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

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Smart Medical Box using Arduino

Smart Medical Box using Arduino

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International conference on ent Computing and Control systems (ICICCS)

作者： Sweadha. M M. Selvi Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The Smart Medical Box is a sophisticated IoT-driven solution designed to enhance medication adherence by utilizing computer vision and machine learning. The system automates the identification and extraction of essential information from medication labels, ensuring precision in dosage reminders and reducing human error. It incorporates a high-resolution camera, an OCR engine, and an Arduino-based microcontroller to enable seamless image processing and real-time analysis. With an intuitive interface and GPS-enabled location services, users can efficiently manage their medication schedules while accessing nearby pharmacies when needed. This research focuses on designing and implementing a scalable and cost-effective system that improves medication compliance and minimizes health risks associated with missed or incorrect dosages. By integrating advanced image processing techniques and AI-driven recognition algorithms, the Smart Medical Box ensures high accuracy in identifying medications. Additionally, features such as automated alerts, real-time monitoring, and pharmacy locator tools enhance the system's usability and accessibility. This innovation is particularly beneficial for elderly individuals and patients with chronic conditions, offering a simplified and automated approach to medication tracking. Performance evaluations of the system indicate a significant improvement in medication adherence, a high recognition accuracy rate, and positive user feedback. Future enhancements, including AI-based OCR, voice assistance, and mobile application connectivity, are expected to further improve functionality and ease of use. This paper presents the conceptualization, development, and testing of the Smart Medical Box, highlighting its potential to revolutionize personal healthcare management by providing an intelligent, efficient, and user-friendly solution.

关键词： Accuracy image recognition Microcontrollers Optical character recognition Medical services Real-time systems Usability Older adults Biomedical imaging Global Positioning System

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AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool for Non-Invasive Medical Diagnostics

AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool fo...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Aniruddha Ghosh Sayan Snigdha Pal Sumedha Das Spandan Bhattacharya Arijit Bhattacharya Nilanjana De Sudipta Sahana Computer Science and Engineering(Artificial Intelligence and Machine Learning) Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata West Bengal India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Thermography is an innovative technique that creates a heatmap of different portions of the human body by using infrared (IR) radiation where color scales vary for various temperature regions. This work's scope aims to extend thermal imaging applications with machine learning algorithms in conjunction with Azure API services to analyze thermal images and return a preliminary diagnosis. The project consists of generating heatmaps for better visualization results, analyzing them through image processing techniques, and feeding the information to Azure’s Custom Vision API to detect anomalies and classify health conditions and finally producing a proper report. This research allows individuals and healthcare professionals to have a quick, non-invasive diagnostic tool that empowers health monitoring and preventive care. It combines the capabilities of contemporary technologies with uses directed toward health practice, leading to inimical patient outcomes and true practices.

关键词： Visualization Machine learning algorithms image color analysis Infrared heating Infrared imaging Thermal analysis Medical diagnosis Predictive analytics Intelligent systems Monitoring

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AI-Driven Personalized Skincare Recommendations

AI-Driven Personalized Skincare Recommendations

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Electronics and Renewable systems (ICEARS), International conference on

作者： Iyyanar Perumal Kalaivani P Naveenkumar A Saran V Sriram K Suruthi N Department of Information Technology Sona College of Technology Salem India Department of CSE Kongu Engineering College Perundurai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This study investigates the utilization of artificial intelligence (AI) to transform skincare product recommendations and the formulation of individualized routines through the analysis of user inputs and characteristics. The system utilizes data including skin type, acne count, oiliness level, and UV exposure, using KNearest Neighbors (KNN) for personalized product suggestions. Natural Language processing (NLP) approaches are utilized to develop tailored 7-day skincare regimen recommendations by examining product and user problems, converting unstructured data into actionable insights. Moreover, deep learning algorithms are employed for real-time image-based assessments of skin conditions, facilitating precise acne diagnosis and oiliness evaluation. The system integrates visual data with textual analysis, continually adapting through user feedback to improve accuracy and relevance. This article highlights the capacity of AI-driven methods to automate routine generation and provide tailored product recommendations, fostering innovation in the cosmetics sector through a thorough, data-oriented strategy.

关键词： Deep learning Visualization Accuracy Transforms Nearest neighbor methods Skin Natural language processing Real-time systems Artificial intelligence Wearable devices

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Perceptual Learning Technology Based on Computer Vision and Machine Learning algorithms

Perceptual Learning Technology Based on Computer Vision and ...

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International conference on Communication systems and Network Technologies (CSNT)

作者： Tianjian Wang School of Computer Science and Technology Wuhan Qingchuan University Wuhan China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

To address the issue of low accuracy and poor robustness of perceptual learning in complex scenarios, a new method integrating computer vision and machine learning is adopted, that is, by applying deep neural networks, transfer learning and self-supervised learning, combined with multimodal data fusion strategy, to improve target recognition efficiency and learning ability. First, based on the preprocessing algorithm of improved Gaussian filtering and image segmentation, the quality of image features is improved. Data enhancement methods such as random rotation, flipping, and blurring are used to expand data distribution and improve model adaptability. Secondly, a convolutional neural network (CNN) is utilized in combination with an attention mechanism to extract multi-scale target features, and transfer learning is applied to transfer common features from pre-trained models to reduce dependence on large-scale labeled data. Finally, a contrastive learning framework is constructed to mine the correlation of unlabeled data. Transformer is used to realize the fusion of multimodal data of images and texts, and the model performance is optimized through multi-task learning. The mAP (Mean Average Precision) of the traditional method in dynamic occlusion scenarios is 60.5%, which is relatively weak. This may be because the traditional method cannot fully extract the effective features of the target under the occlusion of complex moving targets. The mAP of the method in this paper is 72.8%, which is higher than that of the traditional method. The perceptual learning method adopted in this paper effectively improves the accuracy and robustness in complex scenarios, and provides reliable technical support for intelligent applications. In the future, it can be combined with edge computing to further optimize the real-time processing capability and promote its application in the fields of unmanned driving and intelligent security.

关键词： Adaptation models Computer vision image segmentation Machine learning algorithms Computational modeling Transfer learning Feature extraction Data models Robustness Real-time systems

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Algorithm Optimization and Immersion Enhancement in Virtual Reality Film

Algorithm Optimization and Immersion Enhancement in Virtual ...

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Intelligent systems and Computational Networks (ICISCN), International conference on

作者： Chulei Zhang Hongxing Qian Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

With the widespread application of virtual reality (VR) technology in the fields of entertainment and education, the immersion experienced by users in VR movies and TV shows has become a key factor affecting their acceptance and satisfaction. However, there are still significant shortcomings in terms of image rendering speed in current VR movies and TV shows. This article introduces an algorithm optimization framework based on Convolutional Neural Networks (CNN). Firstly, this article uses CNN algorithm to optimize the image rendering process. Next, use Dynamic Resolution Adjustment (DRS) technology to improve rendering speed and maintain frame rate stability; Then, combining high dynamic range (HDR) rendering and temporal anti aliasing (TAA) technology to enhance the color expression and visual smoothness of the image; Finally, optimize the sensor data processing flow and data transmission protocol. Through experimental evaluation in VR scenes of different complexities, the results showed that the optimized system improved rendering speed by an average of about 58.3%, reduced interaction latency by about 33.3%, and improved image quality indicators PSNR and SSIM by about 26.2% and 9%, respectively. In the above data conclusions, CNN based optimization methods have shown significant advantages in improving VR film and television performance and user experience, providing strong support for the future development of VR film and television technology.

关键词： image quality Visualization TV Heuristic algorithms Virtual reality Motion pictures Rendering (computer graphics) User experience Convolutional neural networks Optimization

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Rectifying Irregular Boundaries Stereoscopic images Using Deep Learning and Evolutionary algorithms

Rectifying Irregular Boundaries Stereoscopic Images Using De...

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IEEE SSD International Multi-conference on systems, Signals and Devices

作者： Muntasser A. Wahsh Zainab M. Hussain Department of Computer Science Informatics Institute for Postgraduate Studies (IIPS) Baghdad Iraq Department of Political Science Al-Iraqia University Baghdad Iraq

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Application of stereoscopic technology has increased significantly in several industries, including the media and entertainment sectors, medical imaging, and virtual reality. However, the overall viewing experience and the feeling of depth can be negatively impacted by uneven boundaries in stereoscopic images. To have a realistic and immersive stereoscopic experience, fixing these wavy edges is an essential process. This work present a distinctive approach for optimizing the rectangular bounds of stereoscopic images using a hybrid of deep learning and evolutionary algorithms. The suggested method uses a genetic algorithm to optimize the parameters of a convolutional neural network (CNN) to extract useful features for residual regression. Due to the lack of compatible datasets, a set of stereoscopic images was constructed from monocular images by calculating the depth of the objects using MiDaS pretrained depth estimation algorithm. Several metrics, including as mean squared error, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Fréchet inception distance (FID), were used to evaluate the performance of the suggested technique. The experimental findings show that the proposed method effectively optimizes rectangular borders with excellent accuracy, even for images with complicated shape and orientations. Various parameter settings were also tested, revealing that increasing the number of generations and population size contributes to improved performance. The combination of the deep learning algorithm, specifically the CNN, and the optimization algorithm, specifically the genetic algorithm (GA), leads to an increase in the accuracy of rectified images. Notably, the average PSNR reached 24.42, SSIM measured at 0.7793, and FID scored 18.26. The proposed method holds promise for a wide range of applications, including image cropping, object detection, and image segmentation, among others. By effectively optimizing rectangular boundaries in ster

关键词： Deep learning Visualization Accuracy PSNR Stereo image processing Depth measurement Evolutionary computation Feature extraction Convolutional neural networks Genetic algorithms

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Online Optimization of Offloading Video Analytics Tasks to Multiple Edges for Accuracy Maximization

Online Optimization of Offloading Video Analytics Tasks to M...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yu Liang Sheng Zhang Jie Wu School of Computer and Electronic Information and the School of Artificial Intelligence Nanjing Normal University State Key Laboratory for Novel Software Technology Nanjing University Center for Networked Computing Temple University US

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Real-time video analytics (VA) presents challenges due to its computational intensity and latency sensitivity, especially when processed on mobile devices with limited local resources. We propose to offload VA tasks to edge servers with diverse computational capabilities. We present a "detect + track" approach with on-device object tracking and edge-assisted object detection. We formulate a long-term nonlinear integer programming to maximize the overall accuracy within detection frequency and latency constraints. We then design a queue-based online optimization algorithm to solve it: relax the original problem from the integer domain to the real domain, then employ a queue-based adaptation and randomized rounding strategy. Via rigorous proof, both dynamic regret regarding detection accuracy and the real-time requirement are ensured. Evaluation results also demonstrate the effectiveness of our approach.

关键词： Accuracy image edge detection Visual analytics Signal processing algorithms Streaming media Signal processing Real-time systems Servers Speech processing Optimization

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28th International conference on image processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd International conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

28th International Conference on Image Processing, Computer ...

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28th International conference on image processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd International conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031859328

The proceedings contain 28 papers. The special focus in this conference is on image processing, Computer Vision, and Pattern Recognition. The topics include: image-Based Seal Recognition: Approaches and Challenges in Current Automated systems;Deep Learning Techniques for Lunar Impact Crater Identification Based on CCD and DEM Data;silicon Wafer Map Defect Classification Using Artificial Intelligence Models;low Light image Enhancement Using Autoencoder-Based Deep Neural Networks;towards Elephants Intelligent Monitoring in Zakouma National Park, Chad;a Review of Multi-modal and Multi-view Applications in Hand-Drawn Sketch images;residential Real Estate image Classification for Property Valuation;Novel Method to Investigate Decay in Rotting Bananas Using RGB Color images;lalitha: A Hand Gesture-Based Computer Control System;dishari: A Novel Gesture-Based Educational Application for Specially Challenged People;mobility Anomaly Detection with Intelligent Video Surveillance;Weakly-Supervised Video Anomaly Detection Using Modified Anomaly Score Module and Modified BERT;Contour Detection of Seeds Based on Traditional and Convolutional Neural Network (CNN) Based algorithms;Early Detection of Lameness in Dairy Cattle Using Activity Data, image Analysis, AI and ML - An Approach for Improved Animal Welfare and Economic Impact;implications for Designing Hawks Detection with Data Augmentation and Network Optimizations;a Review of Detecting and Quantification of Cracks Using Convolutional Neural Networks and image processing Techniques;using Linkage Context for Automated Correction in Unsupervised Entity Resolution;the Rising Threat Against Modern Technology in Cybersecurity;an Online Bookstore Design and Implementation;cybersecurity: Sight and Foresight;the Application of Blockchain Technology in the Transmission of Semiconductor Process Recipes;the Use of Social Media and the Internet for the Facilitation of Human Trafficking.

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Role, Significance, and Impact of Method Noise in Multi-Modal Medical image Fusion

Role, Significance, and Impact of Method Noise in Multi-Moda...

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Emerging systems and Intelligent Computing (ESIC), International conference on

作者： Shailesh Bhosekar Prabhishek Singh Deepak Garg School of Computer Science and Artificial Intelligence SR University Warangal Telangana India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Multi modal medical image fusion in, for instance, combining information from computed tomography (CT) scans, positron emission tomography (PET) and magnetic resonance imaging (MRI) into one single dataset, improves the accuracy of diagnostics due to atomistic and functional details of the images. In this paper, the role, importance, and effect of method noise in multi-modal medical image fusion, is considered to assess and improve fusion algorithms. The effectiveness of an algorithm can be determined in terms of its efficacy at preserving salient image content and removing unwanted artifacts while maintaining the former, which is defined by method noise, the residual noise after image processing. In this paper, I review the theoretical basis of method noise, discuss its function in both fusion and denoising processes, and discuss its practical and conceptual implications, showing its importance for quality assessment and algorithm refinement. We apply method noise to the Non-Subsampled Shearlet Transform (NSST) to demonstrate application in multi-modal medical image fusion. Comparative experiments using NSST based fusion with /without method noise demonstrate significantly different visual clarity and quantitative metrics as measured with different metrics. Results show that method noise both maintains image integrity by preventing detail loss and aids in the integration of complementary detail from different modalities. The need for method noise in advancing image fusion techniques is emphasized in this paper, and a framework for future research and application of fusion in diagnostic imaging is presented.

关键词： Visualization Accuracy Reviews Magnetic resonance imaging Computed tomography Noise Refining Quality assessment image fusion Medical diagnostic imaging

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Vehicle Registration Plate Recognition Using YoloV8 and IOT

Vehicle Registration Plate Recognition Using YoloV8 and IOT

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AIML-Applications for Engineering & Technology (ICAET), International conference on

作者： Juber Nadaf Amol Kadam Pramod Jadhav Prasad Kadam Vinod Patil Milind Gaikwad Department of Computer Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of CSBS Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of E & TC Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of Information Technology Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Number Plate recognition or the vehicle License Plate recognition is an interesting topic of research in smart cities which utilizes image processing techniques. Since the number of cars is rising at an exponential rate, automated systems are supposed to store vehicle data for a variety of applications. In this intended algorithm, an efficient technique has been identified for recognition of different vehicle number registration plates. Through this technique, we can easily deal with problems like low light, irregular font number plates, wrong angles. We use the method of Optical Character Recognition to identify characters that are extracted from the registration plate. Even though this project works efficiently for common use cases, it has its own drawbacks and limitations even though we are not using deep learning or any other machine learning algorithms. One of the important steps in vehicle plate recognition is the character recognition which is a technique to obtain individual text characters. YOLOv8 (You Only Look Once), and Darknet- 53 are the methods that is used to extract the features of the data. In this model, the input data is the image of the vehicle that is captured from the camera sensors or cell phones and later cropped to display only the registration plate. We undergo data-pre-processing to improve the quality or the pixels of image on the number plate. Testing is done using two different models one is the model which is obtained with more pre-processing data and the other obtained without any pre-processing data. We evaluate two datasets of number plate images: one with no interference and one where the colour intensity of the image has been decreased to test, the accuracy of the model

关键词： Deep learning YOLO Training image recognition Accuracy Feature extraction Data models Internet of Things Character recognition License plate recognition

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