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

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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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Advancing Multi-Class Arc Welding Defect Classification: DEEPTLWELD Intelligent System Utilizing Computer vision, Deep Learning, and Transfer Learning on Radiographic X-ray images for Bangladesh's Manufacturing Sector

Advancing Multi-Class Arc Welding Defect Classification: DEE...

引用

Computing, applications and Systems (COMPAS), IEEE International conference on

作者： Avijit Chowdhury Department of Mechanical Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Welding defects are a crucial problem in the manufacturing industry. However, the industry faces enormous losses for these defects. Conditional monitoring and quality control can reduce this loss. In Industry 4.0, artificial intelligence revolutionized every field. This research aims to improve industrial automation by creating a multi-class classification system through digital image processing, deep learning, and transfer learning—a real-time application integrated with deep learning, transfer learning, image processing, and MLOPS operations. Accurate prediction occurred by various models, such as custom CNN and pre-trained models (InceptionV3, EfficientNetB7, DenseNet201, and ResNet152). DEEPTLWELD system was developed using a bespoke dataset of 4,000 radiographic X-ray pictures with four frequent categories of defects that are essential for tackling difficulties faced by local industry challenges. This dataset is All radiographic images processed with image processing techniques (distance transform, watershed transform, edge detection). In the transfer learning part, weights were loaded manually as those weights performed better with a particular model. Out of the networks, custom CNN achieved high accuracy with 98% after fine-tuning. The deployment of the produced model in a production environment developed with a Streamlit framework is ensured by its smooth integration into a machine learning pipeline housed on Amazon EC2 servers. This intelligent system has the potential for broader applications in inclusive technology.

关键词： Radiography Deep learning Manufacturing industries Computational modeling Welding Transfer learning Transforms Production Predictive models X-ray imaging

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Edge Detection with different Parameters in Digital image processing using GUI 5

Edge Detection with different Parameters in Digital Image Pr...

引用

5th International conference on Computing Methodologies and Communication, ICCMC 2021

作者： Srujana, P. Priyanka, J. Patnaikuni, V Y S S Sudir Vejendla, Nancharaiah Lendi Institute of Engineering and Technology Department of Ece

ISBN: (纸本)9781665403603

Edge Detection is one of the most popular techniques in image processing, where it finds the boundaries of objects in an image. Edge detection place a vital role in different areas like image processing, computer vision, and machine vision for image segmentation and data extraction etc., Edge is nothing but a change of intensity in an image, whereas edge detection is a technique in which the image is divided into different regions of discontinuity. The proposed research work finds the edges of different digital images by using different operators like Sobel, Perwitt, Roberts, Log, and Canny have compared images by considering different parameters like MSE, PSNR, SNR and found that the Canny operator is the best for edge detection as it is a multi-stage operator, where it detect edges at the same time and it removes noise from the image. Along with its edges, some of the applications using edge detection are considered. Matlab GUI is utilized for further analysis in this paper. © 2021 IEEE.

关键词： Graphical user interfaces

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A Mobile Food Recognition System for Dietary Assessment 21st

A Mobile Food Recognition System for Dietary Assessment

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21st International conference on image Analysis and processing (ICIAP)

作者： Akti, Seymanur Qaraqe, Marwa Ekenel, Hazim Kemal Istanbul Tech Univ Istanbul Turkey Hamad Bin Khalifa Univ Ar Rayyan Qatar

ISBN: (纸本)9783031133213;9783031133206

Food recognition is an important task for a variety of applications, including managing health conditions and assisting visually impaired people. Several food recognition studies have focused on generic types of food or specific cuisines, however, food recognition with respect to Middle Eastern cuisines has remained unexplored. Therefore, in this paper we focus on developing a mobile friendly, Middle Eastern cuisine focused food recognition application for assisted living purposes. In order to enable a low-latency, high-accuracy food classification system, we opted to utilize the Mobilenet-v2 deep learning model. As some of the foods are more popular than the others, the number of samples per class in the used Middle Eastern food dataset is relatively imbalanced. To compensate for this problem, data augmentation methods are applied on the underrepresented classes. Experimental results show that using Mobilenet-v2 architecture for this task is beneficial in terms of both accuracy and the memory usage. With the model achieving 94% accuracy on 23 food classes, the developed mobile application has potential to serve the visually impaired in automatic food recognition via images.

关键词： Food recognition Assistive technology Computer vision

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A Framework For Single image Dehazing Using DWT Based Cross Bilateral Filter Fusion of Generative and ASM Models 2

A Framework For Single Image Dehazing Using DWT Based Cross ...

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2nd International conference on Innovative Sustainable Computational Technologies, CISCT 2022

作者： Revanth, Banala Dwivedi, Sanjay K. Kumar, Manoj Babasaheb Bhimrao Ambedkar University Department of Computer Science Lucknow India

ISBN: (纸本)9781665474160

Due to the latest advancements in technologies like artificial intelligence, machine learning, and deep learning in image processing, computer vision, and their uses in various applications, there has been a lot of interest in the restoration of murky images. This paper gives a novel single-image dehazing (SID) framework for the restoration of single hazy image (SHI). The framework has three parts. The first part of the framework comprises three networks. A white-balanced auto-encoder network in the front, followed by a pair of sequential auto-encoder networks at the end for image dehazing (ID). A white balance auto-encoder network is used for correcting the white balance error in the input hazy image (HI). Pair of sequential auto- encoder networks gives haze-reduced output by taking corrected white balanced error image as input. The second part comprises an ASM-based model Dark Channel Prior (DCP) for ID. DCP produce haze-free images (HFI) by taking HI as input. The third part presents the fusion model for integrating the first and second parts of the framework. DWT-based cross bilateral filter model is the fusion model (FM) to get the final HFI. The first part of the framework is trained by using a hybrid loss function (perceptual loss function combined with MSE loss function) on O- HAZE, I-HAZE, and MRFID data sets. The proposed framework got considerably good result in terms of Structural Similarity Index (SSIM), Peak signal-to-noise ratio (PSNR), compared with cutting-edge methods. © 2022 IEEE.

关键词： Restoration

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Implementation of Fast Gradient Sign Adversarial Attack on vision Transformer Model and Development of Defense Mechanism in Classification of Dermoscopy images

Implementation of Fast Gradient Sign Adversarial Attack on V...

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IEEE Signal processing and Communications applications (SIU)

作者： Elif Kanca Selen Ayas Elif Baykal Kablan Murat Ekinci Yazılım Mühendisligi Karadeniz Teknik Üniversitesi Trabzon Türkiye Bilgisayar Mühendisligi Karadeniz Teknik Üniversitesi Trabzon Türkiye

In recent years, deep learning has been successfully applied in medical images due to its ability to learn high complex and multidimensional data. However, it is known that deep learning models are vulnerable to adversarial machine learning attacks, which add small imperceptible perturbation to the legitimate input image, causing the models to produce incorrect results. In this study, Fast Gradient Sign Method is applied to vision Transformer, a basic pre-trained transformer- based model, which is used for binary classification of a publicly available skin lesion dataset and the robustness of the model is analyzed. Then, the adversarial training approach is used to improve the robustness of the model against adversarial attacks. The experimental results show that the classification accuracy is reduced from 90.1% to 27.38% even for a small perturbation, and the adversarial training approach increases the model's robustness with an accuracy value of 96.61%.

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Leaf Lens: An Intelligent vision for Plant Disease Diagnosis using Deep Learning

Leaf Lens: An Intelligent Vision for Plant Disease Diagnosis...

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Artificial Intelligence and machine Learning applications Theme: Healthcare and Internet of Things (AIMLA), International conference on

作者： P. Yogendra Prasad M. Ramu Sanjamala Reshma Rayachoty Chandana Priya Pasupuleti Anusha B Likhith Reddy Dept of CSSE Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Dept of CSE Annamacharya Institute of Technology & Sciences Tirupati India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

In India, where 70% of the population is involved in agriculture, accurate recognition of botanical disorders is crucial to minimize crop losses. Manual monitoring of these diseases requires significant labor, expertise in plant pathology, and time investment. This paper presents a methodology for identifying plant diseases by analyzing leaves' images. image processing extracts valuable properties and information from the images, while deep learning, [3] a part of artificial intelligence, which simplifies data preprocessing. The algorithms used can process unstructured data, including text and images, automating feature extraction and diminishing the dependance on human professionals. This automated approach facilitates informed decision-making and precise predictions based on extensive training data. The paper introduces an innovative approach to plant disease detection through deep learning, specifically using the VGG19 model, which can scrutinize intricate patterns in plant images, providing a reliable method for identifying diseases.

关键词： Deep learning Plant diseases image recognition Computational modeling Training data Feature extraction Agriculture

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A Novel Design and Development model for people counting in a Closed Environment with machine Learning Approach

A Novel Design and Development model for people counting in ...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Praveen Gupta Umashankar Ghugar Chinta vishnu Vardhan Rakesh Nayak Chaur Singh Rajpoot Department of Computer Science and Engineering GITAM School of Technology GITAM (Deemed to be University) Visakhapatnam India Department of Computer Science and Engineering OP Jindal University Raigarh CG Parul Institute of Technology Parul University Vadodara Gujarat India

image processing can be used to solve some daily applications. Computer vision and Artificial Intelligence in the computer system provide the capability to work like the human mind. In the last decade, computer programming is moving towards processing images and finding useful results and analysis. This experiment is an effort to count the number of people in a closed environment. there are many situations when it is required to count the persons in the room. An application can do this work with accuracy and in less time. this can be enhanced to another area where it can count some other objects also. In this experiment efforts have ben done for the identification of people and count them correctly. It has not been tested on large numbers yet. You only look once (YOLO) real time object detection technique is used with some more microenvironments. this can be used for real-life situations and for the safety of any objects. This paper is based on YOLO. The experiments have many benefits and applications in all kinds of industries. in the future, it may be extended to videos.

关键词： Industries Computer vision image processing Object detection machine learning Programming Real-time systems

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Computer vision-Based Early Fire Detection Using machine Learning

Computer Vision-Based Early Fire Detection Using Machine Lea...

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2022 International conference on Communication, Computing and Internet of Things, IC3IoT 2022

作者： Kumar, K. Sravan Varma, K. Balaram Sujihelen, L. Jancy, S. Aishwarya, R. Yogitha, R. Sathyabama Institute of Science and Technology Department of Cse Chennai India

ISBN: (纸本)9781665479950

Thousands of hectares of land are destroyed every year by fire. There is more carbon monoxide generated by these fires than from all the traffic. The early detection of possible danger areas and early detection of fires can greatly reduce response times and firefighting costs as well as the possibility of damage. By using image processing technology, a fire could be detected early and people would be alerted. The sensor method detects fires through smoke, but it has limited applications, and is only suitable for certain areas. In this proposed work, limitations will be reduced and the technology will be optimized. In this proposed system, a Haar Cascade classifier is used for fire detection. Pycharm IDE is used for implementing this work. The system uses the webcam as a source of input for capturing the video feed from the surrounding environment. Detection of the fire will be exact and accurate without any delay with the proposed work. © 2022 IEEE.

关键词： Fires

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Application of Computer vision in Education in Mechatronic Control System 14

Application of Computer Vision in Education in Mechatronic C...

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14th International conference ELEKTRO, ELEKTRO 2022

作者： Tudic, Vladimir Stancic, Adam Kralj, Damir Tropcic, Tomislav Karlovac University of Applied Sciences Professor Department of Mechanical Engineering and Mechatronics Karlovac Croatia

ISBN: (纸本)9781665467261

The use of computer vision in mechatronics is one of the most important and rapidly increasing fields of science and technology today. Modern mechatronics engineers must be familiar with these types of simple and inexpensive programmable control systems. The open-source control system designed in the Python Open CV library is described in this paper as a mechatronic educational prototype "ball and plate"with customizable configuration options. Positions for the educational prototype were constructed in 3D print techniques during the phases of computer-aided design, and an experimental manageable platform with camera feedback was exhibited. The purpose is to change the set point position in order to control the ball's position. Two-degrees-of-freedom mechatronic system with computer vision USB HD camera as feedback and DC servomotors as actuators. Using the Arduino platform and Python script-based open-source control system tools, you may change the color, PID controller parameters, and indirect control the movement of servomotors via PWM signals. This enables students to build and test position control algorithms for various round object forms and colors. Students can use the presented mechatronic platform to further their education by learning about the meaning of computer vision in mechatronics and the working principles of PID controller feedback. © 2022 IEEE.

关键词： Python

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