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20th CSI International Symposium on Artificial Intelligence and Signal processing, AISP 2024

作者： Mirsharji, Seyed Ali Ghassemian, Hassan Tarbiat Modares University Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350383942

In this study, we present an innovative unsupervised hyperspectral image classification method using a dual-branch architecture that merges spatial and spectral feature extraction. Our unique approach employs masked autoencoders, significantly outperforming traditional methods with an impressive overall accuracy of 97.1%. The paper details the model's performance evaluation, offers visual insights into its classification capabilities, and compares it with existing techniques, demonstrating its effectiveness and potential for advancing remote sensing applications. © 2024 IEEE.

关键词： image classification

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FIFAWC:a dataset with detailed annotation and rich semantics for group activity recognition

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Frontiers of Computer Science 2024年第6期18卷 271-272页

作者： Duoxuan PEI Di HUANG Yunhong WANG State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Intelligent Recognition and Image Processing Lab. School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the c... 详细信息

关键词： has collective gained

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Unsupervised Hyperspectral image Classification: Spatial and Spectral Feature Fusion with Masked Autoencoders

Unsupervised Hyperspectral Image Classification: Spatial and...

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International Symposium on Artificial Intelligence and Signal processing (AISP)

作者： Seyed Ali Mirsharji Hassan Ghassemian Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

In this study, we present an innovative unsupervised hyperspectral image classification method using a dual-branch architecture that merges spatial and spectral feature extraction. Our unique approach employs masked autoencoders, significantly outperforming traditional methods with an impressive overall accuracy of 97.1%. The paper details the model’s performance evaluation, offers visual insights into its classification capabilities, and compares it with existing techniques, demonstrating its effectiveness and potential for advancing remote sensing applications.

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Research on auto focusing algorithm of microscopic imaging system

Research on auto focusing algorithm of microscopic imaging s...

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2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2022

作者： Lv, Meini Yang, Qiuhui Ma, Wenjing Huang, Zeqiong College of Wuzhou Wuzhou China Wuzhou University Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Wuzhou China

ISBN: (数字)9781665480901

ISBN: (纸本)9781665480901

In the intelligent microscopic imaging system, the focusing evaluation function is one of the important core links in the automatic focusing system. In order to solve the problem that the focusing curve loses the characteristics of the ideal curve caused by illumination change, an automatic focusing algorithm based on Robert and wavelet transform is proposed in this paper. The algorithm extracts the maximum value of the high-frequency component wavelet coefficients decomposed by the wavelet transform, and uses the extracted wavelet coefficients to weight the Robert evaluation value coefficients, increases the sensitivity factor of the algorithm, and makes an experimental analysis with five traditional focusing functions under the condition of different illumination intensity to verify the feasibility of the algorithm. The experimental results show that the algorithm can overcome the change of illumination, has strong anti noise, sensitivity and stability, and is suitable for the fine focusing process in the microscopic imaging system. © 2022 IEEE.

关键词： Imaging systems

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IRSEnet: Differentially Private image Generation with Multi-Scale Feature Extraction and Residual Channel Attention 13

IRSEnet: Differentially Private Image Generation with Multi-...

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13th International Conference on Intelligent Control and Information processing, ICICIP 2025

作者： Li, Jiahao Wang, Zhongshuai Ghazali, Kamarul Hawari Bin Yan, Suqing Lan, Rushi Sun, Xiyan Luo, Xiaonan Guangxi Key Lab. of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China Centre for Advanced Industrial Technology University of Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia Int. Joint Research Lab. of Spatio-temporal Information and Intelligent Location Services Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9798331516147

Privacy-preserving image generation is particularly crucial in fields like healthcare, where data are both sensitive and limited. However, effective privacy preservation often compromises the visual quality and utility of the generated images due to privacy budget constraints. To address this issue, in this paper, We propose a novel network architecture, IRSEnet, which combines multi-scale feature extraction technology and residual channel attention mechanisms, aiming to enhance the visual quality of generated images and improve the performance of downstream classification tasks under differential privacy. The differential privacy mechanism ensures the security of sensitive data during training, while the multi-scale feature extraction module enhances feature extraction capabilities through parallel convolutional layers at multiple scales. Additionally, the channel attention module dynamically adjusts channel weights to focus on the most discriminative features. Experimental results demonstrate that this model significantly improves the utility of generated images and the accuracy of downstream classification tasks while preserving privacy. Future work will explore the application of this approach on larger datasets and across more diverse tasks. © 2025 IEEE.

关键词： Differential privacy

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Deep Learning Based Discovery of New Breast Cancer Stages: StyleGAN3 and Swin Transformer Approach

Deep Learning Based Discovery of New Breast Cancer Stages: S...

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Medical Technologies National Conference (TIPTEKNO)

作者： Reyhan Dede Gokhan Bilgin Department of Computer Engineering Signal and Image Processing Lab. (SIMPLAB) at YTU Yildiz Technical University (YTU) Istanbul Turkey

ISBN: (数字)9798331529819

ISBN: (纸本)9798331529826

This paper introduces a novel deep learning framework for the discovery of breast cancer stages, which integrates GAN-generated synthetic images with multi-omics data. By employing StyleGAN3 for the generation of realistic histopathological images and Swin Transformer for classification, the model draws upon both visual and biological data to enhance the accuracy of cancer staging predictions. The proposed methodology entails the generation of high-quality synthetic images using StyleGAN3, with a Fréchet Inception Distance (FID) score of 35, indicating a reasonable degree of similarity to real images. The images, in conjunction with RNA, miRNA, and clinical data, are integrated into a Swin Transformer-based classifier, resulting in an accuracy of 95.03 %, a precision of 95.00 %, and an F1 score of 95.00 %. A threshold-based softmax probability analysis was employed during the inference stage to explore the potential discovery of new cancer stages. The preliminary observation-based threshold of 30 % may be optimized through further experimentation. In the event that the model exhibited a confidence level for a given class below the specified threshold, the image was identified as a potential candidate for a previously unidentified stage. This study underscores the potential of multimodal data integration in enhancing breast cancer staging and offers insights into leveraging deep learning models for generating and classifying histopathological data, alongside identifying novel disease stages.

关键词： Deep learning Visualization Accuracy RNA Data integration Predictive models Transformers Breast cancer Data models Biomedical imaging

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Discussion on application of FLIC-Fluent coupled simulation technology on medium-scale and large-scale municipal solid waste incinerators 6

Discussion on application of FLIC-Fluent coupled simulation ...

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6th International Conference on Intelligent Computing, Communication, and Devices, ICCD 2023

作者： Zhang, Hongze Guangxi Key Laboratory of Machine Vision and Intelligent Control Guangxi Wuzhou China Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Guangxi Wuzhou China School of Electronics and Information Engineering Wuzhou University Guangxi Wuzhou China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

Today, the world economy is in the stage of rapid development, followed by the rising quantity, the decreasing moisture content, and the increasing heat value of Municipal Solid Waste (MSW). Since waste incineration has the advantages of excellent volume reduction effect, high resource utilization efficiency, and less secondary pollution to the environment, waste incineration disposal technology has received extensive attention worldwide. With the continuous increase of the urban population, the amount of sewage sludge is growing year by year. The process of the sludge incineration system is highly complicated, and the cost of construction and operation is high. The waste incinerator can process not only MSW but also sewage sludge. Therefore, the co-incineration of sewage sludge with MSW in the existing waste incinerator is a safe and effective way to dispose of sewage sludge. In this paper, the simulation results of Computational Fluid Dynamics (CFD) are analyzed for medium-scale and large-scale waste incinerators, characterized by high moisture content, high ash content, low heat value of sewage sludge, and incomplete combustion and high pollutant emissions in waste incinerators. With FLIC-Fluent coupled simulation method, the critical information such as temperature field, velocity field, concentration field of principal components, and the pollutant emissions can be predicted. Based on the CFD simulation results, the performance and structure of waste incinerators can be optimized, and the new products can be designed and developed. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Waste incineration

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A Micro-Expression Recognition Network Based on Attention Mechanism and Motion Magnification

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IEEE Transactions on Affective Computing 2024年

作者： Wu, Falin Xia, Yu Ma, Boyi Hu, Tianyang Yang, Jingyao Li, Haoxin Huang, Di Beihang University SNARS Laboratory School of Instrumentation and Optoelectronic Engineering Beijing China Beihang University Intelligent Recognition and Image Processing Lab. School of Computer Science and Engineering Beijing China

Micro-expressions (MEs) are spontaneous facial movements that reveal an individual's genuine emotions and play a crucial role in various domains, including lie detection, criminal analysis, mental health treatment, national security, and others. Micro-expression recognition is a highly complex aspect within the domain of affective computing, aimed at identifying subtle facial motions that are difficult for humans to discern accurately. To model the subtle facial muscle motions and the brief duration of MEs, we propose a robust micro-expression recognition (MER) network, named the attention mechanismbased motion magnification guided micro-expression recognition network (AM-MM-MER). This network consists of two primary components: the ST-MEMM network, which enhances subtle motions in micro-expression videos to reveal imperceptible facial muscle motions, and the AM-MER, which focuses on facial landmarks related to micro-expressions and incorporates novel landmark positions to extract the underlying relationships among these landmarks, thereby reducing interference from video magnification and irrelevant identity features. Extensive analysis on the CASME II and SAMM datasets demonstrates the high accuracy and effectiveness of the proposed network, achieving superior results compared to state-of-the-art methods. Ablation studies further illustrate the robustness of the proposed network. © 2010-2012 IEEE.

关键词： National security

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Neural Networks with Divisive normalization for image segmentation with application in cityscapes dataset

arXiv

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arXiv 2022年

作者： Hernández-Cámara, Pablo Laparra, Valero Malo, Jesús Image Processing Lab. Universitat de València Paterna46980 Spain

One of the key problems in computer vision is adaptation: models are too rigid to follow the variability of the inputs. The canonical computation that explains adaptation in sensory neuroscience is divisive normalization, and it has appealing effects on image manifolds. In this work we show that including divisive normalization in current deep networks makes them more invariant to non-informative changes in the images. In particular, we focus on U-Net architectures for image segmentation. Experiments show that the inclusion of divisive normalization in the U-Net architecture leads to better segmentation results with respect to conventional U-Net. The gain increases steadily when dealing with images acquired in bad weather conditions. In addition to the results on the Cityscapes and Foggy Cityscapes datasets, we explain these advantages through visualization of the responses: the equalization induced by the divisive normalization leads to more invariant features to local changes in contrast and illumination. © 2022, CC BY.

关键词： image segmentation

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Neural Networks with Divisive Normalization for image Segmentation

SSRN

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SSRN 2022年

作者： Hernández-Cámara, Pablo Vila-Tomás, Jorge Laparra, Valero Malo, Jesús Image Processing Lab. Universitat de València Paterna46980 Spain

One of the key problems in computer vision is adaptation: models are too rigid to follow the variability of the inputs. The canonical computation that explains adaptation in sensory neuroscience is divisive normalization, and it has appealing effects on image manifolds. In this work we show that including divisive normalization in current deep networks makes them more invariant to non-informative changes in the images. In particular, we illustrate this concept in U-Net architectures for image segmentation. Experiments show that the inclusion of divisive normalization in the U-Net architecture leads to better segmentation results with respect to the conventional U-Net. The gain increases steadily when dealing with images acquired in bad weather conditions. In addition to the positive results on the Cityscapes and Foggy Cityscapes datasets, we explain these advantages through the visualization of the responses: the equalization induced by the divisive normalization leads to more invariant features to local changes in contrast and illumination. © 2022, The Authors. All rights reserved.

关键词： image segmentation

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