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MOPO-HBT: A movie poster dataset for title extraction and recognition

Multimedia Tools and Applications 2024年第18期83卷 54545-54568页

作者： Ghosh, Mridul Roy, Sayan Saha Banik, Bivan Mukherjee, Himadri Obaidullah, Sk Md Roy, Kaushik Dept. of Computer Science Shyampur Siddheswari Mahavidyalaya Howrah India Dept. of Computer Science & Engineering Aliah University Kolkata India Dept. of Electronics & Electrical Communication Engineering IIT Kharagpur Kharagpur India TISA Lab Dept. of Computer Science West Bengal State University Barasat India

Real-world images often encompass embedded texts that adhere to disparate disciplines like business, education, and amusement, to name a few. Such images are graphically rich in terms of font attributes, color distribution, foreground-background similarity, and component organization. This aggravates the difficulty of recognizing texts from these images. Such characteristics are very prominent in the case of movie posters. One of the first pieces of information on movie posters is the title. Automatic recognition of movie titles from images can aid in efficient indexing as well as information conveyance. However, it is accompanied by other texts like the names of actors, producers, taglines, dates, etc. Though the organization of components is somewhat similar across different film industries like Tollywood (West Bengal), Bollywood (Mumbai), and Hollywood (Los Angeles), the graffiti patterns differ in multifarious instances. To address the problem of movie title understanding, we propose a dataset named MOvie POsters-Hollywood Bollywood Tollywood (MOPO-HBT) that encompasses movie posters from the aforementioned industries. The entire dataset is publicly available (http://***/11564) for research purposes. The baseline results of title identification and recognition were obtained with a CNN-based (Convolutional Neural Network) approach, wherein the titles were extracted using the M-EAST (Modified-Efficient and Accurate Scene Text) detector model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Character recognition

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Enhancing Bikers' Safety and Coordination: A LoRa-Enabled Wearable System for Smart Cycling 4

Enhancing Bikers' Safety and Coordination: A LoRa-Enabled We...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Krishna Prasad, D.S. Sandeep Kumar, S. Deeksha, G.D. Rao, Manasa Megha, S. Shashwathi, N. Dept. of Computer & Communication Engineering Nitte Karnataka574110 India

ISBN: (纸本)9798331515720

In recent years, the popularity of biking, especially mountain biking, has surged, drawing enthusiasts seeking adventure and adrenaline amidst rugged landscapes. However, with this surge in popularity comes an increased awareness of the safety challenges inherent in the sport, particularly in remote or challenging terrains where traditional communication methods often fail to provide adequate support. Recognizing these challenges, the paper presents a LoRa-enabled wearable system tailored to enhance safety and coordination for mountain bikers, focusing on challenging terrain scenarios. By incorporating GPS functionality, the system delivers crucial information such as status updates and emergency notifications, thereby markedly elevating safety standards for riders. Furthermore, the project enables real-Time communication in network-deficient areas, effectively surmounting communication hurdles posed by mountainous landscapes. Leveraging LoRa technology extends the communication range, guaranteeing uninterrupted connectivity and empowering bikers to traverse rugged terrains with heightened assurance and security. © 2024 IEEE.

关键词： Global positioning system

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Hardware Implementations of Dehazing Algorithms 18

Hardware Implementations of Dehazing Algorithms

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Akash, E. Ashokkumar, S.R. Banupriya, N. Subash Thangam, K. Sanjay Kumar, N. Jananeshwaran, M. Sri Eshwar College of Engineering Dept. Computer and Communication Engineering Coimbatore India

ISBN: (纸本)9789380544519

Haze, a phenomenon arising from the scattering of light by atmospheric particles, imparts undesirable visual artifacts and degradation to images, posing challenges across various applications. This review paper delves into the realm of dehazing algorithms, with a specific focus on their hardware implementations. The introductory section lays the groundwork by elucidating the nature of haze, its diverse types, and the detrimental impacts it inflicts on image quality through light scattering mechanisms. In response to the pressing need for real-time dehazing solutions, the paper navigates through the landscape of hardware-based dehazing algorithms This review addresses the intricate hurdles encountered in translating dehazing algorithms into hardware architectures. These challenges span architectural design considerations, power efficiency optimizations, memory bandwidth management, and algorithmic adaptation for parallel processing. To benchmark the advancements achieved thus far, the paper delves into a comparative analysis of diverse hardware implementations and their corresponding results. Notable architectural choices, from FPGA (Field-Programmable Gate Array) to specialized ASICs (Application-Specific Integrated Circuits) are dissected in terms of their performance metrics, energy efficiency, and suitability for real-time applications. This assessment also encompasses an examination of trade-offs between accuracy and computational speed in various hardware contexts. Drawing insights from the reviewed literature, this paper offers a comprehensive overview of the strides taken in the field of hardware-based dehazing algorithms. The synthesis of challenges, architectures, and comparative results provides a roadmap for future research directions, offering insights into potential areas of improvement and innovation. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： ASICs FPGA Hardware Implementation Haze Image Dehazing VLSI

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Enhancing Elderly Exergaming with AI Components for Assessing Cognitive Status 8

Enhancing Elderly Exergaming with AI Components for Assessin...

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8th IEEE International Forum on Research and Technologies for Society and Industry Innovation, RTSI 2024

作者： Danousis, Michail Goumopoulos, Christos University of the Aegean Information & Communication Systems Engineering Dept. Samos Greece

ISBN: (纸本)9798350362138

Maintaining physical and cognitive health is challenging for the older population, and thus creative ways to promote active lives are needed. The GAME2AWE platform is an exergaming platform that promotes the well-being of older people by utilizing serious games to encourage movement in a fun and engaging way. To improve the GAME2AWE experience, the platform's capabilities have been enhanced with AI components that provide user-aware functionalities. One of the GAME2AWE components is responsible for assessing the cognitive as well as the physical condition of the user playing in real-time. In this paper, we extend our investigation into this AI component by exploring the development of relevant models for assessing user cognitive status. In our machine learning (ML) training experiments, we extensively explored the area of ML hyperparameter tuning in order to achieve performance that is competitive with the standards set by related work. This study highlights the potential of AI to turn exergaming into a more effective tool for the well-being of the elderly by promoting exercise in a safe and controllable manner. © 2024 IEEE.

关键词： Adversarial machine learning

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Solar Energy based Smart Home Monitoring and Automation using IOT 4

Solar Energy based Smart Home Monitoring and Automation usin...

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4th International Conference on Power, Energy, Control and Transmission systems, ICPECTS 2024

作者： Dharmaraj, N. Adithyaa, A. Satyanarayanaa, H. Mouleesh, Y. Rupesh, S. Priyadharshini, K. Rajalakshmi Institute of Technology Dept. of Electronics and Communication Engineering Chennai India Rajalakshmi Institute of Technology Dept. of Computer and Communication Engineering Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

In the pursuit of sustainable living and energy efficiency, integrating renewable energy sources with smart home technologies has become increasingly important. This paper presents a novel system that utilizes solar energy as the primary power source for a smart home, combined with Internet of Things (IoT) technology for monitoring and automation. The primary goal of this project is to reduce waste in conventional ways by making extensive use of energy recovery sources, enhancing safety, and removing environmental impact. Optimizing the equipment's use of program energy in accordance with the actual time of solar power generation is one of the key goals. Another is enhancing the effectiveness of energy transfer through remote monitoring and control using data and smartphone applications. The integration of Internet of Things (IOT) technology with solar energy systems is critical to the sustainability, efficiency, and administration of residential surroundings. This article investigates the deployment capabilities of the Internet of Things (IOT) for solar household energy system automation and monitoring. Future house automation will rely on solar energy and Internet of Things monitoring technologies once this work is completed. © 2024 IEEE.

关键词： Smart homes

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Designing and Implementing an IoT-Based Long-Distance Fire-Extinguishment System 'Stationary 3-Axis Fire-Emergency Response Base' 8

Designing and Implementing an IoT-Based Long-Distance Fire-E...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kundu, Shyantan Dey, Swarajit Abdullha, Md. Mustafa Sen, Gobinda Gangopadhyay, Malay Institute of Engineering & Management Dept. of Computer Science & Engineering Kolkata India Institute of Engineering & Management Dept. of Electronics & Communication Engineering Kolkata India

ISBN: (纸本)9798331520762

This paper presents the design and implementation of an Internet of Thing (IOT)-based long-distance fire-extinguishment system named 'Stationary 3-Axis Fire-Emergency Response Base' (STAFERB). The system aims to provide immediate response to fire hazards in various environments, including residential areas, building complexes, commercial areas, small-scale industries, large factories, dense slum areas, and remotely located forest regions. The proposed STAFERB is designed to receive fire location coordinates, fire intensity, and probable cause information. It then calculates the path of a projectile based on considering obstacles and self-detection of a clear line of sight to reach the t1arget location. The system uses this information to select the appropriate speed and type of fire retardant. The designed STAFERB features robust communication, self-path detection, auto-orientation, and reduced response time, making it a significant advancement in fire-fighting technology. Experimental results demonstrate the effectiveness of the proposed STAFERB in mitigating fire hazards and hence can be well suited for real-time disaster management. © 2025 IEEE.

关键词： Fire hazards

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Towards a Real Vehicular Data Cloud 11

Towards a Real Vehicular Data Cloud

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11th IEEE International Conference on Software Defined systems, SDS 2024

作者： Cardel, Victor S. Zisner, Philipp Rettore, Paulo H. L. Sevenich, Peter Santos, Bruno P. Federal University of Bahia Dept. of Computer Science Brazil Fraunhofer FKIE Dept. of Communication Systems Bonn Germany

ISBN: (纸本)9798331518325

Intelligent Transportation systems (ITSs) increasingly rely on cloud computing to gather and process vehicular data, enabling the development of adaptable transportation solutions. However, the availability of such data is often limited by privacy, security, and commercial concerns, challenging the open research in ITS. Existing datasets rarely include both intra-vehicular data (created from or inside the vehicle) and extra-vehicular data (created from surrounding sources) or provide flexible scenarios for data extraction. This paper introduces a methodology to collect and publish ITS data, including intra and extra-vehicular sensor data. A quantitative analysis was conducted as part of the data characterization to show the associated data challenges. Moreover, the dataset collected is freely accessible, allowing innovation in ITS research and application development. © 2024 IEEE.

关键词： Big-data Data Fusion Intelligent Transportation systems Vehicular Cloud Computing

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Applying LSTM for River's Water Level Forecasting as Potential Flood Indication: A Case Study in Greece 15

Applying LSTM for River's Water Level Forecasting as Potenti...

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15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Tziolas, Theodoros Papageorgiou, Elpiniki Papageorgiou, Konstantinos Zardas, Georgios Lekidis, Alexios Panagiotis, Papazoglou University of Thessaly Dept. of Energy Systems Gaiopolis Campus Larisa41500 Greece University of Thessaly Dept. of Electrical and Computer Engineering Volos Greece National and Kapodistrian University of Athens Dept. of Architecture and Computer and Communication Simulation Systems Chalkida Greece

ISBN: (纸本)9798350368833

Floods can have significant life-threatening and economic impacts on human societies. Flood forecasting with data-driven methods is a crucial aspect of disaster risk management, providing valuable insights into potential flood events and allowing for timely mitigation measures. This work illustrates the application of a Long Short-Term Memory (LSTM) neural network for short-term forecasting of a river's water level. The water treatment data used for training the LSTM models were gathered from Sperchios river, located in central Greece. Historical time-series data in a half-hour interval of the river's water level, water velocity, and area precipitation were utilized for the development of the proposed scheme. The developed model is deployed in the backend of a web application for half-hour ahead predictions of the river's water level that are visualized in the frontend. The post-analysis of the deployed forecasting model indicates that it consistently delivers forecasts with minimal error and demonstrates high accuracy in recent flood events. © 2024 IEEE.

关键词： Rivers

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A Dual-Action Approach to Mosquito Control: Harnessing Mosquitoes Affinity for Light with Metofluthrin's Lethality 18

A Dual-Action Approach to Mosquito Control: Harnessing Mosqu...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Mittal, Manisha Singh, Mahender Dewan, Prachi Singh, Prabhmeet Dept. of Electronics and Communication Engineering New Delhi India Dept. of Computer Science and Engineering New Delhi India

ISBN: (纸本)9789380544519

Amidst rapid urbanization and fluctuating climatic patterns, the world witnessed a surge in mosquito-borne diseases, escalating global health concerns. Mosquitoes, often dismissed as mere irritants, are carriers for a spectrum of conditions, including malaria, dengue, and the Zika virus, with WHO reporting large numbers of dengue cases annually. This review delves deep into the observation of mosquito behavior and the challenges in eradicating these pests, emphasizing the inefficacy of singular sensory targeting repellents. This paper introduces a pioneering device, combining the mosquito's affinity towards light with the suffocating effects of metofluthrin, offering a holistic solution. By the combination of advanced engineering with a comprehensive understanding of mosquito behavioral dynamics, this device promises not only to repel but to substantially reduce these vectors, fostering safer indoor spaces. Through a detailed exploration of current methodologies and the introduction of this integrated strategy, this paper provides insights into creating a healthier, mosquito-free environment. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： fluorescent light metofluthrin mosquito control mosquito-borne diseases multifaceted sensory system

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Key Vertex Pairs Extraction for Graph Classification Based on Probability Distribution Learning 6

Key Vertex Pairs Extraction for Graph Classification Based o...

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6th International Conference on Artificial Intelligence in Information and communication, ICAIIC 2024

作者： Huang, Jianming Kasai, Hiroyuki WASEDA University Dept. of Computer Science and Communication Engineering Tokyo Japan

ISBN: (纸本)9798350344349

Graph classification is a hot topic of machine learning for graph-structured data, and it is also a very potential and valuable research. However, the difficulty of graph classification is challenging and special, which is quite different from the normal classification problems. One of the most difficult points of graph classification is that the numbers of vertex neighbors in graphs are usually variable, which makes the number of weights uncertain and ambiguous. Recent work such like the graph attention network apply the transformer on the graph neural network. However, the learned attentions cannot strictly reveal the importance of each part of graph, which makes the model less explainable. Moreover, for small datasets, it performs less effectively because of the excessive parameters. In order to overcome these difficulties, we propose a lightweight model with an edge weighting function based on the probability distributions of node pair features learned by the Gaussian mixture model. Although the proposed framework is simple, the experimental results shows its effectiveness on small datasets. © 2024 IEEE.

关键词： Graph neural networks

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