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Semi-supervised polarimetric SAR images classification based on FixMatch 12

Semi-supervised polarimetric SAR images classification based...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

作者： Lv, Qiwen Wang, Lei Hong, Hanyu Zhao, Shuwei Peng, Lingmu Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology 206 Guanggu 1st Road East Lake New Technology Development ZoneHubei Province Wuhan City430000 China

ISBN: (数字)9781510674929

ISBN: (纸本)9781510674912

Polarimetric synthetic aperture radar (PolSAR) images are widely used in the fields of disaster detection and military reconnaissance. PolSAR image classification is one of the most important applications in PolSAR image interpretation. Existing image classification methods based on deep learning usually rely on a large number of labeled training data. Labeling large-scale data sets is expensive and time-consuming Therefore, semi-supervised learning is an important research direction in the field of image classification. Fixmath has achieved excellent semi-supervised classification performance for nature image, so it is introduced into the field of PolSAR image interpretation, and to improve the performance of semi-supervised PolSAR image classification. Fixmatch has the following advantages: (1) It extends the training data, using generated, labeled and unlabeled data to train the network. (2) By utilizing the information of unlabeled data, FixMatch can improve the generalization ability of the classification model, thus allowing the model to better predict on unseen data. Because of these two advantages, FixMatch has shown excellent results in semi-supervised PolSAR image classification. Experiments on two real PolSAR datasets show that the overall accuracy reaches 89.9 % when the number of labeled samples is set to 110, and reaches 98.6 % when the number of labeled samples is set to 1000. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image classification

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Photovoltaic panel string detection based on prior knowledge and feature learning 12

Photovoltaic panel string detection based on prior knowledge...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

作者： Li, Bolin Cheng, Bolin Ye, Liang Hubei Key Laboratory of Optical Information and Pattern Recognition China Hubei Engineering Research Center of Video Image and HD Projection China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

image-based photovoltaic panel inspection has become one of the important tasks of photovoltaic power generation. Due to the repeated patterns of photovoltaic string numbers and different specifications, it is easy to cause false detection by traditional template matching or model training methods. In order to solve this problem, this paper proposes a photovoltaic panel string detection method based on prior knowledge and feature learning. First, the infrared inspection image is segmented for the first time by using the difference in radiation characteristics between the photovoltaic string and the environmental background. Subsequently, according to the local features of the border around the PV string template image, feature matching is performed in the segmented foreground area, and then determine the exact position of the string through the closure and integrity of the matching area. Experiments show that this method has high detection accuracy and has good adaptability to complex and diverse string specifications. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Inspection

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An anti-occlusion tracking algorithm based on mask strategy and adaptive response fusion 12

An anti-occlusion tracking algorithm based on mask strategy ...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

作者： Duan, Zhongyu Huang, Shixiang Yue, Zhongmu Tian, Tian Tian, Jinwen National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

In long-Term target tracking, target occlusion is an important factor that affects the performance of the tracker, and it is also an inevitable problem in long-Term target tracking. Therefore, it is necessary to adopt corresponding strategies for target occlusion to improve the robustness of the tracker in corresponding scenarios. In response to the problem of target loss in complex background scenarios such as target occlusion in current filtering based tracking algorithms, this paper studies the use of masking strategies and adaptive response fusion methods to improve the accuracy and robustness of tracking *** experimental results demonstrate the effectiveness of various strategies in resisting occlusion in tracking algorithms. The experimental results of the overall algorithm indicate that the algorithm can effectively solve occlusion problems while balancing accuracy, speed, and robustness. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Target tracking

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Overview of remote sensing image fusion based on deep learning 12

Overview of remote sensing image fusion based on deep learni...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

作者： Wang, Qian Southwest Forestry University 300 Bailong Temple Kunming650024 China

ISBN: (数字)9781510674929

ISBN: (纸本)9781510674912

With the widespread application and rapid development of remote sensing technology, the quality requirements for remote sensing images are gradually improving. Currently, relying solely on one sensor is difficult to ensure high signal-to- noise ratio while capturing images with high spatial and spectral resolution. Remote sensing image fusion technology obtains high-quality images by combining the spatial information and spectral (or spectral) information of different sensors. Meanwhile, in recent years, deep learning theory has developed rapidly and is widely applied in image processing such as remote sensing image fusion. Therefore, in order to gain a more systematic understanding of the current status of remote sensing image fusion based on deep learning and promote the development of remote sensing image fusion, this article first introduces commonly used remote sensing satellite images and traditional image fusion algorithms;Then, the remote sensing image fusion algorithm based on deep learning is emphasized, and the advantages and disadvantages of deep learning fusion methods (PanNet, LPPN, WSDFNet) are compared and analyzed;Finally, the future prospects of remote sensing image fusion methods based on deep learning are presented. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image fusion

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Dense Center Prediction for Centerline Extraction Based on BP Network 12

Dense Center Prediction for Centerline Extraction Based on B...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

作者： Gui, Chuanqi Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

In the line structured light three-dimensional measurement system, high-precision laser stripe centerline extraction is the key to improving measurement accuracy. The laser line extraction technology based on neural network can automatically detect and extract laser lines with high accuracy and robustness. In this paper, a BP neural network containing one hidden layers is constructed, and the centerline obtained through feature extraction of the network can effectively overcome the influence of noise interference. Compared with the gray-gravity method and the Steger algorithm, the method in this paper has the advantage of high accuracy and can meet the requirements for extracting the center of complex light strips. © 2024 SPIE.

关键词： Extraction

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An improved image segmentation algorithm based on MRF and Sobel operator 12

An improved image segmentation algorithm based on MRF and So...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

作者： Gao, Yao Tian, Keyun Zhu, Cheng Hubei Key Laboratory of Optical Information and Pattern Recognition China Hubei Engineering Research Center of Video Image and HD Projection China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

In response to the issues of over-segmentation, excessive noise, and suboptimal segmentation results commonly encountered in existing image segmentation algorithms based on Markov Random Fields (MRF), this paper proposes an enhanced image segmentation algorithm that integrates MRF with the Sobel operator. The algorithm begins by performing an initial segmentation of the image using a Markov Random Field (MRF)-based method. Subsequently, an enhanced Sobel operator is employed to eliminate noise points and extract fine edge details from the image. Finally, the segmentation result is refined through pixel-wise operations with the edge detection result, resulting in the ultimate segmentation output. The evaluation of segmentation performance is conducted using the Dice coefficient and Mean Hausdorff Distance as assessment metrics. Through experimental analysis, the method in this paper can improve the segmentation effect of the traditional MRF segmentation algorithm, and has better performance and higher adaptivity. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image segmentation

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Fully electrically controlled light-field camera via electrowetting liquid lens and liquid-crystal microlens array 12

Fully electrically controlled light-field camera via electro...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

作者： Hu, Jiaqi Zhang, Chunyu Zhang, Xinyu National Key Laboratory of Science & Technology on Multispectral Information Processing Huazhong University of Science & Technology Wuhan430074 China School of Artificial Intelligence & Automation Huazhong University of Science & Technology Wuhan430074 China

ISBN: (数字)9781510674929

ISBN: (纸本)9781510674912

Light-field imaging is an effective and straightforward way to image with three-dimensional information. In this paper, a new type of the fully electrically controlled light-field camera based on electrowetting liquid lens and liquid crystal microlens array (EW-LCMLA) is proposed. In the proposed camera, a liquid-crystal microlens array (LCMLA) is placed on the CMOS sensor to get the raw light-field images. Unlike conventional light-field camera using LCMLA, the proposed camera can rapidly adjust the compound-eye degree of light-field image by inserting a liquid lens between main lens and LCMLA, and by switching the external voltage on the liquid lens, the camera can work on multiple imaging modes, such as light-field imaging, conventional two-dimensional imaging and focal-stack imaging. For further improve the. In this study, an algorithm based on focal stack theory is also proposed to extract the depth information from the raw light-field image and merge the image focus on different distances to get all-in-focus image. We test the different imaging mode of the camera in indoor environments, and the experiments demonstrate that the proposed camera can capture high quality image on different mode, and shows excellent focal length adjustment ability without complex mechanical structure. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Microlenses

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MIPPR 2023: multispectral image Acquisition, processing, and Analysis

MIPPR 2023: Multispectral Image Acquisition, Processing, and...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

ISBN: (纸本)9781510674912

The proceedings contain 16 papers. The topics discussed include: fully electrically controlled light-field camera via electrowetting liquid lens and liquid-crystal microlens array;study on transmission and nanofocusing characteristics of surface array micronano metasurface;tuning of near-field optical properties based on magneto-tip array super-surfaces;ice area and 3D ice shape measurement method based on polarized light imaging;study on the polarization response of aluminum gratings with graphene;toroidal composite liquid crystal microlens array co-driven by four independent signal voltages;semi-supervised polarimetric SAR images classification based on FixMatch;and overview of remote sensing image fusion based on deep learning.

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Residual multi-scale pixel attention fusion network for image deblurring 12

Residual multi-scale pixel attention fusion network for imag...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

作者： Li, Zezheng Xiang, Guoliang Shu, Ting Yang, Mingjuan Hua, Xia Hubei Key Laboratory of Optical Information and Pattern Recognition School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China Wuhan Raycus Fiber Laser Technologies Co. Ltd Wuhan430075 China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

The applications of multi-scale fusion strategy to feature are very effective and common in image restoration. However, there is a lack of research on the application of the multi-scale fusion strategy to attention mechanism, although attention mechanism has also been verified to be effective in image restoration. To address this problem, we propose a residual multi-scale pixel attention fusion block (RMPAFB) to refine the input feature, which successfully combine the multi-scale fusion strategy with pixel attention. RMPAFB can capture feature correspondences from multi-scale pixel attention map, which can be more effective for feature refinement than single-scale pixel attention map. Based on RMPAFB, we build an efficient and effective network called RMPAFNet for image deblurring. Substantial experiments on several benchmark datasets have showed that multi-scale pixel attention performs better than single-scale pixel attention and our proposed RMPAFNet achieves state-of-The-Art performance while requiring fewer overheads than recent competing deblurring models. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Pixels

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MIPPR 2023: pattern recognition and Computer Vision

MIPPR 2023: Pattern Recognition and Computer Vision

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

ISBN: (纸本)9781510674950

The proceedings contain 25 papers. The topics discussed include: accurate extraction of line-structured optical stripe centerlines under low exposure;research on grid inspection technology based on general knowledge enhanced multi-modal large language models;a comparison between deep-learning models for scene recognition;binary network design for dedicated hardware circuits;evaluation of training datasets for typical target detection networks;optimal exposure selection of high dynamic range-based reflection compensation for printed circuit board reconstruction using structured light 3D measurement system;fall detection model based on Alphapose combined with LSTM and Lightgbm;and an image enhancement algorithm based on multi-scale Retinex theory to improve the images quality of sensors.

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