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Biometric-Based Access Control Systems with Robust Facial recognition in IoT Environments 3

Biometric-Based Access Control Systems with Robust Facial Re...

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3rd ieee International conference on Intelligent Techniques in Control, Optimization and Signal processing. INCOS 2024

作者： Satish Babu, B.V. Manoranjini, J. Changala, Ravindra Aarif, Mohd Mary, S. Suma Christal Raj, I Infant Prasad v Potluri Siddhartha Institute of Technology Department of It Vijayawada India Chaitanya Bharathi Institute of Technology Department of Ai & Ml Hyderabad India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India Associate Editor-Publishing Global Research Network Noida India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India

ISBN: (纸本)9798350361186

The integration of biometric-based access control systems with robust facial recognition technology within Internet of Things (IoT) environments holds paramount significance in addressing contemporary security challenges. The novelty of the research endeavours to contribute to this critical area by investigating and implementing a novel approach, employing Convolutional Neural Networks (CNN) for facial classification within the biometric framework. The significance of this study lies in its potential to enhance security measures in IoT environments, ensuring reliable and efficient access control. Previous attempts at integrating facial recognition into IoT have encountered notable challenges, including suboptimal accuracy, vulnerability to adversarial attacks, and scalability issues. This research builds upon these prior issues by introducing CNN for facial classification, leveraging its capabilities in image processing.and pattern recognition to achieve enhanced accuracy and resilience against potential security threats. The novelty of the study is underscored by the application of CNN within the biometric context of IoT, offering a cutting-edge solution tailored to the intricacies of interconnected devices. Experiments conducted on benchmark datasets showcase the promising capabilities of the proposed system, with face recognition accuracies reaching an impressive 98%. These results underscore the potential of this research to significantly advance the state-of-the-art in biometric-based access control, particularly within the context of IoT environments. The utilization of CNN introduces a pioneering element, contributing to the evolution of secure and efficient access control solutions in the era of IoT. © 2024 ieee.

关键词： Biometrics

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Integrating dual attention with TransUNet for esophagus image segmentation

Integrating dual attention with TransUNet for esophagus imag...

引用

2024 International conference on image, Signal processing. and pattern recognition, ISPP 2024

作者： Yu, Zhehao Jiang, Shaochen Wang, Liejun College of Computer Science and Technology Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510680425

Esophageal cancer is a common digestive tract tumor with a high mortality rate. Currently, the identification of esophageal carcinoma predominantly hinges on thoracic computed tomography scanning. Because the area of the esophagus on CT images is small and belongs to the small target region, the traditional image segmentation network is difficult to accurately segment the tumor area of esophageal cancer. Although the traditional U-Net architecture and Transformer integrated variants perform well in medical segmentation tasks, they lack image localization and channel feature extraction capabilities. To address the aforementioned issues, this manuscript proposes a novel medical image segmentation model, christened DAA-TransUNet. We propose a new module called Dual Attention Area (DAA) module,it can extract image position and channel features. And we combine the DAA module and the Efficient Channel Attention (ECA) module into a new module, which we call the Efficient Channel Dual Attention (ECDA) module. DAA-TransUNet integrates Transformer and ECDA modules into a traditional U-shaped architecture, enabling it to extract not only global and local information, but also image position and channel features. We have verified the superior performance of our network by performing numerous experiments on our collected esophageal dataset. We used DAA-TransUNet to perform experiments on our collected dataset, and the average DSC(%) reached 75.26% and the average HD95(mm) reached 3.54. © 2024 SPIE.

关键词： image segmentation

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Synthesizing CTA image Data for Type-B Aortic Dissection using Stable Diffusion Models 46

Synthesizing CTA Image Data for Type-B Aortic Dissection usi...

引用

46th Annual International conference of the ieee Engineering in Medicine and Biology society, EMBC 2024

作者： Abaid, Ayman Ali Farooq, Muhammad Hynes, Niamh Corcoran, Peter Ullah, Ihsan University Hospital Galway University of Galway Newcastle Road Ireland University of Galway School of Computer Science Ireland University of Galway School of Engineering College of Science and Engineering Ireland University of Galway Insight Sfi Research Center for Data Analytics Ireland

ISBN: (纸本)9798350371499

Stable Diffusion (SD) has gained a lot of attention in recent years in the field of Generative AI thus helping in synthesizing medical imaging data with distinct features. The aim is to contribute to the ongoing effort focused on overcoming the limitations of data scarcity and improving the capabilities of ML algorithms for cardiovascular image processing. Therefore, in this study, the possibility of generating synthetic cardiac CTA images was explored by fine-tuning stable diffusion models based on user defined text prompts, using only limited number of CTA images as input. A comprehensive evaluation of the synthetic data was conducted by incorporating both quantitative analysis and qualitative assessment, where a clinician assessed the quality of the generated data. It has been shown that Cardiac CTA images can be successfully generated using using Text to image (T2I) stable diffusion model. The results demonstrate that the optimized T2I CTA diffusion model successfully render images with features that are typically unique to acute type B aortic dissection (TBAD) medical conditions. © 2024 ieee.

关键词： computerized tomography

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Multiview heterogeneous graph representation learning with fusion of high-order information and low-order information

Multiview heterogeneous graph representation learning with f...

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2024 International conference on image, Signal processing. and pattern recognition, ISPP 2024

作者： Sun, Dengdi Meng, Yundong Luo, Bin Ding, Zhuanlian School of Artificial Intelligence Anhui University Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China School of Internet Anhui University Hefei230039 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China

ISBN: (纸本)9781510680425

Graph neural networks (GNNs) are extensively employed in the analysis of data structured as graphs and excel at learning intricate node relationships. However, traditional GNN methods encounter difficulties when applied to analyzing heterogeneous graphs (HGs) with diverse node types and relationships, due to the complex heterogeneity and semantics of the data. Therefore, we propose Multi-view Heterogeneous Graph Representation Learning with Fusion of High-Order Information and Low-Order Information(MHGRL). MHGRL produces node embeddings by integrating node content transformation, meta-path-based intra- and inter-aggregation, as well as the amalgamation of low- and high-order information from multiple perspectives. Comprehensive experiments on three diverse heterogeneous graph datasets reveal the significant benefits of our MHGRL in node classification endeavors. © 2024 SPIE.

关键词： Contrastive Learning

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Adaptive division of multilayer feature fusion for Detailed 3D reconstruction

Adaptive division of multilayer feature fusion for Detailed ...

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2024 International conference on image, Signal processing. and pattern recognition, ISPP 2024

作者： Li, Saiya Jiang, Guoqing Zhang, Zongliang Su, Jinhe JiMei University The School of Computer Engineering Xiamen China

ISBN: (纸本)9781510680425

Point cloud is a versatile and easily accessible lightweight data representation, is widely used in visual ***, the discrete nature of 3D objects, along with the challenges posed by noise interference and uneven density, contribute to the complexity of accurately reconstructing 3D *** to only using global features for 3D reconstruction makes the result surface difficult to describe the target details, and only using local features for 3D reconstruction makes the result surface *** this paper, we propose a method of adaptive division and multilayer feature fusion to reconstruction the three-dimensional shapes with unoriented point *** adjusted the octree division rule based on neural network to adaptively divide the target point cloud with a more reasonable division *** addition, we inherit the moving least squares method based on the octree and transform the discrete point set into a three-dimensional *** difference is that we combine the octree features of different depths to obtain the context information of the shape in a local-plus-global *** conduct comparative experiments with other learning-based methods on ShapeNet dataset, our method outperforms advanced learning-based surface reconstruction approaches, shows superior quantitative and qualitative results. © 2024 SPIE.

关键词： image reconstruction

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Predicting blood pressure from the change of photoplethysmography features

Predicting blood pressure from the change of photoplethysmog...

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2024 International conference on image, Signal processing. and pattern recognition, ISPP 2024

作者： Chen, Qin Yang, Xuezhi Chen, Yawei Wang, Dingliang School of Computer and Information Hefei University of Technology Hefei230601 China Anhui Province key Laboratory of Industry Safety and Emergency Technology Hefei230601 China Department of Electronic Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9781510680425

Blood pressure (BP) is a crucial indicator for assessing cardiovascular health, and effective monitoring contributes to early treatment of cardiovascular diseases. Photoplethysmography (PPG) has shown immense potential in non-invasive BP monitoring. However, existing studies often overlook the impact of feature variations on BP changes. Therefore, this study propose a blood pressure estimation method based on PPG feature changes. Initially, high-quality PPG segments are obtained through signal processing.from PPG waveforms, and 68 contributory PPG differential features (DF) for BP changes are derived through differencing. Subsequently, optimal subsets of DFs are identified using correlation analysis and stepwise forward feature selection. Finally, an interpretable model is employed to map DF to BP changes, incorporating calibration values for accurate BP estimation. The proposed method is evaluated on self-collected datasets. The root mean square error (RMSE) for systolic blood pressure (SBP) is 8.39 mmHg, and for diastolic blood pressure (DBP), it is 5.75 mmHg. The corresponding Pearson correlation coefficient (PCC) values are 0.88 and 0.83. Correlation analysis indicates a close relationship between PPG differential features and BP changes. Compared to direct use of PPG features, the RMSE of BP estimation improves by approximately 40%, highlighting the effectiveness of PPG differential features in BP estimation. © 2024 SPIE.

关键词： Blood pressure

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Cross-modal image text retrieval based on Cross-optimization and Joint strategy

Cross-modal image text retrieval based on Cross-optimization...

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2024 International conference on image, Signal processing. and pattern recognition, ISPP 2024

作者： Zheng, Wei Han, Na Hu, Yan Kang, Peipei Zhang, Yue Yang, Aqing Zhang, Lei School of Computer Science Guangdong Polytechnic Normal University China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data China School of Computer Science and Technology Guangdong University of Technology China School of Information Engineering East University of Heilongjiang China

ISBN: (纸本)9781510680425

In the fields of computer vision and natural language processing. cross-modal retrieval is of great importance that cannot be ignored. In existing multi-granularity alignment methods, significant progress has been made by globally aligning images and sentences or locally aligning regions and words. However, most of these methods rely on attention mechanisms, which may be affected by noise and cannot balance the differences between modalities, resulting in suboptimal image-text alignment. In addition, training with attention mechanisms consumes a lot of computational resources, and the retrieval process is time-consuming. To address these challenges, we develop a novel multigranularity image-text alignment model, which we call DFVLM. First, we independently train the intra-modal encoder and cross-modal encoder to reduce interference between different modal encoders and better learn intra-modal and inter-modal information. Then, we propose a joint strategy to combine the intra-modal and inter-modal information to better capture the information between image and text. In addition, we introduce a hard negative pair-based method to train the performance of the intra-modal multi-granularity encoder without using attention mechanisms. Unlike existing methods, We treat image and text retrieval as a two-way process and delve into the intrinsic connections between the two matrices. We propose a cross-validation method to optimize the retrieval result based on common image-to-text alignment scenarios in daily life, i.e., during the image-to-text retrieval process, a text-to-image reverse verification is performed on the retrieved corresponding text. Through extensive qualitative experiments and analysis, our approach performs well on both Flickr30K and MSCOCO datasets and also significantly reduces the time required for testing. © 2024 SPIE.

关键词： Natural language processing.systems

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Visual Tracking with Confidence Correction based on Log-polar Mapping

Visual Tracking with Confidence Correction based on Log-pola...

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2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Liang, Qifeng Liu, Peiqiang Fu, Jingyi An, Zhiyong School of Computer Science and Technology Shandong Technology and Business University Shandong Yantai264005 China

ISBN: (纸本)9781510666351

Transformer-based object tracking has become mainstream due to its high tracking performance at present. Most transformer-based trackers still classify the foreground and background and then calculate the bounding box by regression. However, we find that the classification confidence of transformer tracking is inaccurate. This leads to the bounding box with the highest confidence is not the closest to the ground truth. To address the problem, we propose a robust visual tracking approach with confidence correction using log-polar mapping. By correcting the confidence, the transformer tracking can obtain the best bounding box and improve the accuracy of the tracker. Extensive experiments on OTB2015, UAV123, and UAV20L show that our tracker is superior to the state-of-the-art trackers. © 2023 SPIE.

关键词： Deep learning

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Single-Shot End-to-end Road Graph Extraction

Single-Shot End-to-end Road Graph Extraction

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ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Bahl, Gaetan Bahri, Mehdi Lafarge, Florent IRT St Exupery Toulouse France Imperial Coll London London England Univ Cote Azur INRIA Nice France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Automatic road graph extraction from aerial and satellite images is a long-standing challenge. Existing algorithms are either based on pixel-level segmentation followed by vectorization, or on iterative graph construction using next move prediction. Both of these strategies suffer from severe drawbacks, in particular high computing resources and incomplete outputs. By contrast, we propose a method that directly infers the final road graph in a single pass. The key idea consists in combining a Fully Convolutional Network in charge of locating points of interest such as intersections, dead ends and turns, and a Graph Neural Network which predicts links between these points. Such a strategy is more efficient than iterative methods and allows us to streamline the training process by removing the need for generation of starting locations while keeping the training end-to-end. We evaluate our method against existing works on the popular RoadTracer dataset and achieve competitive results. We also benchmark the speed of our method and show that it outperforms existing approaches. Our method opens the possibility of in-flight processing.on embedded devices for applications such as real-time road network monitoring and alerts for disaster response.

关键词： Training Satellites Head Roads image edge detection Feature extraction Graph neural networks

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A proposed matrix-based Pythagorean fuzzy set distance metric and its usage in disease diagnosis

A proposed matrix-based Pythagorean fuzzy set distance metri...

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2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Zhao, Rongcheng Academy of Computer and Information Science Software Academy Southwest University ChongQing China

ISBN: (纸本)9781510666351

It is currently unclear how to calculate the distance between two Pythagorean fuzzy sets. Several techniques have been proposed. However, not all available approaches can accurately exhibit differences between Pythagorean fuzzy sets and fulfill the axiom of measurement characteristic. And some other methods ignore the relation between the three variables of the Pythagorean fuzzy set. A new direction of estimating distance is presented to address the issue. Then some numerical examples are provided to demonstrate that the way of measuring distances avoids producing counter-intuitive and irrational conclusions and is more effective and reasonable than other similar methods. Furthermore, the suggested method of measuring distances between PFSs is evaluated in a real-world application scenario and compared to earlier approaches to illustrate its superiority and efficiency. At the same time, the viability of the suggested method in dealing with uncertainties in practice is demonstrated. © 2023 SPIE.

关键词： Fuzzy sets

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