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检索条件"任意字段=Conference on Optical and Digital Image Processing"

共 5529 条记录，以下是131-140 订阅

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Research and development of image homology identification technology based on fine registration

Research and development of image homology identification te...

引用

2024 International conference on image processing and Artificial Intelligence, ICIPAl 2024

作者： Bai, Zhiqing Esquivel, James A. Graduate School Angeles University Foundation Angeles City2009 Philippines School of Electronic Engineering School of Intelligent Manufacturing Anhui Xinhua University Hefei City230000 China

ISBN: (纸本)9781510681514

The function of recording and reproducing facts according to statistics is widely used. However, the appearance of image editing software makes it easy to tamper with image information, which reduces its credibility and authenticity. Therefore, judging and restoring the originality, authenticity, and integrity of digital images has become an urgent problem to be solved. To address these issues, we propose a corresponding image recognition technology based on fine registration. We measure the system of image matching and automatic correction for users in different shooting angles, image sizes, image acquisition devices, image rotation differences, and different lighting conditions. This technology aims to eliminate noise pollution in the process of image transmission and establish testing standards for images. Through the deep learning process, we have achieved a high-precision image homology recognition method that possesses the characteristics of good robustness, fast response speed, and strong practicability. © 2024 SPIE.

关键词： Noise pollution

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Research on spatial technology of distribution network digital twin model based on multimodule data fusion 2

Research on spatial technology of distribution network digit...

引用

2nd International conference on Physics, Photonics, and optical Engineering, ICPPOE 2023

作者： Li, Fan He, Zhimin Tian, Yingjie Song, Jie Su, Yun State Grid Shanghai Municipal Electric Power Company Shanghai200000 China State Grid Smart Grid Research Institute Co. LTD Jiangsu Nanjing210003 China

ISBN: (纸本)9781510674684

In this paper, the generation technology of distribution network digital twin scene based on multi-module data fusion is studied. Firstly, multi-module data fusion is carried out in complex dynamic operating environment of distribution. Aiming at the deficiency of constructing digital twins from a single model source, various digital model resources in distribution link are collected and integrated to support the construction of hierarchical digital twin model of distribution network. The second step is to carry out the multi-source comprehensive reconstruction and entity hierarchical processing of the distribution network, establish the multi-source digital model of the universal distribution network equipment, and realize the global and local multi-level and multi-scale model display of the distribution network environment on the basis of comprehensive reconstruction;The third step is to configure the digital twin spatial mapping based on the distribution network's panoramic data fusion to realize the panoramic mapping between the real image, spatial coordinates and equipment monitoring data and the digital twin model, as well as multi-layer semantic information embedding such as equipment, region and job, forming the corresponding characteristics of multi-block, multi-level and multi-type digital twins. Finally, the distribution network panoramic digital twin model is dynamically updated to realize the projection of various real-time perception data to the digital twin, and the digital twin is updated synchronously and grown dynamically. © COPYRIGHT 2024 SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Semantics

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Polarization Multi-image Synthesis with Birefringent Metasurfaces 15

Polarization Multi-Image Synthesis with Birefringent Metasur...

引用

15th IEEE International conference on Computational Photography (ICCP)

作者： Hazineh, Dean Lim, Soon Wei Daniel Guo, Qi Capasso, Federico Zickler, Todd Harvard Univ Dept Appl Phys Boston MA USA Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN USA Harvard Univ Dept Elect & Comp Engn Boston MA USA

ISBN: (纸本)9798350316766

optical metasurfaces composed of precisely engineered nanostructures have gained significant attention for their ability to manipulate light and implement distinct functionalities based on the properties of the incident field. Computational imaging systems have started harnessing this capability to produce sets of coded measurements that benefit certain tasks when paired with digital post-processing. Inspired by these works, we introduce a new system that uses a birefringent metasurface with a polarizer-mosaicked photosensor to capture four optically-coded measurements in a single exposure. We apply this system to the task of incoherent opto-electronic filtering, where digital spatial-filtering operations are replaced by simpler, per-pixel sums across the four polarization channels, independent of the spatial filter size. In contrast to previous work on incoherent opto-electronic filtering that can realize only one spatial filter, our approach can realize a continuous family of filters from a single capture, with filters being selected from the family by adjusting the post-capture digital summation weights. To find a metasurface that can realize a set of user-specified spatial filters, we introduce a form of gradient descent with a novel regularizer that encourages light efficiency and a high signal-to-noise ratio. We demonstrate several examples in simulation and with fabricated prototypes, including some with spatial filters that have prescribed variations with respect to depth and wavelength.

关键词： Metasurface image processing Polarization-Encoded Point-Spread Functions optical Filtering

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Secure multiple image steganography based on invertible neural network and neural style transfer 16

Secure multiple image steganography based on invertible neur...

引用

16th International conference on digital image processing, ICDIP 2024

作者： Feng, Weibin Liu, Yijun Ye, Wujian Tan, Dongjun School of information engineering Guangdong University of Technology Guangdong Guangzhou510006 China

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

Multiple image steganography involves hiding multiple secret images in a single cover image and eventually recovering all secret images from the stego image. Although the current image steganography has been greatly improved in terms of steganography capacity and invisibility, its security, as well as its steganography capacity and invisibility in style transfer scenarios are facing serious challenges. To solve these problems, an improved high-security multiple image steganography framework SMIS is proposed by combining invertible neural network and style transfer network. First, the introduction of a pre-trained style transfer network can disrupt the hidden features of the secret image embedded in the stego image, thus significantly reducing the detection accuracy of steganography. Secondly, to enable the dense invertible neural network (DINN) to extract secret images effectively, on the one hand, a feature reconstruction module (FRM) is designed based on the UNet structure to reconstruct the semantic features of the secret information, and on the other hand, a semantic reconstruction loss is designed to constrain the overall training of the steganography model. In addition, to solve the problem of limited global receptive field and excessive feature redundancy of UNet, the explicit visual center (EVC) and a self-calibration Neck (ScNeck) are introduced. The experimental results show that the proposed SMIS can efficiently hide the multiple secret images greatly improve the invisibility of steganography, and extract them from stylized stego image. At the same time, the security of the proposed method is higher than that of the existing mainstream methods. © 2024 SPIE.

关键词： Steganography

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Methods for Improving the Efficiency of Object Detection in Sonar images

Methods for Improving the Efficiency of Object Detection in ...

引用

2024 International Russian Automation conference, RusAutoCon 2024

作者： Saenko, Aleksandr Mironov, Andrey Fomina, Ekaterina Higher School of Cybernetics and Digital Technologies Pacific National University Khabarovsk Russia

The presented work addresses the issues of improving efficiency in solving the problem of detecting and identifying objects using neural network methods on sonar images. It is shown that it is possible to apply methods developed for optical images - both classical and modern neural network ones - when implementing the process of secondary data processing for a sonar image. The methods of manual and automatic object detection in a sonar image are considered. The disadvantages of these methods are indicated. The issues related to the use of High-resolution neural networks for test and training sets of convolutional neural networks used for object detection and identification are discussed in detail. As part of a numerical experiment on real data, it is shown that the use of High-resolution neural networks as part of the detector allowed increasing the object detection efficiency by 10 percent and completeness by 10 percent. The most effective is the object detector on the sonar image, for which the High resolution BSRGAN method is used at the training stage of the Yolo3 neural network. © 2024 IEEE.

关键词： Sonar

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An image encryption algorithm based on non-degenerate chaos and dynamic Bi-directional permutation 16

An image encryption algorithm based on non-degenerate chaos ...

引用

16th International conference on Signal processing Systems, ICSPS 2024

作者： Liao, Yunlong Xu, Qingyang Zhang, Qiyuan Xing, Zheng Yuan, Xiaochen Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9781510689251

In the wake of the accelerated advancement of Internet of Things(IoT) technology, a significant volume of multimedia information is emerging as the dominant component of IoT applications. However, this information also led to big security risks. Therefore, a secure and reliable image encryption scheme based on non-degenerate hyper chaos and dynamic bidirectional permutation is proposed for the characteristic of digital images which the primary information carrier in the IoT communication environment. The scheme uses non-degenerate discrete chaos to generate pseudo-random sequences and applies them to the encryption process involving image confusion, dynamic permutation, and dynamic diffusion. Through theoretical analysis and experimental verification, the algorithm exhibits high sensitivity to both the plaintext images and the ciphertext images. Concurrently, it can effectively resist common noise attacks and cryptographic attacks. In conclusion, the proposed scheme offers superior encryption capabilities and stands as a secure solution, compatible with a diverse range of IoT environments. © 2025 SPIE.

关键词： Chaos theory

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Track-before-detect algorithm based optimized interactive multiple model particle filtering for tracking maneuvering weak target

Track-before-detect algorithm based optimized interactive mu...

引用

2024 International conference on image processing and Artificial Intelligence, ICIPAl 2024

作者： Wu, Xiaojia Yang, Jinlong School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing100039 China

ISBN: (纸本)9781510681514

This paper proposes an optimized algorithm for tracking maneuvering weak radar targets in complex sea environments. In the proposal, we first preprocesses radar echo images using the fractional Fourier transform (FrFT) to reduce clutter interference. Then, Feedback factor and residual resampling are introduced to optimize the interactive multiple model particle filtering (IMMPF) algorithm to achieve effective tracking of maneuvering targets. Additionally, the optimized IMMPF is combined with track before detect algorithm (TBD) to suppress clutter and accumulate target trajectory. Simulation experiment results demonstrate that the proposed algorithm can effectively track targets compared to other TBD algorithms. © 2024 SPIE.

关键词： Monte Carlo methods

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Adaptive multi maneuver target model tracking method based on digital images

Adaptive multi maneuver target model tracking method based o...

引用

2024 International conference on image processing and Artificial Intelligence, ICIPAl 2024

作者： Liu, Gang Wang, Ronghui Basic Science Department Air-force Aviation University Jilin Changchun130022 China Electric and Computer Department Jilin Architecture University Jilin Changchun130022 China

ISBN: (纸本)9781510681514

The primary approach to analyzing the model of a maneuvering target is to treat the target's maneuvers as an increase in the state noise variance. Within this context, the Singer model and the "current" statistical model are extensively employed, while the Jerk model is recommended for targets with high agility. This study conducts simulation tests on three frequently utilized models for maneuvering targets. It finds that the Singer model is apt for describing targets moving at a steady speed or with uniform acceleration, whereas the "current" statistical model is particularly effective for modeling target movements involving step changes in acceleration, especially in comparison to the Jerk model. The outcomes of these simulation tests reveal that the proposed method achieves a root mean square error (RMSE) of 3.92 when transitioning from uniform acceleration to step-acceleration maneuvers. This RMSE is significantly lower than those of 6.23 and 4.78, indicating enhanced tracking precision. © 2024 SPIE.

关键词： Acceleration

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FSTU-Net: Volumetric fast convolution and hierarchical transformer for faster medical image segmentation 16

FSTU-Net: Volumetric fast convolution and hierarchical trans...

引用

16th International conference on digital image processing, ICDIP 2024

作者： Li, Xuan Wang, Han Chen, Wanhui Liu, Peishun Tang, Ruichun College of Information Science and Technology Ocean University of China Qingdao266100 China Qingdao Center for Disease Control and Prevention Qingdao266000 China

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

Recently, hybrid CNN-Transformer models have attained a state-of-the-art performance in 3D medical segmentation tasks. The effectiveness of this approach is predominantly ascribed to the large receptive field for non-local self-attention, albeit at the cost of a substantial number of model parameters. Despite the introduction of deep convolution to reduce FLOPs of the model, deep convolution still has the problem of frequent memory access. In this paper, we introduce FSTU-Net, a lightweight 3D medical image segmentation model, which aims to adapt the inherent features of the Swin Transformer through ConvNet modules and enhance volumetric segmentation performance using a smaller model capacity. Specifically, we introduce Volumetric Fast Convolution (FConv) with Large Kernel (LK) size to emulate the large receptive field operations generated from attention in the Swin Transformer. We have developed a simple, rapid, and efficient new Fast Convolution (FConv) to eliminate the problem of frequent memory access associated with deep convolution or group convolution, reducing computational redundancy and the number of memory accesses. It holds significant potential to replace the current preferred method of deep convolution. We validated our FConv method and FSTU-Net model on BTCV and AMOS 2022 public datasets, assessing both efficiency and accuracy. Our results demonstrated state-of-the-art performance, effectively reducing the number of model parameters. © 2024 SPIE.

关键词： image segmentation

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Research on underwater degraded image enhancement technology based on lightweight network 3

Research on underwater degraded image enhancement technology...

引用

3rd International conference on digital Signal and Computer Communications, DSCC 2023

作者： Luo, Zhikun Zhu, Yifeng Zhao, Mingzhu Changchun University of Science and Technology CA Satellite road ChangChun China

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

The underwater imaging environment is relatively complex. The light in the water has blurred image edges, low overall contrast, and the objects in the water are difficult to recognize. This paper proposes a method based on depth learning and physical model to enhance the quality of underwater image. The enhanced image is obtained by building the module to calculate the important parameters of the model combined with the input. The experimental results show that the algorithm in this paper has faster processing speed and better imaging effect than traditional methods, and can correct the color deviation of underwater images. © 2023 SPIE.

关键词： image enhancement

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