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Scene Change Captioning in Real Scenarios 3rd

Scene Change Captioning in Real Scenarios

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3rd international conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 24th international conference on Human-computer Interaction (HCII)

作者： Qiu, Yue Nakashima, Kodai Satoh, Yutaka Suzuki, Ryota Iwata, Kenji Kataoka, Hirokatsu Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki 3058560 Japan

ISBN: (纸本)9783031056437;9783031056420

This paper discusses the scene change captioning task that describes scene changes using natural language for real scenarios. Most current three-dimensional understanding tasks focus on recognizing static scenes. Despite its importance in a variety of real environment applications, scene change understanding remains less discussed. Existing change understanding methods discussed in robotics focus on change detection and lack the ability to perform detailed recognition of scene changes. Most previous experiments on change captioning methods were conducted on simulation datasets with limited visual complexity, limiting their availability for real scenarios. To solve the above issues, we propose a scene change captioning dataset with scenes photographed using RGB-D cameras. We also propose an automatic simulation dataset generation process, aiming for training models transferring to real scenarios. We conducted experiments with various input modalities and proposed a method that integrates different input modalities using an attention mechanism over modalities and dynamic attention to select related information during the sentence generation process. The experimental results show that models trained on the proposed simulation dataset obtained promising results on real scenario dataset, indicating the proposed dataset generation process's practicality in real scenarios. The proposed multimodality integrating method can generate change captions with high change type and object attribute accuracy while showing robustness in real scenarios. We hope our work can open a door for future research on scene change understanding in real scenarios.

关键词： computer vision Deep learning Natural language processing

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Enhanced License Plate Detection using YOLOv8n: An Approach for Efficient Vehicle Identification

Enhanced License Plate Detection using YOLOv8n: An Approach ...

引用

Applied Intelligence and Computing (AIC), IEEE World conference on

作者： Vishesh Chandok Sudhanshu Maurya Kashish Mirza Ajay Kumar Yadav Sneha Thomas Varghese Rachit Garg Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Computer Science & Engineering Graphic Era Hill University Bhimtal Campus Uttarakhand India Department of Computer Application United Institute of Management Prayagraj India School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

An essential feature of the intelligent transportation system is the automatic detection and recognition of license plate information. The e-payment systems for parking and toll collection in traffic control security could be utilized with this application. Numerous algorithms have been created for License Plate Recognition (LPR) and Identification, each with pros and cons depending on the circumstances. computer vision has increased in terms of techniques and developments since deep learning emerged and became popular in several artificial intelligence domains. It is one of the most efficient ways to monitor traffic signals, tax paid for parking, and police monitoring through Automatic LPR. Although it, as a technology, is widely used and developed, much more must be done to improve the system's accuracy. The current science community has excelled in efficiency and problem-solving in recent years. Along with the You Only Look Once (YOLO), targeted at the object detection area and applied to this study, our primary goal is to summarize and analyze several methods and advancements in LPR in the deep learning age.

关键词： YOLO Deep learning Adaptation models Accuracy Surveillance Traffic control Real-time systems Safety Security License plate recognition

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H∞ Tracking control Design of Networked Mobile Robot with Transmission Delay

H∞ Tracking Control Design of Networked Mobile Robot with T...

引用

Artificial Intelligence, Human-computer Interaction and Robotics (AIHCIR), international conference on

作者： Zhiyue Zhou Suiwu Zheng Shanlin Zuo Jie Wang School of Advanced Manufacturing Guangdong University of Technology Guangdong China National Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Mechanical Engineering Sichuan University of Science and Engineering Zigong China School of Mechanical Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798331534035

ISBN: (纸本)9798331534042

Our current research is concerned with $H$ ∞ tracking control topic for networked mobile robot. The transmission delay of control system is considered, which could be induced by the networked environment. Moreover, the $H$ ∞ controller is developed to cope with mobile robot's system disturbance. With the above control strategies, sufficient conditions are established firstly to accomplish a prescribed H ∞ performance index. Then, a desired controller is designed under this help of linear matrix inequality convex optimization. At the end of work, the effectiveness of designed controller could be demonstrated on the basis of a numerical example.

关键词： Human computer interaction Sufficient conditions Stability criteria Convex functions Delays Performance analysis Linear matrix inequalities Mobile robots Indexes Numerical stability

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Simulation of Magnetoplasmadynamic thruster with a fluid model 3

Simulation of Magnetoplasmadynamic thruster with a fluid mod...

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3rd international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2022

作者： Zhao, Boqiang Li, Yong Zhou, Cheng Zheng, Jinxing Beijing Institute of Control Engineering Beijing100190 China Institute of Plasma Physics Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China

ISBN: (纸本)9781510657281

Since the applied-field magnetoplasmadynamic thruster (AF-MPDT) has the unique characteristics of high plasma density, strong magnetic flux density and non-equilibrium state, an innovative simulation model, namely the multi-component magnetic hydrodynamic (MHD) model, is proposed in this work. The conventional MHD model includes 2 sets of equations, which are the fluid describing the motion of plasma and the Maxwell equations for the electromagnetic description. In the proposed multi-component MHD model, the Maxwell equations are maintained, but the fluid part is improved by considering the two components of the plasma, electrons and ions, separately. In order to describe the relationship between the electrons and ions, the high-frequency collision model of the two components and the strong electromagnetic acceleration model of the plasma are further coupled with the fluid part. Various significant parameters such as the plasma density distribution and the ion velocity distribution of the AF-MPDT can be obtained by solving the model with a high spatial-temporal resolution Total Variation Diminishing (TVD) scheme. The acquired simulation results show that the density and axial velocity of both electrons and ions are low when the magnetic flux density is absent or low. Interestingly, with the increase of the magnetic flux density, electrons and ions gradually gather near the anode and cathode, leading to an increase of both the density and velocity, and therefore the performance of the thruster is greatly improved. © 2022 SPIE.

关键词： Ions

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Automated Detection of COVID-19 from X-ray Images using Deep Convolutional Neural Networks 3

Automated Detection of COVID-19 from X-ray Images using Deep...

引用

3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Sachin Bhat, Aruna Delhi Technological University Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665438117

Coronavirus, also known as COVID19, is a dangerous disease that has put many people's lives in jeopardy around the world by damaging the lungs directly. The detection of coronavirus is a challenging medical procedure due to its increasing cases. Currently, the use of x-ray images for coronavirus diagnosis is commonly used. Recently, various deep learning based models have been used for image classification. These models have generated competitive results in terms of feature selection and classification. In this article, we proposed a set of seven pretrained neural network models (VGG16, VGG19, InceptionV3, ResNet50, Xception, DenseNet121 and InceptionResNetV2) for the detection of coronavirus infection using chest X-ray images collected from an open source. It was observed that out of these models, pretrained DenseNet121 yielded highest classification accuracy of 97% for the particular dataset. © 2021 IEEE.

关键词： Coronavirus

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Machine Aided 3D Urban Design based on Style Transfer 3

Machine Aided 3D Urban Design based on Style Transfer

引用

3rd international Academic Exchange conference on Science and Technology Innovation, IAECST 2021

作者： Dong, Zhiyong Liu, Yubo Deng, Qiaoming State Key Laboratory of Subtropical Building Science South China University of Technology Guangzhou China

ISBN: (纸本)9781665402675

Deep learning is widely used to identify input factors and make effective processing recently. Neural style transfer based on deep learning has been used in many fields such as art and computer vision, and it is also used as a tool of generative design to inspire the field of architecture. This article aims to control multiple influencing factors through data sorting and labeling and uses style transfer to realize more effective urban intention generation. Through adjusting self-defined parameters, the machine would generate the creative project as an urban plan for the real situation. Then labeled information of the output image is processed reversely to obtain height data and generate 3D models. According to the generated result, the article analyses whether it makes an effective response to the input factors and conditions, so as to discuss its rationality and feasibility. © 2021 IEEE.

关键词： Deep learning

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An Optimization Scheme for Consortium Blockchain in Airport Collaborative Decision Making 3

An Optimization Scheme for Consortium Blockchain in Airport ...

引用

3rd IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2021

作者： Zhang, Xiaohui Miao, Xianghua Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Faculty of Information Engineering and Automation Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming China

ISBN: (纸本)9781665425186

Consortium blockchain-based A-CDM (Airport Collaborative Decision Making) was a decentralized technology solution proposed by some researchers in recent years to solve robustness, privacy, and security issues in the traditional centralized architecture. The key idea of blockchain-based A-CDM is to use blockchain technology to store the flight operation data in a distributed, consistent, and tamper-proof ledger, further improve the capability of flight management and reduce the trust barriers among the multi-stakeholders such as airport, air traffic management bureau, airlines, and ground service agency, etc. The RAFT consensus was adopted in many blockchain-based data sharing solutions to meet the need for efficiency in data transmission. However, the RAFT algorithm can only be used in a non-byzantine network because security requirements such as preventing malicious nodes are not fully considered. It will bring an immeasurable impact on flight operation when the leader node of RAFT-based A-CDM was caused by malicious control. In response to the above problems, this paper proposes an optimization scheme to improve the security performance of the consortium blockchain in A-CDM. © 2021 IEEE

关键词： Blockchain

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Indoor Target Detection and Pose Estimation Based on Point Cloud 3

Indoor Target Detection and Pose Estimation Based on Point C...

引用

2021 3rd international conference on computer, Communications and Mechatronics Engineering, CCME 2021

作者： Zhang, Di Zhang, Weimin Li, Fangxing Guo, Ziyuan Nie, Fuyu Jin, Jiahao Wang, Yang Shi, Yongliang Huang, Qiang School of Mechatronical Engineering Beijing Institute of Technology Beijing100811 China Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Ministry of Education China Beijing Advanced Innovation Center for Intelligent Robots and Systems China

The indoor environment faced by service robots is complex, with plenty of objects and mutual occlusion, traditional algorithms cannot detect the target and estimate its pose accurately. We propose a set of target detection and pose estimation algorithm based on point cloud templates to solve the above problems. Through the segmentation and clustering of scenes, the application of the algorithm in complex scenes is realized. Based on the key points to estimate the pose of the target, we achieve the adaptation to mutual occlusion. Besides, we propose an improved RANSAC algorithm, which maintains the accuracy of the original algorithm while greatly improving the speed. Finally, we test the algorithm through the open source model library, simulated and actual scenarios, compared with the SAC-IA algorithm, our algorithm improves the time by 89%, and in the application of complex scenes, the error of the pose estimation is at the millimeter level. © Published under licence by IOP Publishing Ltd.

关键词： computer vision

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Enhancing Text Detection in Natural Scenes: A Hybrid Approach with MSER, Connected Components, and Norm-CLAHE

Enhancing Text Detection in Natural Scenes: A Hybrid Approac...

引用

Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Prabu Selvam Akshaj Nevgi C. Gunasundari Sowrish V K Natarajan B S. Sharon Jessika School of Computer Science and Engineering VIT University Chennai India School of Computer Science and Engineering VIT Vellore Chennai India Department of Computer Science and Engineering Roever Engineering College Perambalur Tamilnadu Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering VIT University Vellore India School of Computer Science and Engineering VIT Vellore Vellore India

The textual information present in natural scene images is a valuable clue for many vision-based applications. Locating text areas becomes particularly challenging due to issues like varying lighting conditions, complex backgrounds, non-uniform text sizes, and more. Detecting curved text, in particular, is a demanding task in the fields of computer vision and pattern recognition. This research work proposes a robust approach for the detection of text in natural scene images with different orientations. The proposed method is divided into three key stages: (i) Pre-Processing: The original image is converted to grayscale, and this research propose a new Norm-Contrast-limited Adaptive Histogram Equalization (CLAHE) to enhance the contrast in the grayscale image by limiting noise amplification and prevents saturation in dark and bright text regions. (ii) Text Region Extraction: this research combined Otsu's thresholding, Maximally Stable Extremal Region (MSER) and Sobel edge de-tector to extract text regions and enhance edges in the image. (iii) Non-Text Region Removal: In this stage, this research combine Connected Component (CC), Morphological operations, MSER region filters, and basic text geometric properties to create a framework for removing non-text regions and drawing rectangle bounding boxes around the detected text regions. this research assessed the performance of the proposed method utilizing the ICDAR 2017 text dataset, which yielded an overall accuracy of 93.47%, demonstrating highly satisfactory results.

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Automated Retinal Disease Classification: Leveraging Convolutional Neural Networks for Early Diagnosis

Automated Retinal Disease Classification: Leveraging Convolu...

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Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Vishnu Kant Nagendar Yamsani Charanjit Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Departtment of CSE Chandigarh University Punjab India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Retinal diseases—including diabetic retinopathy, age-related macular degeneration (AMD), and retinal vein occlusion—are main causes of vision impairment and blindness worldwide, therefore profoundly influencing health and wellbeing. Early diagnosis and intervention are key to preventing significant vision loss; nevertheless, sometimes specific tools and qualified professionals are required by standard diagnosis methods, therefore limiting access. This work employs CNNs in a unique approach for automatic eye illness classification. The proposed CNN model learns to identify and categorize various retinal disorders efficiently using a 2,560-tagged retinal picture dataset acquired from the Kaggle platform, hence promoting innovation. The architecture is aimed to extract hierarchical components from retinal images, hence enhancing classification accuracy. With an overall accuracy of 82% on an external test set, the model was able, following analysis, to identify and classify retinal diseases. Faster and more accurate diagnosis helps CNNs considerably lower the load on healthcare providers, improve patient outcomes, and boost better education for medical professionals. Future studies will focus on enhancing the performance of the model by means of additional optimization and validation on more extensive, more varied datasets, therefore guaranteeing its sustainability for practical uses in retinal disease diagnosis and management.

关键词： Macular degeneration Deep learning Diabetic retinopathy Accuracy Retina Convolutional neural networks Optimization Medical diagnostic imaging Diseases Load modeling

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