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A Novel Approach to Hand Gestures recognition Using Optimized Quantum GAN and ResNet-152 Features 4

A Novel Approach to Hand Gestures Recognition Using Optimize...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Kaliappan, Seeniappan Muthukannan, M. Devi, N. Durga Irfan, M.M. Maranan, R. Ramya, M. Kcg College of Technology Department of Mechanical Engineering Karapakkam Tamil Nadu Chennai600097 India Kcg College of Technology Department of Civil Engineering Karapakkam Chennai600 097 India Aditya University Department of Computer Science and Engineering Andhra Pradesh Surampalem India Sr University Department of Electrical and Electronics Engineering Telangana Warangal506371 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India Lovely Professional University Division of Research and Development Punjab Phagwara144411 India

ISBN: (纸本)9798331518981

the creation of hand gesture recognition approach, such as sign language applications, is crucial to bridging the communication gap with non-sign language users. this work proposes a novel approach to hand gesture detection with improved efficiency and accuracy through the use of sophisticated pre-processing, feature extraction, and classification algorithms. the suggested approach presents a robust image pre-processing technique called Window-aware guided image filtering, which smooths gesture images while maintaining edge details that are essential for feature extraction. After that, high-level discriminative features are extracted from gesture photos using ResNet-152, a deep convolutional neural network. In order to improve gesture classification even more, a Quantum Generative Adversarial Network (QGAN) is included. this network makes use of quantum computing to accelerate convergence and improve learning effectiveness. Furthermore, the Zebra Optimization (ZO) is utilized to optimize the QGAN model by fine-tuning the network parameters for enhanced recognition performance. this hybrid approach achieves superior accuracy, robustness, and speed, outperforming traditional hand gesture recognition systems. the presented approach gained the efficiency of around 99% for all recognition metrics. © 2024 IEEE.

关键词： Quantum computers

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Interaction with Media Players through Hand Gesture recognition 4

Interaction with Media Players through Hand Gesture Recognit...

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4th international conference on Communication Technologies, ComTech 2023

作者： Saleem, Maryam Yousaf, Zainab Arshad, Iqra Abubaker, Maria Arisha Tooba Rana, Tauseef MCS Department of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350337211

Hand devices provides a huge barrier between convenient and user friendly Human-computer Interaction (HCI). there is a strong need for marker-less natural interface between human and computer that can also facilitate disabled people. this paper presents a novel technique for hand gesture recognition for controlling multimedia player through hand gestures. Existing techniques available in the literature have number of limitations, such as scalability, complex implementations and limited accuracy. the solution proposed in this paper is based on image processing which does not require designing of any complicated data sensors-based gloves. Our outcome shows that an effective HCI can be achieved with minimal hardware requirement. Furthermore, the proposed system is easily scalable for controlling many other computer applications through hand gestures. © 2023 IEEE.

关键词： Human computer interaction

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CAMFNet: A Context-Aware Multiscale Fusion Network for Enhanced Multispectral image Fusion in Remote Sensing and Security 4

CAMFNet: A Context-Aware Multiscale Fusion Network for Enhan...

引用

4th international conference on Multimedia processing, Communication and Information Technology, MPCIT 2024

作者： Suneel, J.S. Dileep, M.R. Visvesvaraya Technological University Nitte Meenakshi Institute of Technology Department of Master of Computer Applications Bengaluru India Nitte Meenakshi Institute of Technology Department of Master of Computer Applications Bengaluru India

ISBN: (数字)9798350375466

ISBN: (纸本)9798350375466

this research is mainly concerned with enhancing the object recognition and classification of multispectral imagery imagery especially of people and vehicles in the heavily vegetated regions. Sub-technology such as PCA and WT have inherent limit with regards to the ability to preserve both spatial and spectral properties. To fill the above-mentioned gaps, this work presents a CAMFNet;a framework that integrates multiscale fusion together with Visible (VIS), Near-Infrared (NIR) and thermal Infrared (TIR) data for fusion and feature extraction and classification. CAMFNet uses classifiers like Gradient Tree Boost and SVM for differentiating people, cars, trees and other such objects. Feature extraction is carried out using PCA and HOG for enhancing the methods prior to the extraction. One of the radical innovations is the employment of certain aspects in CAMFNet, including multiscale feature extraction and attention mechanisms for boosted data fusion. the framework also involves generative aerial networks (GANs) and it is capable of preserving important spatial and spatial details profiles thereby making it to be more accurate, to more recalls and has low false positives compared to normal methods. Experiments made on real-world datasets coming from different landscapes, revealed that the CAMFNet outperformed the other networks in the classification metrics, as well as in PSNR and SSIM. Surveillance, agriculture, and the military are some of its uses to emphasize work in large-scale and distant investigations. It pushes the state-of-art in multispectral image fusion with this strong, scalable, real-time method for nonmilitary and military domains for large-scale objects recognition. © 2024 IEEE.

关键词： image fusion

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Autonomous Social Amenity, Pothole Detection and Reporting 4

Autonomous Social Amenity, Pothole Detection and Reporting

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4th international Multidisciplinary Information Technology and Engineering conference, IMITEC 2024

作者： Mosela, David Olaifa, Moses Owolawi, Pius Malele, Vusi Tshwane University of Technology Dept. Computer Systems Engineering Tshwane South Africa Tshwane University of Technology Dept. of Computer Systems Engineering Tshwane South Africa

ISBN: (纸本)9798350387988

Challenges faced by traffic management in many developing nations across the globe is a result of the poor quality of road infrastructure. Inadequate and defective road infrastructure are major contributors to this effect. A key element that poses significant risk to road safety and that leads to increased road management cost is the increasing number of potholes and inefficient detection and reporting approach. this study provides an in-depth exploration of the latest developments in pothole detection systems and examines various methodologies and technologies used in these systems, emphasizing the importance of real-time monitoring for proactive road maintenance. In addition, we proposed a system that addresses the persistent challenge of road infrastructure degradation by adopting cutting-edge technologies, including computer vision, machine learning, and sensor networks (such as ultrasonic and GPIO systems) that are used to identify and assess potholes on road surfaces. To achieve the aim of the study, the system adopts the use of RasberryPi 4 Model B for pothole detection and YOLOv4 for image processing. © 2024 IEEE.

关键词： Highway administration

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Sag measurement method of overhead transmission line based on image processing technology 4

Sag measurement method of overhead transmission line based o...

引用

4th international conference on Mechanical, Electronics, and Electrical and Automation Control, METMS 2024

作者： Chen, Zhen Xu, Naiwen Liu, Gang Chang, Hao Qu, Honglei Emergency Rescue Center of State Grid Liaoning Electric Power Supply Co. Ltd. Liaoning Shenyang110021 China Liaoning Power Transmission and Transformation Engineering Co. Ltd. Liaoning Shenyang110021 China

ISBN: (数字)9781510679887

ISBN: (纸本)9781510679870

Transmission line sag is an important index of safe and stable operation in the process of transmission line pay-off, and real-time monitoring has become an important research topic at home and abroad. In the process of pay-off, the tractive force and the change of pay-off environment may lead to the sag change of the transmission line. If the sag is too small, the tension of the wire will increase and the wire will break easily. If the sag is too large, the wire will dance easily. For the sag of overhead transmission line in the process of pay-off, how to measure the result quickly and accurately is an important problem. Based on computer vision technology and image processing technology, the feasibility of the scheme is determined by measuring and calculating the traverse sag of overhead transmission line. © 2024 SPIE.

关键词： computer vision

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Culinary Fusion:Vegetable recognition Recipe Generation 4

Culinary Fusion:Vegetable Recognition Recipe Generation

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4th IEEE international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Kanchi, Lohitha Lakshmi Vishnu, Kanduri Purna Venkata Siva Sai Vara Eswar, Kasarabada Bharath Harshitha, Lakkakula Sai Eswar, Maddineni Vasireddy Venkatadri Institute of Technology Department of Computer Science and Engineering Guntur India

ISBN: (纸本)9798350360165

the presented paper aims to propose a novel way to implement advanced deep learning and natural language processing in vegetable recognition and recipe generation. the proposed work uses the YOLOv8 object detection framework, which is first trained on a very meticulously obtained dataset of 20, 708 images from the website Roboflow, to perform intelligent partitioning into three sets: Finally, the proposed methodology has attained 88% accuracy in predicting different vegetables. On detecting vegetables in any input image, the model uses OpenCV to detect those vegetables from the PALM API. It takes the names of the detected vegetables as prompts along with the preferences selected by the user in terms of the language and the number of recipes and gives the output recipe using the PALM API in the form of five different language translations, Telugu, Malayalam, Hindi, Kannada, and Tamil. the output recipes are also provided in text and audio format to cater to users of all linguistic backgrounds. this results in a holistic system that ensures a hassle-free and user-friendly approach to vegetable recognition, its recipe generation, and, finally, the enrichment of the culinary experience and promotion of cultural diversity, even in the kitchen. © 2024 IEEE.

关键词： Food ingredients

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An Empirical Review of Dark Web Data Classification Methods Using NLP, SVM, CNN, and GAN 4

An Empirical Review of Dark Web Data Classification Methods ...

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4th international conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Dhande, Yogita H. Zade, Amol Patil, Sonal P. Ghru Amravati Computer Science & Engineering India Ghrcem Computer Science & Engineering Jalgaon India

ISBN: (纸本)9798350389128

the requirement for effective data classification on the Dark Web has increased following the rising sophistication and spread of illicit operations over this secretive internet segment. this paper systematically reviews and compares the state-of-the-art Dark Web data classification methods that fall into text-based, image-based, and hybrid approaches. In the review process, this research outlines their strengths, weaknesses, and common challenges while also identifying gaps. the text-based approaches made extensive use of NLP, machine learning algorithms (such as SVM), and deep learning models, most notably, RNN and CNN. these techniques are good at information processing and understanding textual content, but high variability and obfuscation tactics make the communications hard to understand most of the time. image-based classification leverages state-of-the-art computer vision techniques, for instance, CNN and GAN. these models are effective at detecting and classifying illicit images, such as goods or services that are illegal. However, it faces problems with the low quality or morphed images that are common on the Dark Web. Hybrid approaches incorporate both text and image in the analysis so that the information provided on the Dark Web is considered in an integrated form. these models use multimodal deep learning, which involves the use of CNN for image data and RNN for textual data samples. Hybrid methods combine text and image analysis to provide a more comprehensive understanding of Dark Web data samples. the approaches integrate multimodal deep learning models where CNNs are used for image data and RNNs for text data samples. Hybrid models show promise in improving classification accuracy by capturing the contextual information from both text and image samples. the present work outlines the necessity for developing highly adaptive and robust classification methods for Dark Web data samples. By assessing the strengths and limitations of existing methods, th

关键词： CNN Dark Web Data Classification Hybrid Methods NLP

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A vision-Based Counter System for Children’s Dribble 23

A Vision-Based Counter System for Children’s Dribble

引用

9th international conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Jingxin, Liang Chengqi, Liang Xufeng, Ling Jie, Yang Shanghai Normal University Tianhua College AI School No. 1661 North Sheng Xin Road *** China Gaozhou Middle School Guangdong Province Gaozhou China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University 800 Dongchuan Road 200240 China

ISBN: (纸本)9781450399029

In this paper, a vision-based counter system for children’s dribble based on Google MediaPipe’s human posture recognition algorithm and YOLOv5 object recognition algorithm is introduced. Firstly, the hands’ coordinates of the child is located using MediaPipe, and the coordinates of the ball is found using YOLOv5;by the relative positions of the hands and the ball in the multi-frame images of the video, an action sequence of ball holding, touching and ball bouncing is recognized and used to count the number of dribble. the experimental results show that the system can count a single child’s dribble accurately. © 2023 Copyright held by the owner/author(s).

关键词： computer vision

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Face Makeup Transfer Algorithm Based on CycleGAN 4

Face Makeup Transfer Algorithm Based on CycleGAN

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4th international Signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Gu, Yunjin Xu, Zihan Zhang, Dengyu International School Beijing University of Posts and Telecommunications Beijing China School of Information University of Sheffield Sheffield United Kingdom International School Zhejiang Gongshang University Hangzhou China

ISBN: (纸本)9798331528676

Face makeup transfer is a popular research direction in the domain of computer vision, where makeup style is considered a special style of facial images, and makeup transfer is a part of style transfer. the process of human face makeup transfer is to transfer the makeup of the reference makeup image to the original makeup image while keeping the facial characteristics of the original makeup image. Currently, Generative Adversarial Networks (GANs) have become the mainstream technology for solving the problem of face makeup transfer. Face makeup transfer will be widely used in the future, such as in beauty cameras, makeup software for one-click makeup, virtual makeup trials for cosmetics, and video makeup transfer (such as live streaming software makeup and TV drama character makeup), and has received widespread attention in recent years. It has significant research value both in academia and industry. In this study, the paper adopted a series of innovative strategies to optimize model performance, including the introduction of non-local attention mechanisms to enhance feature extraction capabilities, improved residual blocks to increase network depth and efficiency, and attention modules in discriminators to improve focus on key features. Additionally, the paper used multi-scale discriminators to capture feature information at different levels and added skip connections to promote gradient flow and information integration, thereby achieving more efficient network training and better performance. ©2024 IEEE.

关键词： Discriminators

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Human Action recognition Using Mediapipe Holistic Keypoints: A Deep Learning Approach 4th

Human Action Recognition Using Mediapipe Holistic Keypoints:...

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4th international conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Shandilya, Utkarsh Sharma, Vijeta Mishra, Deepti Cent Univ Haryana Dept Comp Sci & Engn Jaat Haryana India NTNU Norwegian Univ Sci & Technol Educ Technol Lab Dept Comp Sci IDI N-2815 Gjovik Norway

ISBN: (纸本)9783031837951;9783031837968

Human action recognition (HAR) is a crucial field in computer vision with applications ranging from video surveillance to human-computer interaction. this study explores an efficient framework for HAR by leveraging human keypoint extraction using Google's Mediapipe and Long Short-Term Memory (LSTM) networks. the Mediapipe framework provides accurate and real-time human keypoint extraction, significantly reducing the computational complexity associated with raw video data processing. these keypoints, representing skeletal movements, are utilised as input to LSTM networks, which capture the temporal dependencies vital for action classification. the proposed method is evaluated on two benchmark datasets: UCF101 and Kinetics 400. UCF101 contains 13,320 video clips across 101 action classes, while Kinetics 400 features 400 human action categories. the combination of Mediapipe for feature extraction and LSTM for temporal modelling achieves 92.40% and 86.8% accuracy on UCF101 and Kinetics400, respectively. the results demonstrate the effectiveness of keypoint-based approaches for HAR. this study highlights the potential of lightweight, real-time human action recognition frameworks suitable for resource-constrained environments, especially for edgeAI and edge Robotics.

关键词： Human action recognition human-computer interaction keypoint extraction LSTM Mediapipe

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