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

共 1688 条记录，以下是1-10 订阅

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

3rd International Conference on Image Processing and Vision ...

引用

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

ISBN: (纸本)9789897586422

The proceedings contain 23 papers. The special focus in this conference is on image processing and vision engineering. The topics include: Multi-Scale Surface Normal Estimation from Depth Maps;intrinsic image Decomposition: Challenges and New Perspectives;vegetation Coverage and Urban Amenity Mapping Using Computer vision and Machine Learning;a Deep Learning Approach for Estimating the Rind Thickness of Trentingrana Cheese from images;application of Particle Detection Methods to Solve Particle Overlapping Problems;an Anisotropic and Asymmetric Causal Filtering Based Corner Detection Method;layer-wise External Attention for Efficient Deep Anomaly Detection;emotion Based Music Visualization with Fractal Arts;handling Data Heterogeneity in Federated Learning with Global Data Distribution;climbing with Virtual Mentor by Means of Video-Based Motion Analysis;normalised Color Distances;fuzzy Inference System in a Local Eigenvector Based Color image Smoothing Framework;3D Reference-Based Skeletal Movement Evaluation;FUB-Clustering: Fully Unsupervised Batch Clustering;from Depth Sensing to Deep Depth Estimation for 3D Reconstruction: Open Challenges;deep Learning and Medical image Analysis: Epistemology and Ethical Issues;an Integrated Mobile vision System for Enhancing the Interaction of Blind and Low vision Users with Their Surroundings;automatic Defect Detection in Sewer Network Using Deep Learning Based Object Detector;facial Expression Recognition with Quarantine Face Masks Using a Synthetic Dataset Generator;A Global Multi-Temporal Dataset with STGAN Baseline for Cloud and Cloud Shadow Removal.

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Weighted Features Based Classification of Polarimetric SAR images 13

Weighted Features Based Classification of Polarimetric SAR I...

引用

13th Iranian/3rd international Machine vision and image processing conference (MVIP)

作者： Saneipour, Fatemeh Imani, Maryam Ghassemian, Hassan Tarbiat Modares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Today, classification of polarimetric images is an important topic where various statistical pattern recognition methods have been used to achieve the high accurate classification maps. In this work, weighting the polarimetric features according to their statistical behavior (the mean vector and variance values as the first and second statistics) is suggested to improve the PolSAR image classification. A weighted feature matrix is composed and applied to the popular classifiers such as maximum likelihood, K-nearest neighbor and support vector machine. The weighted feature matrix can be also implemented on other arbitrary classifiers to improve their discrimination ability. The experiments on the L-band AIRSAR dataset show appropriate classification results.

关键词： polarimetric synthetic aperture radar (PolSAR) weighted features classification

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Residual Convolutional Neural Network With Autoencoder Based Attention For PolSAR image Classification 13

Residual Convolutional Neural Network With Autoencoder Based...

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13th Iranian/3rd international Machine vision and image processing conference (MVIP)

作者： Imani, Maryam Tarbiat Modares Univ Fac Elect & Comp Engn Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

The polarimetric synthetic aperture radar (PolSAR) images contain fine characteristics and abstract spatial features, which attention to them can improve the classification accuracy. In this work, the residual convolutional neural network with autoencoder based attention (RCNN-AA) is proposed for PolSAR image classification. The scaled difference of the convolutional autoencoder with the original input patch is used as the weight, which contains information about the fine spatial features. Multiplication of this normalized difference in the input patch provides the attention feature maps that can be concatenated with the original input and used as input of the RCNN. An ablation study is done, and also, the proposed RCNN-AA model is compared to some deep learning based models. The results show preference of the RCNN-AA with respect to the competitors.

关键词： autoencoder convolutional neutral network (CNN) residual learning PolSAR classification

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Spatial-Spectral Neural Network for High Resolution Multispectral image Classification 13

Spatial-Spectral Neural Network for High Resolution Multispe...

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13th Iranian/3rd international Machine vision and image processing conference (MVIP)

作者： Tanha, Roozbeh Ghassemian, Hassan Tarbiat Modares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Classification of multispectral images in remote-sensing area having the capability to analyze and categorize diversified land cover. In this issue, extracting suitable spatial, spectral and even temporal features is one of the main challenges. Also, the existence of sufficient data required for the classification training process is another challenge, because in many cases it may not be available and we may not even have a reliable classification map. The use of neural networks for simultaneous feature extraction and classification is very popular and significant progress has been made in this field, but these networks usually have a high computational cost and require significant training data in the training process. In this work we propose a neural network for multispectral image classification purpose which requires few training samples and less calculation without using filterbanks for spatial feature extraction and it can improve classification accuracy by fusion of spatial and spectral features. The simulations indicate that the proposed method shows an acceptable performance.

关键词： Remote sensing multispectral image classification feature extraction neural network

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Application of image classification in forest fire detection using UAV 3

Application of image classification in forest fire detection...

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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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image denoising based on residual network 3

Image denoising based on residual network

引用

3rd international conference on Electronic Information engineering, Big Data, and Computer Technology, EIBDCT 2024

作者： Ma, Lihe Wang, Yan Sha, Ning Ma, Lin School of Information Engineering Harbin University Harbin China Heilongjiang Government Affairs Big Data Center3 Harbin China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin China

ISBN: (纸本)9781510680449

image denoising has always been a hot research topic in the field of computer vision, playing an important role in improving image quality and accuracy. In this paper, a method for image denoising based on residual network is proposed, which uses deep learning technology to improve the effect of image processing. This method can automatically learn useful features in the image and effectively remove noise in the image, achieving automatic removal of image noise. Finally, experiments were conducted on standard image datasets for evaluation. The experimental results show that the image denoising method based on residual network has better performance compared to traditional methods. It can effectively remove various types of noise, improve image quality and clarity, and has broad application prospects in image processing. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image denoising

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Novel Civil engineering Construction Site Safety Monitoring and Warning System based on Machine vision 3

Novel Civil Engineering Construction Site Safety Monitoring ...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Zhang, Qiankun Cheng, Qibin Zhejiang Construction Investment Innovation Technology Co. Ltd. Zhejiang Hangzhou310000 China Zhejiang Construction Engineering Group Co. Ltd. Zhejiang Hangzhou310013 China

ISBN: (纸本)9798331509675

Machine vision is widely used in the engineering field, especially in the intelligent monitoring, safety warning and information recognition scenarios. In this study, the novel civil engineering construction site safety monitoring and warning system based on machine vision is proposed. As a pioneering research, this system realizes all-weather real-time security monitoring by combining the daytime and nighttime image processing technology. In daytime image processing scenarios, this study proposes a new image de-hazing algorithm. This algorithm models the imaging process by improving the atmospheric scattering model. At the same time, the guided filtering algorithm is used for optimization, which significantly improves the image clarity in low-visibility environments. In the night image processing scenario, this study uses infrared imaging technology and filtering algorithms for image enhancement. This operation can effectively solve the challenges of image noise and detail loss in low-light environments. As the core of the model, this study proposes a safety warning framework based on image anomaly detection to meet the special needs of construction sites. This innovative architecture includes human posture rotation detection and image color deviation anomaly recognition. Through such operations, automatic recognition and warning of dangerous behaviors are achieved. Experimental results prove the effectiveness of the recognition algorithm. © 2025 IEEE.

关键词： Machine vision

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Research on Target Detection and Depth Estimation in Binocular images Based on YOLOv8 3

Research on Target Detection and Depth Estimation in Binocul...

引用

3rd international Signal processing, Communications and engineering Management conference, ISPCEM 2023

作者： Liu, Xuanyu Mao, Peiyu School of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350307023

As artificial intelligence technology enters a new era, target detection in binocular images and depth estimation have entered a high-quality development stage. We are committed to developing deep learning with YOLOv8 and proposing the issue of algorithms that meet the needs of image recognition and depth perception for intelligent vision. Based on the dynamic development of YOLOv8, the theoretical analysis framework of target detection in binocular images and depth estimation system are built according to the binocular image processing protocol specification, which can explain the mechanism for extracting and matching image features as well as estimating and optimizing the depth cycle mechanism generated by the binocular image acquisition and pre-processing model and image recognition module. In addition, from the perspective of performance changes and practical interpretation of target detection in binocular images and depth estimation, we will continue to explore the possibility of moving towards high-quality development of target detection in binocular images and depth estimation. Target detection in binocular images and depth estimation provide standard image recognition and depth perception for intelligent vision and continuously improve the quality of target detection in binocular images and depth estimation to improve users' satisfaction. Therefore, we take measures, such as enhancing the control of the binocular image recognition model based on the YOLOv8 quality inner loop, building interaction and feedback mechanism between the image acquisition module and pre-processing module, and binocular image recognition module, setting up an evaluation system for the target detection in binocular images and depth estimation. This study achieves high-quality development of target detection in binocular images and depth estimation, which is conducive to the progress of intelligent vision. © 2023 IEEE.

关键词： image recognition

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vision Transformer-Based image Inpainting Method 3

Vision Transformer-Based Image Inpainting Method

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3rd IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Lin, Rui Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing China

ISBN: (纸本)9798350398410

The paper built a vision transformer-based three-stage image inpainting model, which can effectively process multiple public data sets and outperforms traditional reference model. By using the vision transformer-based multitask learning strategy, we developed a new end-to-end image inpainting model to improve the performance of the three-stage model. It has s superior performance when processing three different data sets. After introducing a specific deep learning framework, we can effectively solve image inpainting tasks, greatly reduce Sub-I burden, and provide more potential for the application of deep learning in image inpainting field. © 2023 IEEE.

关键词： image enhancement

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Detection of Appearance Defects in Cigarettes Based on AI Artificial Intelligence 24

Detection of Appearance Defects in Cigarettes Based on AI Ar...

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3rd international conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE)

作者： Tian, Qiusheng Liu, Qiang Wang, Li Zhang, Kunfeng Henan Zhongyan Ind Ltd Liabil Co Zhumadian Cigarette Factory Zhumadian 463000 Henan Peoples R China

ISBN: (纸本)9798400718212

The detection of appearance defects in cigarettes is a key quality control link in tobacco production. Traditional detection methods rely on artificial vision, which has problems such as strong subjectivity, low efficiency, and susceptibility to errors. This paper proposes a cigarette appearance defect detection method based on Artificial Intelligence (AI), aiming to improve detection efficiency and accuracy. This method utilizes image processing and analysis methods in AI technology, combined with convolutional neural network algorithms, to achieve automated detection and classification of cigarette appearance defects. The experimental results indicate that this method has higher accuracy and detection efficiency.

关键词： cigarette appearance defect detection artificial intelligence deep learning image processing classification

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