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image denoising based on Swin Transformer Residual Conv U-Net

Image denoising based on Swin Transformer Residual Conv U-Ne...

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ACIS International conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Yong Gan Shaohui Zhou Haonan Chen Yuefeng Wang Zhengzhou University of Technology Zhengzhou China College of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou China

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

In the field of computer vision, image denoising remains a fundamental and challenging problem, playing a crucial role in the preprocessing of various image processing tasks. The introduction of Convolutional Neural Networks (CNNs) into the image denoising domain has yielded significant improvements across different levels of visual tasks. In recent years, models based on the Swin Transformer have also been applied to the image denoising field, demonstrating superior denoising performance that surpasses CNN-based methods, thus becoming advanced techniques in current image denoising research. This paper proposes a Swin-Conv module that combines the local modeling capabilities of residual convolutional layers with the non-local modeling capabilities of the Swin Transformer and integrates this module into the UNet architecture for image denoising. For the dataset used in the model training process, data augmentation techniques were employed to randomly enhance the dataset, thereby improving overall robustness. The results indicate that the proposed Swin Transformer Residual Conv U-Net model shows improvement over current advanced networks, achieving PSNR and SSIM values of 36.09 and 0.963 at $\sigma=15,33.87$ and 0.915 at $\sigma=25$, and 28.96 and 0.810 at $\sigma=50$.

关键词： Training visualization AWGN Computational modeling Noise reduction Transformers Data augmentation

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Evaluating Multi-Modal LLMs for Automatically Recognizing Semantic Elements in UML Use Case Diagram images

Evaluating Multi-Modal LLMs for Automatically Recognizing Se...

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ieee International conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Jameleddine Hassine Information and Computer Science Department KFUPM Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems KFUPM Dhahran Saudi Arabia

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

Requirements engineering commonly employs UML Use Case Diagrams (UCD) to visually capture system interactions and functionality, facilitating clear communication between stakeholders. Recognizing and extracting semantic information from UCDs is essential for applications such as automated requirements extraction and system design validation, which improves software analysis accuracy, and streamlines model understanding for both developers and stakeholders. Recent advancements in large language models (LLMs) with visual processing capabilities enable interpreting intricate diagrammatic content. This paper evaluates multi-modal LLMs, specifically GPT-4o and GPT-4o-mini, in accurately identifying semantic elements within UCDs. We conducted experiments on a new dataset of UCDs and other diagrams collected from online sources. Experimental results show that both models struggled to accurately identify and interpret key UCD elements, often misclassifying or overlooking essential ones.

关键词： visualization image recognition Large language models Unified modeling language Semantics Software Stakeholders Requirements engineering Data mining System analysis and design

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6th International conference on Microelectronics, Electromagnetics, and Telecommunications, ICMEET 2021

6th International Conference on Microelectronics, Electromag...

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6th International conference on Microelectronics, Electromagnetics, and Telecommunications, ICMEET 2021

ISBN: (纸本)9789811685538

The proceedings contain 50 papers. The special focus in this conference is on Microelectronics, Electromagnetics, and Telecommunications. The topics include: Optical Letter Recognition for Roman-Text;intelligent Noise Detection and Correction with Kriging on Fundus images of Diabetic Retinopathy;a Nanoplasmonic Ultra-wideband Antenna for Wireless communications;antenna Array Synthesis of Shaped Beam Using Deterministic Method;a Novel Semi-blind Digital image Watermarking Using Fire Fly Algorithm;Real-Time image Enhancement Using DCT Techniques for Video Surveillance;performance Analysis of Automatic Modulation Recognition Using Convolutional Neural Network;design of Wearable Microstrip Patch Antenna for Biomedical Application with a Metamaterial;estimation of Gender Using Convolutional Neural Network;automatic Modulation Recognition of Analog Modulation Signals Using Convolutional Neural Network;taxonomy on Breast Cancer Analysis Using Neural Networks;a Novel Cuckoo Search with Levy Distribution-Optimized Density-Based Clustering Model on MapReduce for Big Data Environment;drowsiness Detection System for Drivers Using 68 Coordinate System;human Action Recognition in Videos Using Deep Neural Network;Performance Analysis of Underwater Acoustic Communication System with Massive MIMO-OFDM;360° Video Summarization: Research Scope and Trends;Construing Crop Health Dynamics Using UAV-RGB based SpaceTech Analytics and image processing;review of Different Binarization Techniques Used in Different Areas of image Analysis;An Improved Unsharp Masking (UM) Filter with GL Mask;on Performance Improvement of Wireless Push Systems Via Smart Antennas;classification of Non-fluctuating Radar Target Using ReliefF Feature Selection Algorithm;Design of Arrayed Rectangular Probe Patch Antenna at 6.2 GHz for 5G Small Cell Applications;visual Words based Static Indian Sign Language Alphabet Recognition using KAZE Descriptors;preface.

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Vision, Deduction and Alignment: An Empirical Study on Multi-Modal Knowledge Graph Alignment

Vision, Deduction and Alignment: An Empirical Study on Multi...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yangning Li Jiaoyan Chen Yinghui Li Yuejia Xiang Xi Chen Hai-Tao Zheng Shenzhen International Graduate School Tsinghua University Peng Cheng Laboratory Department of Computer Science The University of Manchester Tencent

Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in knowledge engineering. Existing EA methods mostly focus on utilizing the graph structures and entity attributes (including literals), but ignore images that are common in modern multi-modal KGs. In this study we first constructed Multi-OpenEA — eight large-scale, image-equipped EA benchmarks, and then evaluated some existing embedding-based methods for utilizing images. In view of the complementary nature of visual modal information and logical deduction, we further developed a new multi-modal EA method named LODEME using logical deduction and multi-modal KG embedding, with state-of-the-art performance achieved on Multi-OpenEA and other existing multi-modal EA benchmarks.

关键词： Knowledge engineering visualization Knowledge graphs Benchmark testing Signal processing Acoustics Speech processing

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Hardware-Accelerated ZYNQ-NET Convolutional Neural Networks on Virtex-7 FPGA

Hardware-Accelerated ZYNQ-NET Convolutional Neural Networks ...

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2021 International conference on Microelectronics, ICM 2021

作者： El-Maksoud, Ahmed J. Abd Gamal, Amr Hesham, Aya Saied, Gamal Ayman, Mennat-Allah Essam, Omnia Mohamed, Sara M. El Mandouh, Eman Ibrahim, Ziad Mohamed, Sara Mostafa, Hassan Cairo University Faculty of Engineering Electronics and Electrical Communications Department Giza12613 Egypt Mentor Graphics University of Science and Technology Nanotechnology and Nanoelectronics Program Zewail City of Science and Technology October Gardens 6th of October Giza12578 Egypt

ISBN: (纸本)9781665408394

Convolutional neural network is a class of deep neural networks that has made a great breakthrough in image recognition. CNNs are commonly used to detect and classify visual applications so that they are frequently embedded in image classification tasks. The common trend nowadays is to accelerate the processing of CNNs in order to use them in real-time applications such as image classification and object recognition. This paper presents the implementation of ZynqNet CNN architecture on FPGA. The full ZynqNet CNN layers are implemented on FPGA to reach the max acceleration and make full use of all DSP units. Several optimizations techniques are used in different design phases to improve processing speed, utilized area, and power consumption. In addition, the proposed hardware accelerator achieves 15.6 fps for ZynqNet CNN at maximum frequency. The proposed architecture runs at two different frequencies of 100MHz and 125MHz, and is implemented on Virtex-7 FPGA. © 2021 ieee.

关键词： Field programmable gate arrays (FPGA)

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Matpr-Unet: A Multi Attention Two-Path Residual Unet for Focal Cortical Dysplasia Lesions Segmentation

Matpr-Unet: A Multi Attention Two-Path Residual Unet for Foc...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Wenjing Zhang Hao Yu Manli Zhang Gongpeng Cao Guixia Kang Lixin Cai Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China Department of Pediatric Epilepsy Center Peking University First Hospital Beijing China

Medical imaging is now a widely used test for the preoperative evaluation of focal cortical dysplasia (FCD). Deep learning-based methods can learn lesion features from image data to automatically recognize and segment FCD in epilepsy treatment. However, the existing FCD segmentation networks lack the ability to fully extract the FCD lesion information and automatically aggregate the salient features of the lesions, the segmentation accuracy needs to be improved. To this end, we propose an end-to-end 3D Convolutional Neural Network segmentation model, Multi Attention Two-Path Residual UNet (MATPR-UNet). Specifically, we propose two modules: (1) Two-Path Residual Attention module, which can extract both local and global information, and suppress invalid information by fully fusing the features in channel and space; (2) Spatially Gated Attention module, which enables the model to automatically focus on the FCD lesion region, highlighting its salient features. We combine the proposed two modules with the 3D UNet to construct the MATPR-UNet. Extensive experiments on the private FCD dataset and the public EPISURG dataset demonstrate that our method outperforms other state-of-the-art methods, and is robust.

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A No Reference Deep Quality Assessment Index for 3D Colored Meshes

A No Reference Deep Quality Assessment Index for 3D Colored ...

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ieee International conference on Systems, Man and Cybernetics

作者： Zaineb Ibork Anass Nouri Olivier Lézoray Christophe Charrier Raja Touahni SETIME Laboratory Information Processing and A.I Team Faculty of Sciences Ibn Tofail University Kenitra Morocco Normandie Univ UNICAEN ENSICAEN CNRS GR-EYC Caen France

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The advent of 3D data has revolutionized various industries, from architecture and engineering to healthcare and entertainment, enabling more precise simulations and realistic visualizations. However, 3D data is susceptible to noise and loss during generation and transmission, making quality assessment crucial for ensuring accuracy and usability. While existing literature addresses quality assessment for 3D point clouds and meshes separately, a gap exists in assessing the quality of 3D colored meshes due to the lack of reference datasets. This paper proposes an approach for No Reference 3D Colored Mesh visual Quality Assessment (CMVQA), based on previous work related to quality assessment of 3D non colored meshes quality assessment. Our approach combines geometric and color features with spatial domain features extracted from mesh projections. Through extensive experiments and comparisons with full-reference metrics, including image quality metrics, our proposed approach demonstrates superior performance.

关键词： Measurement image quality visualization Solid modeling Three-dimensional displays Feature extraction Propagation losses Quality assessment Indexes Usability

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6th International conference on Microelectronics, Electromagnetics, and Telecommunications, ICMEET 2021

6th International Conference on Microelectronics, Electromag...

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6th International conference on Microelectronics, Electromagnetics, and Telecommunications, ICMEET 2021

ISBN: (纸本)9789811685491

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IFSeg: image-free Semantic Segmentation via Vision-Language Model

IFSeg: Image-free Semantic Segmentation via Vision-Language ...

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： Sukmin Yun Seong Hyeon Park Paul Hongsuck Seo Jinwoo Shin Korea Advanced Institute of Science and Technology (KAIST) Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Google Research

Vision-language (VL) pre-training has recently gained much attention for its transferability and flexibility in novel concepts (e.g., cross-modality transfer) across various visual tasks. However, VL-driven segmentation has been under-explored, and the existing approaches still have the burden of acquiring additional training images or even segmentation annotations to adapt a VL model to downstream segmentation tasks. In this paper, we introduce a novel image-free segmentation task where the goal is to perform semantic segmentation given only a set of the target semantic categories, but without any task-specific images and annotations. To tackle this challenging task, our proposed method, coined IFSeg, generates VL-driven artificial image-segmentation pairs and updates a pretrained VL model to a segmentation task. We construct this artificial training data by creating a 2D map of random semantic categories and another map of their corresponding word tokens. Given that a pretrained VL model projects visual and text tokens into a common space where tokens that share the semantics are located closely, this artificially generated word map can replace the real image inputs for such a VL model. Through an extensive set of experiments, our model not only establishes an effective baseline for this novel task but also demonstrates strong performances compared to existing methods that rely on stronger supervision, such as task-specific images and segmentation masks. Code is available at https://***/alinlab/ifseg.

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Boosted Spectral Perception via Synergistic Vision for Cross-Domain Few-Shot Hyperspectral image Classification

Boosted Spectral Perception via Synergistic Vision for Cross...

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Signal, Information and Data processing (ICSIDP), ieee International conference on

作者： Xinyi Yan Jiaojiao Li Zhiyuan Zhang Haitao Xu Tie Zheng Qian Du ISN Xidian University Xi’an China Key Laboratory of EITSS NSSC Beijing China Department of Electrical and Computer Engineering Mississippi State University Starkville USA

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Cross-domain few-shot learning (CDFSL) hyper-spectral image classification (HSIC) techniques have significantly enhanced the performance of HSIC under a few annotations. Due to the scattered spatial distribution of HSI and limited labels, the spatial information of the HSI sample is restricted, which means the spectral information may contain more discriminative category features than spatial information. However, most existing CDFSL HSIC methods lack targeted spectral discriminative feature extraction and struggle to capture spectral subtleties. To address this, a Human Eye-like Dual-Branch Network (HEDB-Net) is proposed, comprising a central branch and a peripheral branch. The central branch focuses on extracting core features from complete spectral dimension HSI patches. Simultaneously, these patches are input into the peripheral branch via random sampling and reintegration of spectral bands, enabling the model to perceive initially dispersed spectral information. These two branches represent the model’s central and peripheral vision for the spectrum, allowing it to focus on different levels of feature information and significantly boosting spectral perception capabilities. To fully leverage the advantages of synergistic vision, a consistency constraint is applied to align their category probability prediction distributions, which helps ensure consistency in classification decisions between the two branches. Experimental results on the PaviaU dataset demonstrate that HEDBNet outperforms other advanced algorithms in terms of overall accuracy(OA), average accuracy(AA), and Kappa coefficient. This proves that HEDBNet can simultaneously focus on primary and secondary spectral features, thus significantly boosting classification accuracy.

关键词： visualization Accuracy visual systems Feature extraction Boosting Classification algorithms Data mining Few shot learning image classification Hyperspectral imaging

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