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Fabric image retrieval based on multi-modal feature fusion

SIGNAL image AND VIDEO processing 2024年第3期18卷 2207-2217页

作者： Zhang, Ning Liu, Yixin Li, Zhongjian Xiang, Jun Pan, Ruru Jiangnan Univ Key Lab Ecotext Minist Educ Wuxi Jiangsu Peoples R China Shaoxing Univ Inst Artificial Intelligence Coll Text & Fash Shaoxing Zhejiang Peoples R China

With the increasing of multi-source heterogeneous data, flexible retrieval across different modalities is an urgent demand in industrial applications. To allow users to control the retrieval results, a novel fabric image retrieval method is proposed in this paper based on multi-modal feature fusion. First, the image feature is extracted using the modified pre-trained convolutional neural network to separate macroscopic and fine-grained features, which are then selected and aggregated by the multi-layer perception. The feature of the modification text is extracted by long short-term memory networks. Subsequently, the two features are fused in a visual-semantic joint embedding space by gated and residual structures to control the selective expression of separable image features. To validate the proposed scheme, a fabric image database for multi-modal retrieval is created as the benchmark. Qualitative and quantitative experiments indicate that the proposed method is practicable and effective, which can be extended to other similar industrial fields, like wood and wallpaper.

关键词： Separable feature extraction Multi-modal feature fusion Visual-semantic joint embedding Fabric retrieval

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Acoustic-to-hyper-spectral: real-time perimeter intrusion detection system monitoring through learnable filters and hyper-spectral image generation from distributed acoustic sensing systems

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OPTICS EXPRESS 2025年第3期33卷 4109-4126页

作者： Pierau, Ruth-emely Katsifolis, Jim Meehan, Alaster Rezatofighi, Hamid Stuckey, Peter J. Monash Univ Clayton Vic 3800 Australia Univ Melbourne ARC Training Ctr Optimisat Technol Integrated Meth Parkville Vic 3052 Australia AVA Risk Grp Co Future Fibre Technol 10 Hartnett CL Mulgrave Vic 3170 Australia

This paper presents an integrated distributed acoustic sensing (DAS) system with artificial intelligence to provide real-time system monitoring for fence perimeter and buried system applications. The DAS system is a Rayleigh backscatter based fibre optic sensing system that has been deployed in two real-world, commercial applications to detect acoustic wave propagation and scattering along perimeter lines, and classify intrusions accurately. What we believe to be three novel signal processing methods are proposed to train filters for automatically selecting frequency bands from the power spectrum and generating hyper-spectral images from the data gathered by the DAS system without expert knowledge. The hyper-spectral images are analyzed by a neural network based object detection model. The system achieves 81.8% accuracy on a fence perimeter installation and 60.4% accuracy on a buried system application in detecting and classifying various intrusion events. The evaluation interval of the integrated DAS system framework between event sensing and detection does not exceed 5 s. (c) 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Deep learning Hyperspectral imaging Machine learning neural networks Signal processing Wave propagation

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Advancements in memory technologies for artificial synapses

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JOURNAL OF MATERIALS CHEMISTRY C 2024年第15期12卷 5274-5298页

作者： Sehgal, Anubha Dhull, Seema Roy, Sourajeet Kaushik, Brajesh Kumar Indian Inst Technol Roorkee Roorkee 247667 Uttarakhand India

neural networks (NNs) have made significant progress in recent years and have been applied in a broad range of applications, including speech recognition, image classification, automatic driving, and natural language processing. The hardware implementation of NNs presents challenges, and research communities have explored various analog and digital neuronal and synaptic devices for resource-efficient implementation. However, these hardware NNs face several challenges, such as overheads imposed by peripheral circuitry, speed-area tradeoffs, non-idealities associated with memory devices, low on-off resistance ratio, sneak path issues, low weight precision, and power-inefficient converters. This article reviews different synaptic devices and discusses the challenges associated with implementing these devices in hardware, along with corresponding solutions, and prospecting future research directions. Several categories of emerging synaptic devices such as resistive random-access memory (RRAM), phase change memory (PCM), analog-to-digital hybrid volatile memory-based, ferroelectric field effect transistor (FeFET)-based, spintronic-based spin transfer, spin-orbit, magnetic domain wall (DW) and skyrmion synaptic devices have been explored, and a comparison between them is presented. This study provides insights for researchers engaged in the field of hardware neural networks. This article reviews different synaptic devices and discusses the challenges associated with implementing these devices in hardware, along with corresponding solutions, applications, and prospecting future research directions.

关键词： Speech recognition

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End-to-End artificial Intelligence-Based System for Automatic Stereo Camera Self-Calibration

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IEEE ACCESS 2024年 12卷 160927-160945页

作者： Satouri, Boutaina El Abderrahmani, Abdellatif Satori, Khalid USMBA Fac Sci Fes Dept Math & Informat LISAC Fes 30000 Morocco

Stereo camera self-calibration is a complex challenge in computer vision applications such as robotics, object tracking, surveillance and 3D reconstruction. To address this, we propose an efficient, fully automated End-To-End AI-Based system for automatic stereo camera self-calibration with varying intrinsic parameters, using only two images of any 3D scene. Our system combines deep convolutional neural networks (CNNs) with transfer learning techniques and fine-tuning. First, our end-to-end convolutional neural network optimized model begins by extracting matching points between a pair of stereo images. These matching points are then used, along with their 3D scene correspondences, to formulate a non-linear cost function. Direct optimization is subsequently performed to estimate the intrinsic camera parameters by minimizing this non-linear cost function. Following this initial optimization, a fine-tuning layer refines the intrinsic parameters for increased accuracy. Our hybrid approach is characterized by a special optimized architecture that leverages the strengths of end-to-end CNNs for image feature extraction and processing, as well as the pillars of our nonlinear cost function formulation and fine-tuning, to offer a robust and accurate method for stereo camera self-calibration. Extensive experiments on synthetic and real data demonstrate the superior performance of the proposed technique compared to traditional camera self-calibration methods in terms of precision and faster convergence.

关键词： Cameras Cost function Convolutional neural networks Three-dimensional displays Robot vision systems Solid modeling Accuracy Calibration Nonlinear distortion Mathematical models Camera self-calibration varying intrinsic parameters nonlinear direct optimization convolutional neural network transfer learning fine tuning matching interest points

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An improved deep learning-based optimal object detection system from images

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MULTIMEDIA TOOLS AND applications 2024年第10期83卷 30045-30072页

作者： Yadav, Satya Prakash Jindal, Muskan Rani, Preeti de Albuquerque, Victor Hugo C. Nascimento, Caio dos Santos Kumar, Manoj GL Bajaj Inst Technol & Management GLBITM Dept Comp Sci & Engn Greater Noida 201306 India Fed Inst Educ Sci & Technol Ceara IFCE Grad Program Telecommun Engn PPGET Fortaleza CE Brazil Amity Univ Dept Comp Sci & Engn Noida 201313 India SRM Inst Sci & Technol Dept Elect & Commun Engn Delhi NCR CampusDelhi Meerut Rd Ghaziabad Uttar Pradesh India Univ Fed Ceara Dept Teleinformat Engn Fortaleza CE Brazil Univ Wollongong Dubai Sch Comp Sceince FEIS Dubai Knowledge Pk Dubai U Arab Emirates Middle East Univ MEU Res Unit Amman 11831 Jordan

Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional neural networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms-You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional neural networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss.

关键词： Object Detection Chess Piece Identification You Only Look Once (YOLO) Single Stage Detector (SSD) Faster Region-Based Convolutional neural networks (R-CNN)

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Advancing Oncology Diagnostics: AI-Enabled Early Detection of Lung Cancer Through Hybrid Histological image Analysis

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IEEE ACCESS 2024年 12卷 64396-64415页

作者： Noaman, Naglaa F. Kanber, Bassam M. Al Smadi, Ahmad Jiao, Licheng Alsmadi, Mutasem K. Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Zarqa Univ Dept Data Sci & Artificial Intelligence Zarqa 13100 Jordan Imam Abdulrahman Bin Faisal Univ Coll Appl Studies & Community Serv Dept Management Informat Syst Dammam 34212 Saudi Arabia

Against the backdrop of the pervasive global challenge of cancer, with particular emphasis on lung cancer (LC), this study centers its investigation on the critical realm of early detection leveraging artificial intelligence (AI) within the domain of histological image analysis. Through the fusion of DenseNet201 with color histogram techniques, a novel hybrid feature set emerges, engineered to elevate classification accuracy. The comprehensive evaluation encompasses eight diverse machine learning (ML) algorithms, spanning from K-Nearest Neighbors (KNN) to Support Vector Machines (SVM), including notable contenders such as LightGBM (LGBM), CatBoost, XGBoost, decision trees (DT), random forests (RF), and multinomial naive Bayes (MultinomialNB). This rigorous examination illuminates a distinguished model, achieving a remarkable accuracy rate of 99.683% on the LC25000 dataset. The extension of this methodology to breast cancer detection, utilizing the BreakHis dataset, yields a commendable accuracy rate of 94.808%. These findings underscore the transformative potential of AI in the intricate landscape of histopathological analysis, positioning it as a pivotal force in advancing diagnostic capabilities. A meticulous comparative analysis not only underscores the merits but also elucidates the limitations of existing AI applications in medical imaging, thereby charting a roadmap for future refinements and clinical deployments. Consequently, continued research in AI within clinical settings is advocated, with the ultimate aim of fortifying early cancer diagnosis and subsequently enhancing patient outcomes through judicious therapeutic interventions.

关键词： Cancer Feature extraction image color analysis Histograms Convolutional neural networks Histopathology image processing Machine learning Lung cancer Medical diagnosis Oncology Densenet201 histopathological images image processing lung cancer machine learning

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Driving scene image Dehazing model based on multi-branch and multi-scale feature fusion

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neural networks 2025年 188卷 107495页

作者： Yin, Shi Liu, Hui Cent South Univ Sch Traff & Transportat Engn Inst Artificial Intelligence & Robot IAIR Key Lab Traff Safety TrackMinist Educ Changsha 410075 Hunan Peoples R China

image dehazing is critical for enhancing image quality in applications such as autonomous driving, surveillance, and remote sensing. This paper presents an innovative image dehazing model based on a multi-branch and multi scale feature fusion network that leverages spatial and frequency information. The model features a multi-branch architecture that combines local and global features through depthwise separable convolutions and state space models, effectively capturing both detailed and comprehensive information to improve dehazing performance. Additionally, a specialized module integrates spatial and frequency domain information by utilizing convolutional layers and Fourier transforms, enabling comprehensive haze removal through the fusion of these two domains. A feature fusion mechanism incorporates channel attention and residual connections, dynamically adjusting the importance of different channel features while preserving the global structural information of the input image. Furthermore, this is the first model to combine Mamba and convolution layers for driving scene image dehazing, achieving global feature extraction with linear complexity. Each image is processed in only 0.030 s, with a frame rate of 32.41 FPS and a processing efficiency of 67.96 MPx/s, ensuring high efficiency suitable for real-time applications. Extensive experiments on real-world foggy driving scene datasets demonstrate the superior performance of the proposed method, providing reliable visual perception capabilities and significantly improving adaptability and robustness in complex environments.

关键词： image dehazing State space model Frequency domain Autonomous driving

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Touching the limit of Rolling Multilayer Perceptron for efficient two-dimensional medical image segmentation

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ENGINEERING applications OF artificial INTELLIGENCE 2025年 153卷

作者： Liu, Yutong Zhu, Haijiang An, Ning Xu, Li Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China China Coal Res Inst Res Inst Mine Artificial Intelligence Beijing 100013 Peoples R China State Key Lab Intelligent Coal Min & Strata Contro Beijing 100013 Peoples R China Capital Med Univ Beijing Jishuitan Hosp Dept Radiol Beijing 100035 Peoples R China

Significant progress has been made in medical image segmentation using deep learning techniques, with the Ushaped architecture being a classic choice. However, effectively capturing and integrating both local features and remote dependencies remains a key challenge for improving deep learning-based segmentation methods. In this paper, we propose a flexible Rolling Multilayer Perceptron (Rolling-MLP) module to address this issue. Building upon this concept, we present the Rolling-Unet network, which combines the strengths of Multilayer Perceptrons (MLPs) with Convolutional neural networks (CNNs) to efficiently extract and fuse local features and remote dependencies. Furthermore, to explore the potential of Rolling-MLP for two-dimensional medical image segmentation, we propose Rolling-MLP configurations with distinct receptive field shapes (linear and area-shaped) and summarize the influence of Rolling-MLP's key parameters on the shape of receptive fields. We conducted extensive experiments on four datasets, surpassing a variety of state-of-the-art methods in accuracy. Moreover, Rolling-MLP is far ahead in Central processing Unit (CPU) inference speed, indicating its potential in medical cyber-physical systems engineering applications. This paper demonstrates the strong comprehensive ability of Rolling-MLP in two-dimensional medical image segmentation tasks, providing a novel approach for constructing medical image segmentation networks, alternative to CNNs and Transformers.

关键词： Cyber-physical systems engineering application Medical image segmentation Multilayer perceptron Remote dependency

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Transformer-based image and video inpainting: current challenges and future directions

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artificial INTELLIGENCE REVIEW 2025年第4期58卷 1-45页

作者： Elharrouss, Omar Damseh, Rafat Belkacem, Abdelkader Nasreddine Badidi, Elarbi Lakas, Abderrahmane United Arab Emirates Univ Coll Informat Technol Dept Comp Sci & Software Engn Al Ain U Arab Emirates United Arab Emirates Univ Coll Informat Technol Dept Comp & Network Engn Al Ain U Arab Emirates

image inpainting is currently a hot topic within the field of computer vision. It offers a viable solution for various applications, including photographic restoration, video editing, and medical imaging. Deep learning advancements, notably convolutional neural networks (CNNs) and generative adversarial networks (GANs), have significantly enhanced the inpainting task with an improved capability to fill missing or damaged regions in an image or a video through the incorporation of contextually appropriate details. These advancements have improved other aspects, including efficiency, information preservation, and achieving both realistic textures and structures. Recently, Vision Transformers (ViTs) have been exploited and offer some improvements to image or video inpainting. The advent of transformer-based architectures, which were initially designed for natural language processing, has also been integrated into computer vision tasks. These methods utilize self-attention mechanisms that excel in capturing long-range dependencies within data;therefore, they are particularly effective for tasks requiring a comprehensive understanding of the global context of an image or video. In this paper, we provide a comprehensive review of the current image/video inpainting approaches, with a specific focus on Vision Transformer (ViT) techniques, with the goal to highlight the significant improvements and provide a guideline for new researchers in the field of image/video inpainting using vision transformers. We categorized the transformer-based techniques by their architectural configurations, types of damage, and performance metrics. Furthermore, we present an organized synthesis of the current challenges, and suggest directions for future research in the field of image or video inpainting.

关键词： image inpainting Video inpainting Vision transformers

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X-ray PCB defect automatic diagnosis algorithm based on deep learning and artificial intelligence

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neural COMPUTING & applications 2023年第36期35卷 25263-25273页

作者： Liu, Yaojun Wang, Ping Liu, Jingjing Liu, Chuanyang Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210002 Jiangsu Peoples R China Wuhu State Owned Factory Machining Wuhu 241000 Anhui Peoples R China Chizhou Univ Coll Mech & Elect Engn Chizhou 247000 Anhui Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210002 Jiangsu Peoples R China

As a main electronic material, X-ray circuits are widely used in various electronic devices, and their quality has an important impact on the overall quality of electronic products. In the process of mass production of circuit boards, due to the large number of layers, tight lines and some harmful external factors, circuit board quality may be problematic. Detecting circuit board defects are important for improving the reliability of electronic products. This paper introduces deep learning and artificial intelligence technology to conduct research on the automatic detection of X-ray circuit board defects. The study used a defect detection system to study X-ray circuit boards as a detection object and obtained the structure, lighting system and composition of the detection system. The working principle of the detection system is explained, and the image is preprocessed. Testing the processing performance of the PCB defect detection system, when the number of pixels is 6526, 7028, 7530 and 8032, the time consumption ratios between the proposed detection system and image processing on a traditional PC are 35.17%, 35.4%, 35% and 35.28%, respectively. The experimental results make a certain contribution to the future artificial intelligence X-ray PCB defect automatic diagnosis algorithm.

关键词： artificial intelligence artificial neural networks Circuit board defects Diagnostic algorithms

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