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作者： Zhang, Chao Zhang, Jinmei Yun, Lijun Zhang, Jun Su, Junbo Yunnan Key Laboratory of Optoelectronic Information Technology Yunnan Normal University Kunming China School of Physics and Electronic Information Yunnan Normal University Kunming China Engineering Research Center of Computer Vision and Intelligent Control Technology Department of Education of Yunnan Province Yunnan Normal University Kunming China Kunming Institute of Physics Ordnance Science and Research Academy of China Kunming China

The neural network-based technologies have emerged as a potent method for image fusion, object detection, and other computer vision tasks as the rapid development of deep learning. Multi-band infrared images, in particular, capture a more extensive range of radiation details and information compared to conventional single-band infrared images. Consequently, the fusion of multi-band infrared images can provide more features for object detection. However, it is crucial to consider that infrared images may contain sensitive information, potentially leading to privacy concerns. Ensuring datasets privacy protection plays a crucial role in the fusion and tracking process. To address both the need for improved detection performance and the necessity for privacy protection in the infrared environment, we proposed a procedure for object detection based on multi-band infrared image datasets and utilized the transfer learning technique to migrate knowledge learned from external infrared data to internal infrared data, thereby training the infrared image fusion model and detection model. The procedure consists of several steps: (1) data preprocessing of multi-band infrared images, (2) multi-band infrared image fusion, and (3) object detection. Standard evaluation metrics for image fusion and object detection ensure the authenticity of the experiments. The comprehensive validation experiments demonstrate the effectiveness of the proposed procedure in object detection tasks. Furthermore, the transfer learning can train our datasets and update the model without exposing the original data. This aspect of transfer learning is particularly beneficial for maintaining the privacy of multi-band infrared images during the fusion and detection processes. © 2024 John Wiley & Sons Ltd.

关键词： Object detection

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Security and Privacy Concerns in 'Communication Systems' Blockchain Technology and the Internet of Things 3

Security and Privacy Concerns in 'Communication Systems' Blo...

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3rd International Conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Prakash, R.Om Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Noida Instue Engineering and Technology U.P Greator Noida201306 India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

This article examines the security and privacy concerns associated with the integration of blockchain technology and the Internet of Things (IoT) in communication systems. As the IoT continues to grow and evolve, there is an increasing need to ensure the security and privacy of the vast amounts of data generated and exchanged between connected devices. Blockchain technology offers promising solutions for enhancing security and privacy in communication systems;however, it also presents its own set of challenges and vulnerabilities. This article explores the existing literature on the topic and analyzes the materials and methods used in previous studies. The results and discussions delve into the key security and privacy concerns in communication systems with blockchain and IoT, including data integrity, authentication, and confidentiality, scalability, and consensus mechanisms. Finally, the article concludes with recommendations and future directions for addressing these concerns and advancing the security and privacy of communication systems in the context of blockchain technology and the IoT. © 2023 IEEE.

关键词： Internet of things

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Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text Guidance

arXiv

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arXiv 2024年

作者： Arrabi, Ahmad Zhang, Xiaohan Sultani, Waqas Chen, Chen Wshah, Safwan Vermont Artificial Intelligence Lab. Department of Computer Science University of Vermont United States Intelligent Machines Lab. Information Technology University Center for Research in Computer Vision University of Central Florida United States

Aerial imagery analysis is critical for many research fields. However, obtaining frequent high-quality aerial images is not always accessible due to its high effort and cost requirements. One solution is to use the Ground-to-Aerial (G2A) technique to synthesize aerial images from easily collectible ground images. However, G2A is rarely studied, because of its challenges, including but not limited to, the drastic view changes, occlusion, and range of visibility. In this paper, we present a novel Geometric Preserving Ground-to-Aerial (G2A) image synthesis (GPG2A) model that can generate realistic aerial images from ground images. GPG2A consists of two stages. The first stage predicts the Bird’s Eye View (BEV) segmentation (referred to as the BEV layout map) from the ground image. The second stage synthesizes the aerial image from the predicted BEV layout map and text descriptions of the ground image. To train our model, we present a new multi-modal cross-view dataset, namely VIGORv2, built upon VIGOR [66] with newly collected aerial images, maps, and text descriptions. Our extensive experiments illustrate that GPG2A synthesizes better geometry-preserved aerial images than existing models. We also present two applications, data augmentation for cross-view geo-localization and sketch-based region search, to further verify the effectiveness of our GPG2A. The code and dataset are available at https://***/vail-uvm/gpg2a. Copyright © 2024, The Authors. All rights reserved.

关键词： Aerial photography

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HPPS: A Hierarchical Progressive Perception System for Luggage Trolley Detection and Localization at Airports

arXiv

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arXiv 2024年

作者： Sun, Zhirui Zhang, Zhe Zhao, Jieting Ye, Hanjing Wang, Jiankun Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Shenzhen Key Laboratory of Robotics and Computer Vision Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

The robotic autonomous luggage trolley collection system employs robots to gather and transport scattered luggage trolleys at airports. However, existing methods for detecting and locating these luggage trolleys often fail when they are not fully visible. To address this, we introduce the Hierarchical Progressive Perception System (HPPS), which enhances the detection and localization of luggage trolleys under partial occlusion. The HPPS processes the luggage trolley’s position (x, y) and orientation (θ) separately, which requires only RGB images for labeling and training, eliminating the need for 3D coordinates and alignment. The HPPS can accurately determine the position of the luggage trolley with just one well-detected keypoint and estimate the luggage trolley’s orientation when it is partially occluded. Once the luggage trolley’s initial pose is detected, HPPS updates this information continuously to refine its accuracy until the robot begins grasping. The experiments on detection and localization demonstrate that HPPS is more reliable under partial occlusion compared to existing methods. Its effectiveness and robustness have also been confirmed through practical tests in actual luggage trolley collection tasks. A website about this work is available at HPPS. Copyright © 2024, The Authors. All rights reserved.

关键词： robotics

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Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuclei Instance Segmentation in Histology Images

Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuc...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuening Wu Yiqing Shen Qing Zhao Yanlan Kang Ruiqi Hu Wenqiang Zhang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Department of Computer Science Johns Hopkins University Baltimore MD USA Department of Applied Mathematics Hong Kong Polytechnic University Hong Kong Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology School of Computer Science Fudan University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurate nuclei segmentation is fundamental in histology image analysis, playing an essential role in cancer grading and diagnosis. However, this task remains challenging due to variations in staining protocols, heterogeneity among nuclei types, and the densely clustered nature of nuclei. While SAM exhibits zero-shot generalization capabilities in natural image segmentation, its performance degrades when applied to nuclei segmentation in histology images. Existing adaptations of SAM for medical imaging primarily focus on organ or lesion segmentation, which differs substantially from nuclei segmentation due to the unique characteristics of nuclei—specifically, their sparse distribution combined with dense clustering. To address these challenges, we propose Edge-SAN (Segment Any Nuclei with Edge Prompting), an interactive segmentation foundation model specifically designed for nuclei segmentation. Edge-SAN introduces a novel edge prompting method that enhances the delineation of nuclei boundaries, particularly among densely clustered nuclei, by leveraging edge information to improve segmentation accuracy. We evaluate Edge-SAN on 12 diverse datasets in both few-shot and zero-shot scenarios, demonstrating its effectiveness as a foundation model for nuclei segmentation, achieving 66.81% AJI and 73.13% DSC—improvements of 16.33% and 15.48% over SAM-Med2D, respectively. The code is available at https://***/deep-geo/Edge-SAN.

关键词： Instance segmentation Training Technological innovation Accuracy Histopathology Foundation models Image edge detection Biological system modeling Real-time systems Cancer

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Flickr30K-CFQ: A Compact and Fragmented Query Dataset for Text-image Retrieval

arXiv

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arXiv 2024年

作者： Liu, Haoyu Song, Yaoxian Wang, Xuwu Zhu, Xiangru Li, Zhixu Song, Wei Li, Tiefeng Research Center for Intelligent Robotics Zhejiang Lab China Department of Engineering Mechanics Center for X-Mechanics Zhejiang University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

With the explosive growth of multi-modal information on the Internet, unimodal search cannot satisfy the requirement of Internet applications. Text-image retrieval research is needed to realize high-quality and efficient retrieval between different modalities. Existing text-image retrieval research is mostly based on general vision-language datasets (e.g. MS-COCO, Flickr30K), in which the query utterance is rigid and unnatural (i.e. verbosity and formality). To overcome the shortcoming, we construct a new Compact and Fragmented Query challenge dataset (named Flickr30K-CFQ) to model text-image retrieval task considering multiple query content and style, including compact and fine-grained entity-relation corpus. We propose a novel LLM-based Query-enhanced method using prompt engineering based on LLM. Experiments show that our proposed Flickr30-CFQ reveals the insufficiency of existing vision-language datasets in realistic text-image tasks. Our LLM-based Query-enhanced method applied on different existing text-image retrieval models improves query understanding performance both on public dataset and our challenge set Flickr30-CFQ with over 0.9% and 2.4% respectively. Our project can be available anonymously in https://***/view/Flickr30K-cfq. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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A Conditional GAN Based Approach for Distorted Camera Captured Documents Recovery 1

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4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence, MedPRAI 2020

作者： Souibgui, Mohamed Ali Kessentini, Yousri Fornés, Alicia Computer Vision Center Computer Science Department Universitat Autònoma de Barcelona Bellaterra Spain Digital Research Center of Sfax 3021 MIRACL Laboratory Sfax University Sfax Tunisia

ISBN: (数字)9783030718046

ISBN: (纸本)9783030718039

Many of the existing documents are digitized using smart phone’s cameras. These are highly vulnerable to capturing distortions (perspective angle, shadow, blur, warping, etc.), making them hard to be read by a human or by an OCR engine. In this paper, we tackle this problem by proposing a conditional generative adversarial network that maps the distorted images from its domain into a readable domain. Our model integrates a recognizer in the discriminator part for better distinguishing the generated images. Our proposed approach demonstrates to be able to enhance highly degraded images from its condition into a cleaner and more readable form. © 2021, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Adaptive Control of Euler-Lagrange Systems under Time-varying State Constraints without a Priori Bounded Uncertainty

arXiv

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arXiv 2023年

作者： Sankaranarayanan, Viswa Narayanan Satpute, Sumeet Gajanan Roy, Spandan Nikolakopoulos, George Robotics and Artificial Intelligence Group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Robotics Research Center International Institute of Information Technology Hyderabad India

In this article, a novel adaptive controller is designed for Euler-Lagrangian systems under predefined time-varying state constraints. The proposed controller could achieve this objective without a priori knowledge of system parameters and, crucially, of state-dependent uncertainties. The closed-loop stability is verified using the Lyapunov method, while the overall efficacy of the proposed scheme is verified using a simulated robotic arm compared to the state of the art. © 2023, CC BY-NC-ND.

关键词： Lyapunov methods

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HerStory: combating gender stereotypes by highlighting women's contribution to the history of societies 25

HerStory: combating gender stereotypes by highlighting women...

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25th International Symposium on computers in Education, SIIE 2023

作者： Garcia-Holgado, Lucia Garcia-Holgado, Alicia Verdugo-Castro, Sonia Silva, Erika Garcia Garcia-Penalvo, Francisco J. Kosta, Antrea Sofianou, Dimitra Psara, Emily Conde, Miguel A. Universidad de Salamanca Grial Research Group Research Institute for Educational Sciences Salamanca Spain Sikeso Advisory Center for the Support of the Family Nicosia Cyprus KEAN-Cell of Alternative Youth Activities Athens Greece Center for Social Innovation Cyprus Nicosia Cyprus Universidad de León Robotics Group Department of Mechanics Computer Science and Aerospace Engineering León Spain

ISBN: (纸本)9798350329315

HerStory project addresses the need of European Union (EU) to combat gender-based stereotypes, a priority underscored by the EU Council. Also, it adheres to the main aim of the EU Gender Equality strategy 2020-2025, of "Leading equally throughout society"as well as the objectives of the Gender Action Plan III. The project's goal is to highlight the significant contribution of women in history across European cities. HerStory aims to develop a transnational map that will feature important women from different regions shedding light on important historical moments where women's actions were decisive. This initiative will tackle the gender stereotypes that often exclude women from decision-making bodies, whether in politics and social life or work. At the same time, it will promote positive role models operating as an empowerment tool for girls and women. The project's outcomes will provide capacity building and useful methods to youth experts, educators, academics representing a conscious effort to shift gender-related attitudes through education and youth engagement. © 2023 IEEE.

关键词： Decision making

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Test-Time Adaptation with Robust Dual-stream Perturbation for Stable Agent Deployment in Dynamic Scenarios

Test-Time Adaptation with Robust Dual-stream Perturbation fo...

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International Conference on computer Supported Cooperative Work in Design

作者： Haoyu Xiong Chengfu Yang School of Information Science and Technology Yunnan Normal University Kunming China Department of Education of Yunnan Province Engineering Research Center of Computer Vision and Intelligent Control Technology Kunming China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Test-Time Adaptation (TTA) aims to provide a deployed agent capable of adapting to the target domain distribution using only unlabeled test data. Most existing TTA methods have achieved success under mild conditions, such as independently sampled data from a single or multiple static domains. However, these attempts may cause the deployed agent to fail in dynamic scenarios, where the test environment undergoes continuous changes over time. Previous TTA methods attempt to improve the quality of pseudo-labels by designing data augmentation strategies based on domain information. However, this violates the TTA paradigm, i.e., no characteristics of the dataset can be known in advance. Motivated by this, we propose a Robust Dual-stream Perturbation Test-time adaptation approach, called RDPT, to further stabilize the deployed agent from two aspects: (1) boost the robustness to noisy samples by encouraging the model to be consistent with its original image-level perturbation samples; (2) dynamically filtering samples with high entropy values, taking into account the discrepancy between the original and perturbation samples. Extensive experiments on the CIFAR10C and CIFAR100C benchmarks demonstrate the effectiveness of our method in dynamic scenarios, which is also easy to implement and deploy. 1

关键词： Adaptation models Filtering Federated learning Perturbation methods Computational modeling Predictive models Data augmentation

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