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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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applications of Diffusion Model image Restoration in the Field of Heritage Restoration: Overview and Outlook 3

Applications of Diffusion Model Image Restoration in the Fie...

引用

3rd International conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Li, Yiran Nanjing University of Finance and Economics Qixia District Jiangsu Province Nanjing China

ISBN: (纸本)9781643684444

The restoration of museum heritage is an important task with significant cultural and historical value;however, traditional methods of restoration are frequently constrained by the extent of the damage to the heritage as well as the constraints of the restoration techniques. In recent years, a method of restoration known as the diffusion model, which is based on computer vision and machine learning, has been gradually applied to the field of museum relics restoration and has shown enormous potential in relic restoration. This method of restoration was developed in the 1980s and is still in use today. In the field of image restoration, research and application of diffusion models are reviewed, and this article provides a summary of the development history, methodology principles, and application cases associated with these topics. This article provides an introduction to the fundamental ideas and principles underlying diffuse models, as well as a summary of the current state of research and an outlook on potential future trends and prospects pertaining to image repair within the domain of image processing. © 2023 The Authors.

关键词： image reconstruction

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image Labeling Using Convolutional Neural Network

Image Labeling Using Convolutional Neural Network

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2023 IEEE International conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Vasantha Kumari, N. Garimella, Mounika Bhan, Madhu Ramaiah Institute of Technology Dept. of Mca Bengaluru India Ramaiah Institute of Technology Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350300826

Due to the rapid rise in the identification of digital materials, automatic image classification has emerged as the most difficult topic of computer vision. In comparison to human vision, automatic visual understanding and analysis by a system is challenging. The findings of many experiments that were done to fix problems of automatic picture classification with the current categorization method were simple picture primitives. However, those methods don't accurately classify photos. In this research work, a system has been created that makes use of deep learning to deliver the necessary results in the area of computer vision. Our technology automatically categorizes photographs using Convolutional Neural Network, a popular machine learning method. Our algorithm classifies grayscale photos using the Digit of MNIST dataset as a benchmark. The dataset that is employed for the training of the system contains grayscale images, which demand higher processing resources for image categorization. By employing Convolution Neural Network to train the photos, we were able to attain higher accuracy results in the experimental section, demonstrating that our model is highly accurate at classifying images. © 2023 IEEE.

关键词： image classification

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Drone-Assisted Infrared Thermography and machine Learning for Enhanced Photovoltaic Defect Detection: A Comparative Study of vision Transformers and YOLOv8 44th

Drone-Assisted Infrared Thermography and Machine Learning f...

引用

44th SGAI International conference on Innovative Techniques and applications of Artificial Intelligence, SGAI 2024

作者： Memari, Ammar Debich, Tarek Jade University of Applied Sciences Friedrich-Paffrath-Straße 101 Wilhelmshaven26389 Germany

ISBN: (纸本)9783031779176

This paper presents a comparative study on the application of drone-assisted infrared thermography coupled with state-of-the-art machine learning models, including vision Transformers (viTs) and YOLOv8, for efficient and accurate defect detection in Photovoltaic (PV) systems. The research outlines the methodology for on-site inspections and details the integration of drones to capture high-resolution thermal imagery, identifying minute anomalies indicative of potential system failures. By employing advanced image processing techniques and training AI models, the study compares the performance of these models in accurately identifying and classifying PV defects. A segmentation model based on TensorFlow was trained and used to detect the location of PV panels in the camera imagery, followed by the separate application of YOLOv8 and viTs to classify defects in the detected PV panels. The dataset used to train the models was carefully curated and prepared specifically for this study, representing another contribution of this paper. This approach not only enhances detection capabilities but also streamlines the processes of data collection, analysis, and reporting, ultimately leading to improved decision-making for system operators and stakeholders. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Thermography (imaging)

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OPTIMIZING CNN PERFORMANCE ON CIFAR-10: A STUDY ON MULTI-CORE processing, BATCHING TECHNIQUES, AND FUTURE NETWORK ARCHITECTURES 2

OPTIMIZING CNN PERFORMANCE ON CIFAR-10: A STUDY ON MULTI-COR...

引用

2nd International conference on Mechatronic Automation and Electrical Engineering, ICMAEE 2024

作者： Jin, Datong College of Art and Science The Ohio State University Columbus43210 United States

ISBN: (纸本)9781837242672

The performance of Convolutional Neural Networks (CNNs) is critically dependent on the underlying computational configurations, particularly in terms of processing capabilities and data handling techniques. As deep learning models become increasingly complex and data-intensive, the choice between single-core and multi-core processing, along with strategies such as batching, becomes pivotal. These configurations directly impact the efficiency and speed of model training and inference, which are essential for applications requiring real-time processing and high accuracy, such as image recognition and automated systems. Study Contributions: This research focuses on comparing the effects of single-core versus multi-core processing configurations on CNN performance, integrating batching techniques to observe their combined influence on reducing latency and increasing throughput. The study reveals that leveraging multi-core processing with batching substantially enhances CNN operations. Additionally, it addresses the deployment challenges of the YOLOv5 architecture, emphasizing the necessity for further architectural improvements. The paper suggests that future enhancements could include adopting deeper network structures to elaborate on complex features and employing methods like ResNet to counteract gradient vanishing problems. Moreover, it advocates for the fine-tuning of models with pre-trained datasets such as imageNet to boost robustness and accuracy, particularly demonstrated on the CIFAR-10 dataset. These recommendations aim to refine CNN capabilities, thereby advancing the broader field of machine learning and computer vision. © The Institution of Engineering & Technology 2024.

关键词： Convolutional neural networks

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Streamlining Crop Segmentation with Multispectral Imaging and Foundation Models: Minimizing Manual Annotation 20

Streamlining Crop Segmentation with Multispectral Imaging an...

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20th IEEE International conference on Intelligent Computer Communication and processing conference, ICCP 2024

作者： Aszkowski, Przemyslaw Kraft, Marek Institute of Robotics and Machine Intelligence Poznań University of Technology Poznań Poland

ISBN: (纸本)9798331539979

Deep learning advancements have significantly enhanced computer vision applications in precision agriculture. While RGB cameras operating in visible light are affordable, they provide limited information compared to multispectral equipment. This research analyses methods to reduce the need for manual annotation when training a model using only RGB images, without compromising the model's accuracy. We propose a semi-supervised approach where a teacher model, trained on multispectral images, generates artificial ground truth data to train a student model that operates solely on RGB images. This strategy has enabled us to achieve nearly a tenfold reduction in the required training data while maintaining similar performance metrics. Additionally, we explore the potential of segmentation foundation models to simplify the manual annotation process, reducing the need for full segmentation masks to just bounding boxes. Our findings also indicate that using multispectral images as input for the Segment Anything Model is more effective than using RGB images. © 2024 IEEE.

关键词： image segmentation

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image processing using Cloud for Surveillance 5

Image Processing using Cloud for Surveillance

引用

5th International conference on Information Management and machine Intelligence, ICIMMI 2023

作者： Tiwari, Shivam Phukan, Aarhee Vedhavathy, T.R. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798400709418

In the era of digitization and big data, the world is inundated with an ever-growing volume of visual content, be it images or videos. As organizations strive to harness the potential of these multimedia data sources, there is an increasing need for advanced image processing techniques that can automate the analysis and extraction of valuable information. Amazon Web Services (AWS) Rekognition emerges as a powerful solution in this landscape, offering a comprehensive system for image and video analysis through the lens of machine learning and computer vision. This paper delves into the realm of image processing using AWS Rekognition, unveiling the transformative capabilities of this cloud-based service and its applications in various domains. As we embark on this journey, we will explore the principles, methodologies, and real-world implications of leveraging AWS Rekognition for image analysis. © 2023 ACM.

关键词： Computer vision

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2024 2nd International conference on Computer, Communication and Control, IC4 2024

2024 2nd International Conference on Computer, Communication...

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2nd International conference on Computer, Communication and Control, IC4 2024

ISBN: (纸本)9798350387933

The proceedings contain 134 papers. The topics discussed include: an adaptive storage switching algorithm for fault-tolerant network attached storage systems;Covid-19 prediction using machine learning algorithms;energy management of hybrid electric vehicles using cascaded fuzzy logic controller;dynamic lane management with IoT for real-time lane configuration and traffic flow;a closer look at sclera: emerging trends in biometric security;cognitive vision companion: an ai-enhanced support system for the visually impaired;advances in medical image processing for liver tumor recognition: a comprehensive survey;a gradient boosting algorithm to predict energy consumption for home applications;and review on text classification using improved deep learning models.

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Movie Recommendation System Based on Emotion Detection Using machine Learning Techniques

Movie Recommendation System Based on Emotion Detection Using...

引用

2024 IEEE International conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Vaishnavi, S.R. Sreelakshmi, S. Anu Prabha, R.S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382693

The face is a critical perspective in predicting human feelings and moods. More frequently than not human senti-ments are extricated with the utilization of the camera. Various applications are being made based on the location of human sentiments. A few applications of feeling revelation are trade notice suggestion, e-learning, mental clutter, sadness disclosure, criminal conduct discovery, etc. This paper presents a novel real-time emotion-based movie recommendation system that combines computer vision, deep learning, and image processing strategies. The system coordinating OpenCV near DeepFace for effective emotion examination utilizing webcam input, giving clients personalized movie recommendations based on their recognized enthusiastic states. The system commences by using OpenCV to capture real-time webcam feeds and utilizes a Haar-Cascade classifier for facial discovery. The recognized faces are analyzed for prevailing feelings utilizing the DeepFace library, empowering exact feeling distinguishing proof. Within the recommendation stage, the system joins content-based filtering by processing a movie dataset utilizing TF-IDF. Genres and plot keywords serve as features for building the TF-IDF matrix. Cosine similarities between the user's emotion vector and relevant movie genres are then calculated, coming about in a list of personalized movie rec-ommendations. Index Terms-FER, OpenCV, DeepFace, Haar-Cascade Algorithm, Content-Based Filtering, TF-IDF © 2024 IEEE.

关键词： Emotion recognition visualization Webcams Face recognition Motion pictures Real-time systems Libraries

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Context-Aware Automated System for image Caption to ASL Translation for Improved Accessibility in Media applications

Context-Aware Automated System for Image Caption to ASL Tran...

引用

2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Aluko, Oluwafolahanmi Menon, vineetha The University of Alabama in Huntsville Dept. of Computer Science HuntsvilleAL United States

ISBN: (纸本)9798331504847

With the emergence of Artificial Intelligence (AI) and the rapid growth of Generative AI, significant progress has been made in image captioning, delivering remarkably precise captions from static images and, more recently, videos [1]. Alt-Text has significantly progressed due to initiatives aimed at improving the context and sentiment recorded during automated captioning. This paper seeks to elaborate on a portion concerning Natural Language processing (NLP) and its significance in enhancing accessibility and context-awareness within the framework of the American Sign Language (ASL) application. This work presents a case study on significance of providing complete context regarding a caption or image to the end user, encompassing emotions and conscious attributes present within the phrase. This paper extensively elaborates on the accessibility functions presently involved in image-captioning. Is user-dependent (requiring users to supply a caption for the image) or standard machine-created alt-text sufficient for complete accessibility? Might incorporating a sign language, such as American Sign Language (ASL), be essential in providing users with more information than what the text of a caption communicates? The key question is: how can we enhance this image-caption translation to be more context-sensitive to convey the subtleties that could be crucial for user comprehension? The aim is to create a world where all voices resonate and every person feels accepted, and this paper represents an initial move towards an AI revolution that might be crucial in realizing that vision. © 2025 IEEE.

关键词： Computer aided instruction

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Review and Enhancement of Discrete Cosine Transform (DCT) for Medical image Fusion 16th

Review and Enhancement of Discrete Cosine Transform (DCT) fo...

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16th International conference on Information Technology and applications, ICITA 2022

作者： Alhatami, Emadalden Bhatti, Uzair Aslam Huang, MengXing Feng, SiLing School of Information and Communication Engineering Hainan University Haikou570100 China

ISBN: (纸本)9789811993305

An image fusion is a kind of single process which combines the necessary or efficient information from a set of different or similar input images into a single output image where the resulting image is more accurate, informative, and complete than any of the input images with a specific algorithm. image enhancement is a process used to improve the quality of an image and increases the application of these input data images, which is helpful in different fields of science such as medical imaging, microscopic imaging, remote sensing, computer vision, robotics, etc. In this paper, we describe the primary function of image fusion to improve an image’s good quality by evaluating the sharpness. Then we attempt to give an overview of multi-modal medical image fusion methods, emphasizing how we can use the DCT method for medical image fusion. This fused image provides more accurate information about the real world, which is helpful for human vision and machine perception or any further image processing tasks in the future. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image fusion

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