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

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Real-Time Signal processing using AI Integrated Framework for Color and Drawing in Gesture Recognition 5

Real-Time Signal Processing using AI Integrated Framework fo...

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5th International conference on Contemporary Computing and Informatics, IC3I 2022

作者： Lavania, Geerija Arya, vivek Sharma, Neha Rashid, Mamoon Akram, Shaik Vaseem JECRC Artificial Intelligence & Data Science Department Rajasthan Jaipur India Dept of Electronics and Communication Engineering Haridwar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Vishwakarma University Department of Computer Engineering Pune India Uttaranchal Institute of Technology Uttaranchal University Dehradun India

ISBN: (纸本)9798350398267

In the realm of image processing and pattern recognition, writing with gestures in the air has grown in popularity and difficulty over the past few years. It contributes significantly to the creation of automation processes and can improve human-machine communication in a range of systems. Many studies focus on cutting-edge methods and techniques that will expedite processing while preserving a high level of identification accuracy. Internally, the area of computer vision considers object tracking to be crucial. Object tracking techniques are credited to modern computers, easy access to good-looking, affordable video cameras, and automated video analytics requirements. The video analysis technique generally consists of three fundamental steps: first, recognizing the object, second, tracking its movement from frame to frame, and third, assessing the behavior of that object. Each of the four takes into account a separate issue: choosing the best item representation, choosing the tracking function, purchasing the item, and tracking the item. Numerous real-world applications, such as autonomous monitoring, video identification, and vehicle navigation, depend heavily on object-tracking algorithms. This area is utilized by the research, which focuses on it to create a prospective text-to-text converter transition. This Research occasionally works as a freelance journalist. Your fingerprint will be tracked by a computer. The created text can be copied and used for a variety of things, including emails and SMS. For the deaf, it will be a potent means to establish this connection. By removing the need for writing, an efficient communication system decreases the use of the phone and laptop. © 2022 IEEE.

关键词： Computer vision

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machine vision to Empower an Intelligent Personal Assistant for Assembly Tasks 1st

Machine Vision to Empower an Intelligent Personal Assistant ...

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1st International conference on Optimization, Learning Algorithms and applications (OL2A)

作者： Talacio, Matheus Funchal, Gustavo Melo, victoria Piardi, Luis Vallim, Marcos Leitao, Paulo Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal Univ Tecnol Fed Parana UTFPR Avenida 7 Setembro 3165 BR-80230901 Curitiba Parana Brazil

ISBN: (纸本)9783030918859;9783030918842

In the context of the fourth industrial revolution, the integration of human operators in emergent cyber-physical systems assumes a crucial relevance. In this context, humans and machines can not be considered in an isolated manner but instead regarded as a collaborative and symbiotic team. Methodologies based on the use of intelligent assistants that guide human operators during the execution of their operations, taking advantage of user friendly interfaces, artificial intelligence (AI) and virtual reality (VR) technologies, become an interesting approach to industrial systems. This is particularly helpful in the execution of customised and/or complex assembly and maintenance operations. This paper presents the development of an intelligent personal assistant that empowers operators to perform faster and more cost-effectively their assembly operations. The developed approach considers ICT technologies, and particularly machine vision and image processing, to guide operators during the execution of their tasks, and particularly to verify the correctness of performed operations, contributing to increase productivity and efficiency, mainly in the assembly of complex products.

关键词： Computer vision

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Advanced applications of Optical Kerr Micro-combs

Advanced Applications of Optical Kerr Micro-combs

引用

conference on Lasers and Electro-Optics (CLEO)

作者： Yang Sun Mengxi Tan Xingyuan Xu Jiayang Wu Yang Li Sai T. Chu Brent E. Little Roberto Morandotti Arnan Mitchell David J. Moss Optical Sciences Centre Swinburne University of Technology Hawthorn VIC Australia School of Engineering RMIT University Melbourne VIC Australia State Key Lab. of Information Photonics and Optical Communications Beijing U. of Posts and Telecommunications Beijing China Department of Physics City University of Hong Kong Hong Kong China Xi’an Institute of Optics and Precision Mechanics Precision Mechanics of CAS Xi’an China INRS-Énergie Matériaux et Télécommunications Québec Canada

We review our work on advanced applications of optical Kerr frequency microcombs, including an optical convolutional accelerator operating at 11 Tera-OPS, as well as real-time video signal processing at a speed of 18 ... 详细信息

ISBN: (纸本)9781957171258

关键词： Edge detection image processing machine vision Optical networks Optical neural systems Signal processing

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Infrared and visible image fusion via NSST and PCNN in multiscale morphological gradient domain 6

Infrared and visible image fusion via NSST and PCNN in multi...

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conference on Optics, Photonics and Digital Technologies for Imaging applications vi

作者： Tan, Wei Zhang, Jiajia Xiang, Pei Zhou, Huixin Thiton, William Xidian Univ Sch Phys & Optoelect Engn Xian Peoples R China Univ Copenhagen Dept Comp Sci Copenhagen Denmark

ISBN: (数字)9781510634794

ISBN: (纸本)9781510634794

With the development of sensor technologies, imaging technology is developing more rapidly. What followed was the widespread use of image processing technology in many kinds of applications. For instance, image processing technology has been widely used in video surveillance, medical diagnosis, remote sensing detection and object tracking. As a sub-field of image processing technology, image fusion is the one of most studied technology. The aim of image fusion is to acquire an integrated image that contains more information. This integrated image is more conductive for a human or a machine to understand and mine the information contained in the image. In all kinds of image fusion, infrared (IR) and visible (viS) image fusion is one of the most valuable multi-source image fusion. When imaging the same scene using both IR and viS imaging system, more information can be obtained, but more redundant information is generated. The IR sensor acquires the thermal radiation information of the object in a scene, so the object can also be detected when the lighting conditions are poor. The image acquired by viS light sensors has more spectral information, clearer texture details, and higher spatial resolution. Thus, the scene can be described more completely by integrating the IR and viS images into one image. Meanwhile, the scene can be readily understood by observers, and the information of the scene can be easily perceived. In this paper, an effective IR and viS image fusion via non-subsampled shearlet transform (NSST) and pulse-coupled neural network (PCNN) in multi-scale morphological gradient (MSMG) domain is proposed. First, low frequency sub-image and high frequency sub-images are obtained through NSST. Then, the low frequency sub-image and high frequency sub-images are fused via a MSMG domain PCNN (MSMG-PCNN) strategy. Finally, the fused image is reconstructed by inverse NSST. Experimental results demonstrate that the proposed MSMG-PCNN-NSST algorithm performs effecti

关键词： Infrared and visible image fusion non-subsampled shearlet transform pulse-coupled neural network multi-scale morphological gradient

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A machine Learning based Facial Expression and Emotion Recognition for Human Computer Interaction through Fuzzy Logic System

A Machine Learning based Facial Expression and Emotion Recog...

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International conference on Inventive Computation Technologies (ICICT)

作者： K vinutha Manoj Kumar Niranjan Jagdish Makhijani B. Natarajan V. Nirmala T. R vijaya Lakshmi Department of Information Science and Engineering BMS Institute of Technology and Management Bengaluru Karnataka India Department of Computer Applications Rustamji Institute of Technology Gwalior Madhya Pradesh India Department of Computer Science and Engineering Rustamji Institute of Technology Gwalior Madhya Pradesh India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai Tamil Nadu India Department of Science and Humanities Faculty of engineering Karpagam academy of higher education Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Mahatma Gandhi institute of technology Hyderabad Telangana India

Facial recognition is in use for the past decade there are many applications that needs facial expression to learn the human behaviour and emotions for certain activities. Facial recognition is in a development phase where many service providers use this feature to find the expression of the people on using their BlogSpot or website or reading any news article. This recognition of facial expression is highly possible with the help of machine learning technology. This research study has developed a facial expression recognizing algorithm using Python programming language with the help of Keras software package. This algorithm is purely based on machine learning approach that enables the programmer to process the facial image and convert it into data that is helpful in prediction of facial expression using the fuzzy logic technique. The fuzzy logic technique is a prediction method that helps programmer to predict the intermediate data by providing the initial and ending conditions. For enabling the facial recognition to process any system or a mobile device the algorithm needs permission to access the camera, once the onto the access is permitted the algorithm retrieves the image from the vision sensor and with the help of image processing technology of the machine learning algorithm the program the program converts the data from the vision sensor into required facial expression and emotional content.

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A Review of image Classification and Object Detection on machine learning and Deep Learning Techniques 5

A Review of image Classification and Object Detection on Mac...

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5th International conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Sandhya Devi, R.S. vijay Kumar, V.R. Sivakumar, P. Kumaraguru College of Technology Department of EEE Coimbatore India Anna University Regional Campus Department of Electronics and Communication Engineering Coimbatore India PSG College of Technology Department of EEE Coimbatore India

ISBN: (纸本)9781665435246

One of the known computer vision tasks is image recognition and classification. Computer vision - a field that automates tasks and works with multi - dimensional data, when combined with the power of artificial intelligence mimics human visual system. image processing and analysis is an integral part of computer vision. image classification, a vital part in image analysis is used to categorize pixels or classes of interest by extracting information classes from a multiband raster image. Automatic sorting of pixels and analogous multispectral reflectance values are widely used in land cover mapping cases where remote sensing and Geographical Information System (GIS) is integrated. Designing of image classification and processing procedure by considering various factors involved, decides the success of the classification approach being used. This paper provides a summarization of various classical machine learning based classification approaches and the major advanced deep learning based classification techniques, issues affecting the success of each classification approach and its applications. Convolutional Neural Network (CNN) is the most popular deep network architecture used to analyze visual imagery. This paper discusses waste object detection, a part of the waste segregation process based on CNN hierarchical image classification approach. Here, the system is able to detect the object and provides the relative match percentage of the object being detected. Open source software libraries such as Tensorflow and Spyder is used for this process © 2021 IEEE.

关键词： image classification

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Comprehensive Weather Recognition Using ResNet-50 and a Novel Weather image Dataset

Comprehensive Weather Recognition Using ResNet-50 and a Nove...

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Signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Jun Wang Yunhan Zhou Min Fu School of Electrical and Electronics Engineering UESTC Chengdu China Cyrinn Science & Technology Ltd. Guangzhou China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Weather conditions play a crucial role in both daily human activities and industrial operations. For example, recognizing different weather patterns is critical for outdoor automation systems. With the development of AI technology, scientific researchers have trained many models which receive weather images as inputs and output the weather in these images fast and accurately. However, many existing weather recognition models are limited in scope, typically identifying only four types of weather with varying degrees of accuracy. In this study, the two key contributions of this research are: first, the creation of a custom dataset designed specifically for recognizing seven distinct weather conditions, and second, the exploration of multiple machine learning techniques to identify the most effective model for this task. By developing our own dataset, we ensure a broader representation of weather types, allowing the models to train and be tested on more diverse and realistic scenarios. We evaluate five different algorithms: Support Vector machine (SVM), Random Forest, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and ResNet50. To enhance model generalization and reduce overfitting, we employ data augmentation techniques and add dropout layers in the CNN and ResNet50 architectures. Through extensive experimentation, ResNet50 emerges as the best-performing model, achieving an accuracy of approximately 87% on the custom dataset. This work demonstrates the importance of creating domain-specific datasets and using a range of machine learning methods to develop accurate and reliable weather recognition models. Our approach offers a robust solution for outdoor vision systems, with potential applications in autonomous vehicles, surveillance systems, and other environments where accurate weather detection is critical for safety and performance.

关键词： Support vector machines Accuracy image recognition Surveillance Signal processing algorithms Signal processing Safety Convolutional neural networks Meteorology Residual neural networks

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Decoupling feature extraction and classification layers for calibrated neural networks 24

Decoupling feature extraction and classification layers for ...

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Proceedings of the 41st International conference on machine Learning

作者： Mikkel Jordahn Pablo M. Olmos Cognitive Systems Technical University of Denmark Kongens Lyngby Denmark Signal Processing Group (GTS) Universidad Carlos III de Madrid Madrid Spain

Deep Neural Networks (DNN) have shown great promise in many classification applications, yet are widely known to have poorly calibrated predictions when they are over-parametrized. Improving DNN calibration without comprising on model accuracy is of extreme importance and interest in safety critical applications such as in the health-care sector. In this work, we show that decoupling the training of feature extraction layers and classification layers in over-parametrized DNN architectures such as Wide Residual Networks (WRN) and vision Transformers (viT) significantly improves model calibration whilst retaining accuracy, and at a low training cost. In addition, we show that placing a Gaussian prior on the last hidden layer outputs of a DNN, and training the model variationally in the classification training stage, even further improves calibration. We illustrate these methods improve calibration across viT and WRN architectures for several image classification benchmark datasets.

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image Labeling Using Convolutional Neural Network

Image Labeling Using Convolutional Neural Network

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Network, Multimedia and Information Technology (NMITCON) International conference on

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

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.

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Analysis of Impact of image Restoration and Segmentation on Classification Model

Analysis of Impact of Image Restoration and Segmentation on ...

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International conference on Computing Communication Control and Automation (ICCUBEA)

作者： Sushma Rahul vispute K. Rajeswari Aryan Nema Arya Jagtap Mrugendra Kulkarni Pranav Mohite Department of Computer Engineering PCCOE Pune India

A widely studied problem in computer science is the restoration, segmentation, and classification of images, which involves image processing, computer vision, and machine learning techniques. Deep learning has made significant contributions to this field, bringing machine learning closer to artificial intelligence. Deep learning has a broad range of applications, including in surveillance, healthcare, medicine, and sports. Convolutional neural networks (CNN), a combination of artificial neural networks (ANN) and deep learning techniques, have made incredible advancements in deep learning. This survey compares methods for restoring noisy images, such as wiener filter, wavelet method, and wiener filtering with BM3D technique, using Gaussian blurring and noising methods. The survey also examines the RGB colour model and YCbCr colour model for image segmentation. image classification is studied using CNN, where the survey discusses various parameters of convolutional neural networks, including activation functions and pooling methods.

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