNY10016 United States

ISBN: (纸本)9789897586422

This paper proposes a computer vision-based workflow that analyses Google 360-degree street views to understand the quality of urban spaces regarding vegetation coverage and accessibility of urban amenities such as benches. image segmentation methods were utilized to produce an annotated image with the amount of vegetation, sky and street coloration. Two deep learning models were used -- Monodepth2 for depth detection and YoloV5 for object detection -- to create a 360-degree diagram of vegetation and benches at a given location. The automated workflow allows non-expert users like planners, designers, and communities to analyze and evaluate urban environments with Google Street Views. The workflow consists of three components: (1) user interface for location selection;(2) vegetation analysis, bench detection and depth estimation;and (3) visualization of vegetation coverage and amenities. The analysis and visualization could inform better urban design outcomes. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Vegetation mapping

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