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检索条件"任意字段=Iranian Conference on Machine Vision and Image Processing"

共 11454 条记录，以下是81-90 订阅

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Research on machine vision Intelligent Detection System Based on Faster R-CNN 4

Research on Machine Vision Intelligent Detection System Base...

引用

4th IEEE International conference on Data Science and Computer Application, ICDSCA 2024

作者： Wang, Xueting Zeng, Fanshuo Song, Weizhao Zhang, Zhipeng He, Huitong Zhou, Zheyuan Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350368239

With the rapid development of artificial intelligence technology, visual inspection and image processing algorithms have been continuously improved in accuracy and efficiency, and intelligent inspection systems based on machine vision have been widely used in product quality control, defect detection and process monitoring. In this paper, a machine vision intelligent inspection system based on Faster Region-based Convolutional Network (Faster R-CNN) is proposed, which aims to improve the detection accuracy and efficiency. In this paper, the overall architecture of the system is introduced, including the image acquisition module, the data preprocessing module and the detection module based on Faster R-CNN. In the data preprocessing module, the Deep Convolution Generative Adversarial Networks (DCGAN) algorithm was proposed for image expansion generation, and high-resolution image samples that were basically consistent with the collected image datasets were obtained. Then, the Faster R-CNN model was optimized and adjusted for specific application scenarios, so that the average accuracy (mAP) of the model reached 78.23% and the FPS value reached 11.0. ©2024 IEEE.

关键词： Faster R-CNN image processing image recognition machine vision intelligent inspection system

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Building Detection Using Very High Resolution SAR images with Multi-Direction Based on Weighted-Morphological Indexes 12

Building Detection Using Very High Resolution SAR Images wit...

引用

12th iranian/2nd International conference on machine vision and image processing, MVIP 2022

作者： Amjadipour, Fateme Ghassemian, Hassan Imani, Maryam Tarbiat Modares University Image Processing and Information Analysis Lab Tehran Iran

ISBN: (纸本)9781665412162

Today, technological advancement in production of radar images can be seen with high spatial resolution and also the availability of these images' significant growth in interpretation and processing of high-resolution radar images. The building extraction from urban areas is one of the most challenging applications in VHR SAR image, which is used to estimate the population and urban development. Detection of individual buildings in the urban context is highly considered by researchers due to complexity of interpreting radar images in these fields. On the other hand, one of the main issues in the complexity of the scatters received from buildings is change in direction of the building relative to the horizon, which is correlated with the look angle. Other influential parameters are geometric distortions, which include layover and shadow effects. In some cases, the effect of shadow is an auxiliary parameter in detection of these targets that increases accuracy of the detection. In this paper, we intend to extract the building from high spatial resolution SAR images using fuzzy fusion of two morphological indicators, SI and DI, which represent the shadow and bright area, respectively. Due to the effect of SAR imaging geometry on ground targets, different sizes and directions of structural elements were applied to the image. The use of indicators weights with different sizes is proposed in this work. The Detection Ratio of experiment of TerraSAR-X image has a result of 95.3%. © 2022 IEEE.

关键词： Morphology

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Spatial Quality Assessment of Pansharpened images Based on Gray Level Co-Occurrence Matrix 12

Spatial Quality Assessment of Pansharpened Images Based on G...

引用

12th iranian/2nd International conference on machine vision and image processing, MVIP 2022

作者： Aghapour Maleki, Shiva Ghassemian, Hassan Tarbiat Modares University Image Processing and Information Analysis Laboratory Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9781665412162

Assessing the quality of pansharpened images is a critical issue in order to obtain a quantitative score to represent the quality and compare the performance of different fusion methods. Most of the introduced metrics for pansharpened image quality assessment, evaluate the spectral content of the image, while in different applications of remote sensing like detection and identification of image objects, spatial quality has an important role. In the current study, a new index for spatial quality assessment is introduced that extracts gray level co-occurrence matrix (GLCM) from distorted and reference images and compares the similarities of these features. The tempere image database 2013 (TID2013) that provides reference and different types of distorted images with subjective scores of each image is used as the desired database. To solve the high computational complexity of obtaining GLCM features, the fast GLCM method is employed. In this way, 16 different features are extracted. To select the features that have the most consistency with the human visual system (HVS), the forward floating search method is used as a feature selection method and five features are obtained as the final features to form the desired index. Experimental results show the efficiency of the proposed method in determining the spatial quality of fused images compared with that of the available quality assessment metrics. © 2022 IEEE.

关键词： Remote sensing

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Research on optical fiber distribution system based on machine vision 4

Research on optical fiber distribution system based on machi...

引用

4th International conference on image processing and Intelligent Control, IPIC 2024

作者： Yang, Yang Huang, Xiaoqi Chen, Jia Tian, Zhifeng Shenzhen Power Supply Bureau Co. LTD. Guangdong province Shenzhen518000 China

ISBN: (纸本)9781510682313

This paper mainly studies the optical fiber distribution system based on machine vision, and discusses its advantages and limitations in practical application. The development status and problems of optical fiber distribution system are introduced, and the application prospect of machine vision technology in this field is discussed. The design of optical fiber distribution system based on machine vision is introduced in detail, including system structure, hardware equipment selection, software system design and algorithm implementation. In the aspect of system performance test and evaluation, the test environment and data are introduced in detail, and the experimental results are analyzed and the system performance is evaluated. Through this research, it can provide reference and guidance for the practical application of optical fiber distribution system based on machine vision. © 2024 SPIE.

关键词： Optical fibers

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Innovating Tourism: Personalized Recommendations through AI 2

Innovating Tourism: Personalized Recommendations through AI

引用

2nd Asia conference on Computer vision, image processing and Pattern Recognition (CVIPPR)

作者： Palahan, Sirinda Arunthari, Santipat Univ Thai Chamber Commerce Sch Sci & Technol Bangkok Thailand

ISBN: (纸本)9798400716607

This paper delves into the development and deployment of an AI-powered chatbot aimed at enhancing tourism experiences. By integrating advanced natural language processing and machine learning techniques, the chatbot offers personalized travel recommendations and information, serving as a versatile tool for tourists. Through experimental evaluation involving intent classification and user satisfaction, the chatbot demonstrates significant accuracy in understanding user queries and a positive impact on user satisfaction. Intent classification using DistilBERT achieved a high F1-score, indicating superior understanding capabilities. User feedback highlighted satisfaction and likelihood of reuse, pointing to areas for further refinement in response relevance.

关键词： Chatbot Tourism Natural Language processing Intent Classification

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On the impact of learning-based image compression on computer vision tasks 47

On the impact of learning-based image compression on compute...

引用

conference on Applications of Digital image processing XLVII

作者： Akamatsu, Shunsuke Testolina, Michela Upenik, Evgeniy Ebrahimi, Touradj Waseda Univ Adv Multimedia Syst Lab Shillman Hall 4013-14-9 OkuboShinjuku Ku Tokyo 1690072 Japan Ecole Polytech Fed Lausanne EPFL Multimedia Signal Proc Grp MMSPG CH-1015 Lausanne Switzerland

ISBN: (纸本)9781510679344;9781510679351

The image compression field is witnessing a shift in paradigm thanks to the rise of neural network-based models. In this context, the JPEG committee is in the process of standardizing the first learning-based image compression standard, known as JPEG AI. While most of the research to date has focused on image coding for humans, JPEG AI plans to address both human and machine vision by presenting several non-normative decoders addressing multiple image processing and computer vision tasks in addition to standard reconstruction. While the impact of conventional image compression on computer vision tasks has already been addressed, no study has been conducted to assess the impact of learning-based image compression on such tasks. In this paper, the impact of learning-based image compression, including JPEG AI, on computer vision tasks is reviewed and discussed, mainly focusing on the image classification task along with object detection and segmentation. This study reviews the impact of image compression with JPEG AI on various computer vision models. It shows the superiority of JPEG AI over other conventional and learning-based compression models.

关键词： JPEG AI learning-based image compression computer vision image classification object detection

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Application of image classification in forest fire detection using UAV 3

Application of image classification in forest fire detection...

引用

3rd International conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC)

作者： Geng, Bowen Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9798350352634;9798350352627

This paper focuses on the application of image classification in forest fire detection using unmanned aerial vehicles (UAVs), discussing the development history of UAV image classification and the significance of machine vision in fire monitoring. Initially, the dataset used for fire detection and the data processing and enhancement techniques are introduced. Subsequently, the construction and architecture of the image classification model are detailed. The core of this study is to enhance the accuracy of model image recognition in complex forest environments by replacing optimizers, modifying the model architecture, and adding modules. Various models and optimizers are compared and analyzed, and the operations and significance of enhancement methods and attention mechanisms are explored. The aim is to improve training effectiveness through these strategies, thereby effectively supporting UAVs in forest fire detection.

关键词： UAV Forest fire detection image classification machine vision Model optimization

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Robot Automatic Wire Welding Based on machine vision 5

Robot Automatic Wire Welding Based on Machine Vision

引用

5th International conference on Computer Engineering and Application (ICCEA)

作者： Liu, Xinsheng Li, Lidan Xing, Hongmei Wang, Shaohua Zhang, Chuan Diao, Xiyao Zhuo, Zhang China North Vehicle Res Inst Beijing Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Currently, the welding process between electrical connectors and multi-core wires mainly relies on manual operation. This traditional method not only consumes a lot of time and manpower, but also long-term operation may cause certain physical burden and health hazards to the operator. Therefore, researching and implementing automated welding between electrical connectors and multi-core wires has become an urgent problem to be solved. On the basis of summarizing the current research status at home and abroad, the software and hardware parts of the system were designed to meet the requirements of identifying and positioning welding circular electrical connectors. By introducing image processing and machine vision technology, adopting a dual machine collaboration approach and based on machine vision methods, automatic wire welding of electrical connectors has been achieved, improving welding efficiency and reducing the labor intensity of operators. In addition, it is also conducive to promoting the development of industrial automation.

关键词： Industrial robot Automatic welding Visual image Aviation plug Wires

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Multi-Scale Hybrid Attention Integrated with vision Transformers for Enhanced image Segmentation 2

Multi-Scale Hybrid Attention Integrated with Vision Transfor...

引用

2nd International conference on Algorithm, image processing and machine vision, AIPMV 2024

作者： Gong, Zhaoxuan Chanmean, Meas Gu, Wenwen School of Computer Engineering Shenyang Aerospace University Liaoning Shenyang China School of Computer Science Shenyang Aerospace University Liaoning Shenyang China

ISBN: (纸本)9798350390254

In this paper, we present an advanced approach for image segmentation that enhances vision Transformers (ViTs) by integrating multi-scale hybrid attention mechanisms. While ViTs use self-attention to capture global dependencies in images, they often miss local details crucial for segmentation tasks. Our method addresses this by incorporating convolutional layers with different kernel sizes to extract local features at multiple scales. These features are then merged with the global self-attention outputs, creating a hybrid attention map that captures both fine details and overall structures. Testing on standard benchmarks shows our approach significantly improves segmentation accuracy and robustness compared to traditional ViTs, demonstrating its potential for handling complex visual scenes. © 2024 IEEE.

关键词： Convolutional neural networks

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Towards Fine-grained image Classification with Generative Adversarial Networks and Facial Landmark Detection 12

Towards Fine-grained Image Classification with Generative Ad...

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12th iranian/2nd International conference on machine vision and image processing, MVIP 2022

作者： Darvish, Mahdi Pouramini, Mahsa Bahador, Hamid University of Mohaghegh Ardabili Department of Electrical and Computer Science Ardabil Iran

ISBN: (纸本)9781665412162

Fine-grained classification remains a challenging task because distinguishing categories needs learning complex and local differences. Diversity in the pose, scale, and position of objects in an image makes the problem even more difficult. Although the recent vision Transformer models achieve high performance, they need an extensive volume of input data. To encounter this problem, we made the best use of GAN-based data augmentation to generate extra dataset instances. Oxford-IIIT Pets was our dataset of choice for this experiment. It consists of 37 breeds of cats and dogs with variations in scale, poses, and lighting, which intensifies the difficulty of the classification task. Furthermore, we enhanced the performance of the recent Generative Adversarial Network (GAN), StyleGAN2-ADA model to generate more realistic images while preventing overfitting to the training set. We did this by training a customized version of MobileNetV2 to predict animal facial landmarks;then, we cropped images accordingly. Lastly, we combined the synthetic images with the original dataset and compared our proposed method with standard GANs augmentation and no augmentation with different subsets of training data. We validated our work by evaluating the accuracy of fine-grained image classification on the recent vision Transformer (ViT) Model. Code is available at: https://***/mahdi-darvish/GAN-augmented-pet-classifler. © 2022 IEEE.

关键词： Generative adversarial networks

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