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共 11503 条记录，以下是481-490 订阅

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Single Stage Multi-pose Virtual Try-on Network with Deformable Attention

Single Stage Multi-pose Virtual Try-on Network with Deformab...

引用

2023 International conference on machine vision, image processing and Imaging Technology, MVIPIT 2023

作者： Liu, Siyu Lu, Peiyan Wu, Zizhao Hangzhou Diarvi University School of Media and Design Hangzhou China

ISBN: (纸本)9798350306545

Current methods for image-based virtual try-on are limited to a single-pose scenario, where they generate an image of a source people wearing a given garment. However, this limitation restricts their practical applicability, as they cannot generate try-on images for different poses of the person. To overcome this limitation, we present MDAVTON, a single stage multi-pose virtual try-on network with deformable attention. Our framework incorporates two key designs. Firstly, we introduce a flow estimation module that calculates the garment's flow and the person's flow using deformable attention machine. Specifically, the flow estimation module computes the two-dimensional flow fields by sampling from a fixed number of key points and estimating the weights of the sampled points. These sparse attentional weights are applied to the corresponding sampled points so that the sampled points are gradually biased to the points with the strongest correspondence to the source feature. Secondly, we design a conditional generation module to refine the generation results at the pixel level. This module ensures that the generated images are both realistic and capable of retaining the details of both the deformed garment and the deformed person. Through extensive experiments on traditional benchmarks, we have quantitatively and qualitatively validated the efficacy of our framework in context of the multi-view try-on task. © 2023 IEEE.

关键词： Computer vision

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Corrosion Recognition of Transmission Line Armour Clamp Based on Computer vision 2

Corrosion Recognition of Transmission Line Armour Clamp Base...

引用

2nd International conference on Intelligent Perception and Computer vision, CIPCV 2024

作者： Bo, Xu Yijun, Wei Maintenance Branch State Grid Jiangxi Electric Power Company Nanchang China

ISBN: (纸本)9798350360097

Due to the complex environment and special corrosion reasons, the metal parts such as insulation fasteners and fittings of transmission lines have significant differences in the location and shape of corrosion, making it very difficult to detect. With the gradual application of autonomous inspection drones in line detection, it is possible to use machine vision to identify the corrosion situation of Peking Opera. In response to the difficulty of image recognition detection in auxiliary and food detection, this paper proposes an algorithm that combines object detection with collar features to improve the applicability and efficiency of digital image processing in metal external characteristic detection. The main methods include deep learning and corrosion detection. The experimental results show that the comprehensive algorithm can achieve good recognition effect and high recognition accuracy for common metal corrosion features in the text. The combination of deep learning and color features has greatly improved the detection of complex metal corrosion points and the recognition of corrosion degree, providing technical ideas for the daily management and maintenance of related metals in the electrical industry. © 2024 IEEE.

关键词： image enhancement

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2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Zhu, Maosheng Ni, Ruijie Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China

ISBN: (纸本)9798350331417

image key information extraction is an important technique in automatic digital image recognizers. To deal with complex image layout, vague image semantics and image defects, we propose a layout similarity-based model to extract image key information under certain image distortion situations. Our model uses perceptual hash algorithm (PHA) to detect visually similar images and uses a template matching based algorithm to solve distortion problems when PHA cannot find a match. The framework is tested on a dataset with more than 17,000 images and we demonstrate that our model can reach 99.5% accuracy on the original dataset, significantly outperforming Chargrid model and a parity-based model. We also compare the performance of different models on images with artificially overlaid boxes, which is commonly seen on screenshots. Results show that our model still performs much better than the parity-based model. © 2023 IEEE.

关键词： document understanding image key information extraction image text detection image text recognition template matching

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International conference on Optical Communication and Optoelectronic Technology, OCOT 2024

International Conference on Optical Communication and Optoel...

引用

2024 International conference on Optical Communication and Optoelectronic Technology, OCOT 2024

ISBN: (纸本)9781510683266

The proceedings contain 36 papers. The topics discussed include: research on the application of unmanned aerial vehicle monitoring based on computer vision technology in environmental protection management during the construction period of pumped storage power stations;research on two-dimensional code cable equipment information extraction and security verification technology based on image processing algorithm;research on the design of a virtual simulation experimental platform based on digital image processing;optimization of urban public safety and disaster prevention strategies based on deep learning algorithms;application of drone technology in traditional village conservation research - taking Fanjiazhuang Village in Jinzhong City, Shanxi Province as an example;and research on substation lifting safety early warning technology based on machine vision.

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The future of wavelets in medical image processing 30

The future of wavelets in medical image processing

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30th International conference on Systems, Signals and image processing, IWSSIP 2023

作者： Petrovska, Martina Zlatkova, Aleksandra Taskovski, Dimitar Ivanovski, Zoran Ss. Cyril and Methodius University in Skopje Faculty of Electrical Engineering and Information Technologies Skopje Macedonia

ISBN: (纸本)9798350337297

with the accelerated growth of harvesting the capabilities of machine learning and machine vision in the medical field, numerous models and networks have been proposed for handling such a sensitive and complex problems. At the same time, the common problem among the field of medical image processing, whether it be classification, localization or segmentation, is the lack of available datasets. Often, the depth and complexity of the popular and most used networks overpower the small datasets and give insufficient results. To alleviate the problem, we designed a variation of the UNET architecture for a limited dataset based on wavelet transform. The paper focuses on the use of wavelet transform on the input images before they are used in the proposed network. The technique is demonstrated using lumbar spine MRIs. Final outcomes show similar, in some cases even better results using less floating point operations and trainable parameters. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Research on Real-time Positioning and Control of Automatic Navigation Car Based on OpenMV vision System 24

Research on Real-time Positioning and Control of Automatic N...

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2024 International conference on machine Learning, Pattern Recognition and Automation Engineering, MLPRAE 2024

作者： Liang, Zhitao Li, Chenye Ge, Zijie Qiu, Jiawen Qiu, Guangping Zhujiang College of South China Agricultural University China

ISBN: (纸本)9798400709876

With the rapid development of machine vision and embedded technology, real-time positioning technology is increasingly important in the field of automatic navigation. This study developed a real-time positioning and control system for an automatic navigation car utilizing the OpenMVH7Plus camera and STM32 microcontroller. The system utilizes the high-performance image processing capability of OpenMVH7Plus, combined with color recognition and distance measurement technology, to achieve real-time perception of the environment and precise navigation of the car. Additionally, the system adopts incremental and positional PID control algorithms to ensure stable driving and precise parking in various environments. Experimental results demonstrate that with optimized image processing algorithms and precise control strategies, the car can efficiently locate and track specific targets in complex environments. This paper details the design scheme, key technical implementations, and experimental evaluations of the system, verifying its feasibility and effectiveness. © 2024 ACM.

关键词： Three term control systems

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Determining the route and roadway condition in front of autonomous vehicle 3

Determining the route and roadway condition in front of auto...

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3rd International conference on Digital Technologies, Optics, and Materials Science, DTIEE 2024

作者： Nikitenko, Mikhail S. Khudonogov, Danila Yu Popinako, Yaroslav V. Kizilov, Sergey A. Federal State Budgetary Scientific Institution "The Federal Research Center of Coal and Coal chemistry Siberian Branch Russian Academy of Science" 18 Sovetsky ave. Kemerovo Russia

ISBN: (纸本)9781510681590

The article presents results of route detection and roadway conditions in front of autonomous vehicle using machine vision system combining image with light markers creating grid of laser lines. Tracking the roadway parameters is implemented by registering the light markers distortion. Also, an analysis of illuminance effect to green and red laser lines shape detection quality in concrete surface with machine vision is given. The experimental results of the illumination effect on the quality of laser line recognition by machine vision inside industrial premises for a concrete surface are shown. To achieve high-quality recognition, the main variable parameters are recursively changed: image filtering, binary threshold, morphology functions. © 2024 SPIE.

关键词： Computer vision

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Lightweight TransUNet Informal Residential Area segmentation 2

Lightweight TransUNet Informal Residential Area segmentation

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2nd International conference on machine vision, image processing and Imaging Technology, MVIPIT 2024

作者： Zhang, Shuo Jia, Yiru Liu, Zhaoying Yang, Fei School of Computer science Beijing University of Technology Beijing China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331543037

Remote sensing image informal residential area segmentation plays an important role in urban planning, environmental detection, and disaster assessment. With the development of deep learning, the performance of informal residential area extraction has been improved. However, trying to accurately extract the boundaries between different buildings in the same complex is still a challenging task, and at the same time, segmentation model training takes a lot of time. To address the above problems, we propose a deep learning-based segmentation method for remote sensing images of informal residential area instances, aiming to further improve the accuracy of informal residential area segmentation. This paper has the following three main contributions. First, this paper designs and implements a remote sensing image informal residential area instance segmentation method called TransUNet-IRA, which is a model based on TransUNet, and reduces the training file weights by reducing the number of encoder layers and changing the format of the dataset, which increases the computing speed and improves the performance of the model. Second, we combine DiceCE Loss with IoU Loss to form DiceCE-IoU Loss to further improve the model’s performance in informal residential area segmentation. Finally, we collect a large amount of meter-level remote sensing image data from Guilin City, Guangxi Province, and Chenzhou City, Hunan Province, and construct a remote sensing image informal residential area instance segmentation dataset for these two regions based on these remote sensing images, which is annotated with three segmentations of the background, informal residential area, and single building in the formal residential area. After experimental validation, our method demonstrates performance advantages on the constructed dataset, not only outperforming classical models such as UNet, ResUNet, AttUNet, and SwinUNet but also improving on the original TransUNet. ©2024 IEEE.

关键词： image segmentation

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Paper Infrared and Visible image Adaptive Fusion Based on PCNN 2

Paper Infrared and Visible Image Adaptive Fusion Based on PC...

引用

2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Tang, Wei Jia, Fangxiu Nanjing University of Science and Technology Ministerial Key Laboratory of ZNDY Jiangsu Nanjing China

ISBN: (纸本)9798350331417

The multiscale transform decomposition fusion methods have the feedback of poor real-time and the detail informations of visible images cannot be fully preserved. In this paper, we popose an infrared and visual image fusion method based on pulse couple neural network (PCNN) and adaptive linking strength. Firstly, we present a simplified dual-channel PCNN based on infrared and visual image. Secondly, the linking strength is determined by fuzzy logic based on local average energy and detail informations, respectively. Experimental results show that compared with traditional multiscale transform decomposition fusion methods, the fused images can preserve more edge details, and have a better performance. Compared with objective data, Mutual Information(MI), Entroy(EN), (SF),(SD), Mean have increased by 10%, 4.5%, 14.6%, 30.7%, 27.3% respectively than Non-subsampled Contourlet Transform(NSCT). So our method have a better performance in terms of both subjective and objective evaluation. © 2023 IEEE.

关键词： fuzzy logic image fusion local energy pulse couple neural network

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A Fabric Defect Detection Algorithm Based on YOLOv8 2

A Fabric Defect Detection Algorithm Based on YOLOv8

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2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Zhang, Mingjun Yu, Wenjing Qiu, Hongjun Yin, Jin He, Jinyu Software Engineering Institute of Guangzhou Guangzhou China Guangzhou Civil Aviation College Guangzhou China

ISBN: (纸本)9798350331417

In response to the practical problems faced by manual defect detection in the textile industry, this paper studies an automatic fabric defect detection algorithm based on machine vision. Starting from the research background of deep learning based fabric defect detection algorithms, this article introduces the latest YOLOv8 algorithm model released by Ultratics. Based on the complex and diverse textures of textiles, a coordinate attention mechanism is added to the YOLOv8n model to enhance the network's ability to extract features. The experimental results show that the fabric defect detection algorithm designed in this article has high detection accuracy, mAP up to 79.20%, fast speed, and FPS up to 95.4, which can meet the requirements of online real-time detection with low computational resource requirements and can be deployed on edge intelligent computing devices. The algorithm designed in this article has good comprehensive performance and can promote the deployment of textile defect automatic detection technology in the industry. © 2023 IEEE.

关键词： Algorithm Detection Fabric defect machine vision YOLOv8

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