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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

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Breaking Through Color Casts: Enhancing image Fidelity with Machine Learning-Based Correction

Breaking Through Color Casts: Enhancing Image Fidelity with ...

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Intelligent systems for Cybersecurity (ISCS), International conference on

作者： Choppa Jeevan Sai Reddy Bavani Akshay Bange Bhawane Busireddy Parvathammagari Srikanth Reddy Addanki Satish Sree Vidyanikethan Engineering College Tirupati India Dept. of ECE School of Engineering Mohan Babu University Tirupati India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

L Color cast, an aberration common in digital images, poses challenges in various image processing applications, affecting image quality and visual perception. This research investigates diverse methodologies for color cast correction, ranging from traditional algorithms to modern machine learning-based approaches. Leveraging a comprehensive dataset of original and corrected images, the present study evaluates the efficacy of each method using quantitative metrics, including ACMO, BREN, GRAS, LAPM, LAPV, LAPD, and WAVV. Results indicate that while traditional techniques like Gray World Algorithm and White Patch Retinex Algorithm demonstrate moderate effectiveness, the implemented machine learning-based algorithm showcases superior performance across multiple color cast levels. By employing linear regression on RGB values, the method efficiently corrects color cast aberrations, yielding visually appealing and perceptually accurate results. Furthermore, the research highlights the significance of robust color constancy algorithms and their role in mitigating color cast distortions in digital images. This study contributes valuable insights into the field of color cast correction, offering practitioners in image processing and computer vision a comprehensive understanding of effective correction strategies. Future research directions may explore advanced machine learning models and integration with color constancy mechanisms to further enhance color cast correction techniques.

关键词： image quality Computer vision Machine learning algorithms Accuracy image color analysis Digital images Computational modeling

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Compact CNN-based road weather condition detection by grayscale image band for ADAS

Compact CNN-based road weather condition detection by graysc...

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conference on Autonomous systems - Sensors, processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure

作者： Abu-Alrub, Nader J. Abu-Shaqra, Albara D. Rawashdeh, Nathir A. Michigan Technol Univ Dept Elect & Comp Engn Houghton MI 49931 USA Deggendorf Inst Technol Fac Appl Nat Sci & Ind Engn Deggendorf Germany Michigan Technol Univ Dept Appl Comp Houghton MI 49931 USA

ISBN: (纸本)9781510651074;9781510651067

Sensors used in autonomous driving are affected variably in adverse weather conditions. The reliability of each sensor changes with different lighting, precipitation type and intensity, and visibility conditions. Common sensing modalities in autonomous driving include cameras and LiDARs, which have a high resolution signal, but degrade in poor weather conditions. Camera image contrast is also sensitive to changes in lighting. On the other hand, RADARs have a long range, but relatively low spatial resolution. Nonetheless, they are resilient to varying lighting and weather fluctuations. A compact weather detection system can be used to dynamically steer other algorithms used in autonomous vehicles (e.g., vehice control, object detection and tracking) toward the most reliable sensors. By adjusting the weights for different sensors, a fusion scheme can be made to change focus to the currently best performing sensor combination. Alternatively, a weather detection system could be used to switch between weather-specific models or ensemble algorithms. The idea of using multi-model algorithms for autonomous driving has been gaining popularity recently;a model trained in sunny conditions will likely underperform in non-ideal weather conditions. This paper presents a compact Convolutional Neural Network (CNN) that detects current driving weather conditions based on a narrow strip of the grayscale forward facing camera image. Standard multi-class classification metrics are used to assess the performance. The weighted average of the f1-score of all classes was 94%. The model was trained and tested on the RADIATE dataset, which contains multimodal sensor data of driving in different weather conditions including sunny, snow, overcast, fog, and rain.

关键词： Autonomous driving CNN Adverse weather Road surface condition camera ADAS

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Vgg-16 and Vgg-19 Architecture Models in Lie Detection Using image processing 6

Vgg-16 and Vgg-19 Architecture Models in Lie Detection Using...

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6th International conference on Information Technology, Information systems and Electrical Engineering, ICITISEE 2022

作者： Kusumawati, Dewi Ilham, Amil Ahmad Achmad, Andani Nurtanio, Ingrid Hasanuddin of University Department of Electrical Engineering South Sulawesi Gowa Indonesia Hasanuddin of University Department of Informatics South Sulawesi Gowa Indonesia

ISBN: (纸本)9798350399615

The polygraph, LVA, and VSA are three techniques used to find lying. However, the polygraph monitors the physiological reactions that have been linked to lying rather than detecting lies. Validity and reliability testing for VSA and LVA has been conducted, but the results are not reliable enough and unsatisfactory. How to find a lie detection model using the CNN approach is the goal of this research. With the addition of an augmentation process to image processing, dropouts, and three optimization algorithms-Adam, RMSprop, and Adadelta on the two CNN architecture models with VGG-16 and VGG-19 we use. For the dataset this study used a video-based public dataset, the video was converted into an image format, and a confusion matrix was used to analyze the data during validation. The study found that the accuracy achieved from the VGG-16 model that is, Adam + VGG-16 has the best models were obtained from 18 combinations combining the VGG-16 and VGG-19 models along with other optimizations. © 2022 IEEE.

关键词： Adadelta Adam Classification CNN Detection RMSProp VGG-16 VGG-19

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Comprehensive AI-Powered Healthcare Management System

Comprehensive AI-Powered Healthcare Management System

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2025 International conference on Intelligent Computing and Control systems, ICICCS 2025

作者： Devananda Rao, B. Madhukar, Gadde Reddy, Bankula Nithin Voni, Sriyan Kumar Snehith Venkata Kumar, N. MLR Institue of Technology Department of CSE Hyderabad India

ISBN: (纸本)9798331512088

This is the Comprehensive AI-Powered Healthcare Management System intended to revolutionize healthcare delivery, overcoming the shortcomings of present systems by integrating multiple sources of information to predict disease onset and administering personalized care, all based on advanced technologies such as Artificial Intelligence and Machine Learning integrated into Blockchain. The system will have early disease detection and tailor-made treatment plans and holistic patient care. The key algorithms include Random Forest, Support Vector Machine, and Neural Networks. It will deploy Convolutional Neural Networks for the analysis of medical images and Natural Language processing techniques through the application of transformer models such as BERT. Key Technologies to be used are PyTorch, TensorFlow, DialogFlow, and Ethereum. This project shall be developed in phases starting from collecting and integrating diverse health data. Expected output is a fully functional healthcare management platform for the enhancement of patient outcomes, facilitation of greater efficiency by health providers, and secure health data management. These diverse applications have functionalities in improving diagnostic accuracy and patient management in clinics, remote monitoring of patients with chronic diseases, prediction of mental health crisis incidents, and safe storage of patient data through blockchain integration. © 2025 IEEE.

关键词： Analytics in Healthcare Artificial Intelligence in Healthcare Blockchain in Healthcare Data Security and Privacy in Healthcare Healthcare Informatics Machine Learning for Disease Prediction Medical image Analysis Predictive

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Performance Comparison of Machine Learning algorithms for Online Handwriting Recognition: A Brief Analysis

Performance Comparison of Machine Learning Algorithms for On...

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Sustainable Expert systems (ICSES), International conference on

作者： Mamta Gurpreet Singh Punit Soni Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

An online handwriting recognition system is a software application that enables the conversion of handwritten text or symbols into digital form. This system utilizes various technologies such as the processing of images, recognition of patterns, the context of machine learning, and computational intelligence to interpret handwritten content accurately. In this system, users input their handwriting through a stylus or mouse, and the software recognizes information and converts it into machine-readable text. The system can be used for a wide range of applications, including the processing of documents, note-taking, and signature verification that impacts the online handwriting system. The accuracy and efficiency of online handwriting recognition systems have improved due to technological breakthroughs in research development, making them a popular choice for various industrial innovation.

关键词： Handwriting recognition Technological innovation Accuracy Machine learning algorithms image recognition Text recognition Symbols Machine learning Software Expert systems

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image Captioning System for Movie Subtitling Using Neural Networks and LSTM 3rd

Image Captioning System for Movie Subtitling Using Neural Ne...

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3rd International conference on Computing and Communication Networks, ICCCN 2023

作者： Vijay, K. Manohar, Eashaan Saiganesh, B. Sanjai, S. Deepak, S.R. Department of Computer Science and Engineering Rajalakshmi Engineering College Tamilnadu Chennai India Department of Electronics and Communication Engineering Rajalakshmi Engineering College Tamilnadu Chennai India

ISBN: (纸本)9789819708918

With the advent of the Internet, the multimedia business has experienced explosive growth and is now available to consumers worldwide. Particularly important to this growth has been the dominance of over-the-top (OTT) platforms during the COVID-19 epidemic. One important component in this growth has been the implementation of state-of-the-art Machine Learning (ML) methods. These algorithms can generate captions from video frames mechanically, increasing the platform's accessibility. However, it can be difficult to meet the needs of users speaking different languages, as there are many films being streamed in languages that may be inaccessible to consumers in other parts of the world. There is an issue with English being the most spoken language in the world. Therefore, individuals who aren't fluent in English may have trouble finding videos in their native language. By automatically creating English subtitles for any movie, regardless of the language spoken on the original audio track, machine learning plays a significant role in removing this barrier. Neural Networks, used for processing each frame of the video, and LSTM models, used for caption synthesis, are the key models employed here from the realm of machine learning. After the models have been trained, they can be incorporated into the user interface (UI) using a programming language like Python. In the user interface, the created caption can be shown alongside the uploaded image. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： User interfaces

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5th International conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

5th International Conference on Advances in Distributed Comp...

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5th International conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

ISBN: (纸本)9789819735228

The proceedings contain 35 papers. The special focus in this conference is on Advances in Distributed Computing and Machine Learning. The topics include: Computer-Aided Bundle Branch Block Detection Using Symbolic Features of ECG Signal;block-Chain and Cloud-Based Tender Allocation System;reliability Assessment of IoT-Enabled systems Using Fault Trees and Bayesian Networks;resnet-50 Integrated with Attention Mechanism for Remote Sensing Classification;revolutionizing Data Annotation with Convergence of Deep Learning and Active Learning to Enhance Credibility on Twitter Datasets;statistical and Deep-Learning Approaches for Individual Carbon Footprint Calculation in India;intelligent Healthcare System Using Emerging Technologies: A Comprehensive Survey;Efficient Energy Management by Using SJF Scheduling in Wireless Sensor Network;cost Effective and Energy Efficient Drip Irrigation System for IoT Enabled Smart Agriculture;facial Detection and Recognition in Drone imagery Using FaceNet;valluvan: processing Name Board images to Enhance Communication for Native Tamil Speakers;Optimized VM Migration for Energy and Cost Reduction Using TSO Algorithm in Cloud Computing;A Study on the Mental Health Among Indian Population in the Post COVID-19 Pandemic Using Computational Intelligence;Cloud-Based Anomaly Detection for Broken Rail Track Using LSTM Autoencoders and Cross-modal Audio Analysis;Face Recognition Using CNN for Monitoring and Surveillance of Neurological Disorder Patients;a Novel Approach to Breast Cancer Histopathological image Classification Using Cross-colour Space Feature Fusion and Quantum–Classical Stack Ensemble Method;comparative Analysis of Deep Learning-Based Hybrid algorithms for Liver Disease Prediction;preface;performance Improvements of Covert Timing Channel Detection in the Era of Artificial Intelligence;sign Detection Using an N-Gram Language Model and MobileNet.

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Research in image processing for Pipeline Crack Detection Applications

Research in Image Processing for Pipeline Crack Detection Ap...

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2022 International conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Altabey, Wael A. Kouritem, Sallam A. Abouheaf, Mohammed I. Nahas, Nabil Department of Mechanical Engineering Faculty of Engineering Alexandria University Alexandria21544 Egypt Mechatronics Engineering Technology Bowling Green State University Bowling GreenOH43402 United States Université de Moncton Faculté d'Administration MonctonNBE1A 3E9 Canada

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Pipelines, such as gas and utility pipeline systems and networks, are some of the most critical components of civil infrastructure. During the long-term operation of the pipeline, various types of diseases will occur. Pipeline damage may present serious environmental and economical problems. In order to solve the problem that traditional pipelines crack detection, a pipelines crack detection method based on image processing under complex background is proposed. A pipeline crack image segmentation model based on semantic segmentation is built, and cracks in high-resolution crack images are extracted by using the pipeline image segmentation model. The accuracy rate (P%), recall rate (R%), and F-score (F%) of the proposed method are recorded 89.3%,85.7%, and 80.4%, respectively. The results show that, compared with the existing algorithms, the proposed algorithm has a better detection effect and stronger generalization ability in complex pipeline scenes. © 2022 IEEE.

关键词： Semantic Segmentation

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Application of color image encryption algorithm based on logistic sine cosine mapping in image processing

Application of color image encryption algorithm based on log...

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2022 International conference on algorithms, Microchips and Network Applications

作者： Zhang, Jian School of Information Engineering Liaoning Institute of Science and Engineering Jinzhou China

ISBN: (纸本)9781510653290

The rapid development of science and technology has brought convenience for photographing and sharing images. However, with the rapid increase of image data, security problems such as leakage and tampering also occur frequently. The application of image encryption technology is imminent, especially the encryption of color images is in urgent need of improvement and development. The traditional encryption technology is mainly aimed at data stream encryption, which has low efficiency, large amount of calculation and certain limitations. Based on transform domain encryption, the image is transformed from spatial domain to frequency domain for encryption, and then transformed to spatial domain, which belongs to a kind of lossy encryption. Encryption based on chaos has large key space, simple implementation and fast encryption speed. Generally, multiple chaotic systems need to be used to enhance the security of encryption. Therefore, a simple and secure spatial encryption algorithm for RGB three channels of color image is designed in this paper. Firstly, a safer chaotic sequence is generated by logistic sine cosine mapping, and then four rounds of scrambling and diffusion pixels are designed by chaotic sequence. Finally, In a series of security analysis experiments, the security and effectiveness of color image encryption algorithm based on logistic sine cosine mapping are verified. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Mapping

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Controlling a Swarm of Unmanned Aerial Vehicles Using Full-Body k-Nearest Neighbor Based Action Classifier

Controlling a Swarm of Unmanned Aerial Vehicles Using Full-B...

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International conference on Unmanned Aircraft systems (ICUAS)

作者： Chaudhary, Akash Nascimento, Tiago Saska, Martin Czech Tech Univ Fac Elect Engn Tech 2 Prague 16627 Czech Republic Univ Fed Paraiba Joao Pessoa Paraiba Brazil

ISBN: (数字)9781665405935

ISBN: (纸本)9781665405935

The intuitive control of robot swarms becomes crucial when humans are working in close proximity with the swarm in unknown environments. In such operations, it is necessary to maintain the autonomy of the swarm while giving the human operator enough means to influence the decision-making process of the robots. This paper presents a human-swarm interaction approach using full-body action recognition to control an autonomous flock of unmanned aerial vehicles. We estimate the full-body pose of the human operator and use a k-nearest neighbor algorithm to classify the action made by the humans. Finally, the swarm uses the identified action to decide its goal direction. We demonstrate the practicality of our approach with a multi-stage experimental setup to evaluate the prediction accuracy and robustness of the system.

关键词： image processing Decision making Lighting Autonomous aerial vehicles Prediction algorithms Robustness Real-time systems

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