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检索条件"任意字段=2023 International Conference on Image Processing and Computer Vision, IPCV 2023"

共 16232 条记录，以下是4661-4670 订阅

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YOLO-v5 Based Single Step Student Affect State Detection System 2

YOLO-v5 Based Single Step Student Affect State Detection Sys...

引用

2nd international conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal processing, PCEMS 2023

作者： Mandia, Sandeep Mushtaq, Faisel Singh, Kuldeep Mitharwal, Rajendra Panthakkan, Alavikunhu Malaviya National Institute of Technology Department of Electronics and Communication Engineering Jaipur India Malaviya National Institute of Technology Department of Computer Science and Engineering Jaipur India University of Dubai College of Engineering and IT Dubai United Arab Emirates

ISBN: (纸本)9798350310719

Online education has increased tremendously due to vast availability of internet recently. Student emotion and engagement is directly related to learning goals and productivity. The existing computer vision based student engagement analysis techniques require two steps for engagement detection. In this paper, single step student affect state detection method is proposed using recent deep learning algorithms. Also a learning centered affect state dataset is curated from public databases. The YOLO-v5 deep learning algorithm is trained on the curated database to detect the affect states. The experimental results show that the proposed one step method is able to detect the affect states reliably. The proposed method also performs inference on an edge device with limited compute resource. The proposed method achieved 0.996, 0.921, 0.96, and 0.777 values of overall precision, recall, mAP@0.5, and mAP@0.5-0.95 respectively. © 2023 IEEE.

关键词： Students

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Joint Ann-SNN Co-training for Object Localization and image Segmentation 48

Joint Ann-SNN Co-training for Object Localization and Image ...

引用

48th IEEE international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Baltes, Marc Abuhajar, Nidal Yue, Ye Smith, Charles D. Liu, Jundong Ohio University School of Electrical Engineering and Computer Science AthensOH45701 United States University of Kentucky Department of Neurology LexingtonKY40506 United States

ISBN: (纸本)9781728163277

The field of machine learning has been greatly transformed with the advancement of deep artificial neural networks (ANNs) and the increased availability of annotated data. Spiking neural networks (SNNs) have recently emerged as a low-power alternative to ANNs due to their sparsity *** this work, we propose a novel hybrid ANN-SNN cotraining framework to improve the performance of converted SNNs. Our approach is a fine-tuning scheme, conducted through an alternating, forward-backward training procedure. We apply our framework to object detection and image segmentation tasks. Experiments demonstrate the effectiveness of our approach in achieving the design goals. © 2023 IEEE.

关键词： ANN-to-SNN conversion detection segmentation Spiking neural network (SNN)

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Recognizing Indian Classical Dance Forms Using Transfer Learning 4th

Recognizing Indian Classical Dance Forms Using Transfer Lear...

引用

4th international conference on Machine Intelligence and Signal processing, MISP 2022

作者： Reshma, M.R. Kannan, B. Jagathyraj, V.P. Sabu, M.K. Department of Computer Applications CUSAT Kerala Kochi India School of Management Studies CUSAT Kerala Kochi India

ISBN: (纸本)9789819900466

The rise of cultural tourism in India and massive digitization over the last decade has necessitated preserving Indian art forms. Recent advances in artificial intelligence (AI) have provided the tools and techniques to make these efforts more efficient. The potential research areas involved are digital image processing, computer vision, and machine intelligence. Since researchers have not much explored the scope of the digital preservation of Indian classical dance, this paper contributes a method to classify different Indian classical dance forms. computerized dance modeling offers a wide range of uses, including cultural conservation, online learning, entertainment, and even remedial treatment. Because the dataset is small, the paper uses transfer learning models for feature extraction and deep neural networks for classification. The proposed method showed a remarkable accuracy of 97.05 percent. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Automated Cheque processing Through Data Verification and Siamese Networks 1st

Automated Cheque Processing Through Data Verification and Si...

引用

1st international conference on Microelectronics, Communication Systems, Machine Learning, and Internet of Things, MCMI 2020

作者： Muthigi, Anil Kumar, Ashutosh Bhagchandani, Gaurav Muthigi, Kiran Nath, Vijay Department of Computer Science B.I.T. Mesra JH Ranchi835215 India Department of Computer Science Columbia University New YorkNY10027 United States

ISBN: (纸本)9789811919053

Bank cheques are still majorly used for financial transactions all over the world. A large number of cheques are processed manually on a daily basis, thereby requiring a lot of time, money and human effort. In such a manual verification, information like date, signature, amount present on the cheque has to be physically verified. This paper aims at finding a solution for processing cheques which increases the efficiency of this process, while minimizing human intervention. The service was hosted locally on a webpage, we first accepted the cheque image from the user, passed it to OpenCV which returned the various parts of the cheque which were then passed to Google vision API to convert them into text, but MICR code was passed to tesseract OCR. After the successful extraction, the details were verified from the information in the SQLite database and signature was verified using the model trained in Jupyter Notebook. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MICR OpenCV Siamese networks Tesseract OCR

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Ensemble Model of Visual Transformer and CNN Helps BA Diagnosis for Doctors in Underdeveloped Areas 16th

Ensemble Model of Visual Transformer and CNN Helps BA Diagno...

引用

16th Asian conference on computer vision (ACCV)

作者： Wei, Zhenghao Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

ISBN: (纸本)9783031270659;9783031270666

The diagnosis of Biliary Atresia (BA) is still complicated and high resource consumed. Though sonographic gallbladder images can be used as an initial detection tool, lack of experienced experts limits BA infants to be treated timely, resulting in liver transplantation or even death. We developed a diagnosis tool by ViT-CNN ensemble model to help doctors in underdeveloped area to diagnose BA. It performs better than human expert (with 88.1% accuracy versus 87.4%, 0.921 AUC versus 0.837), and still has an acceptable performance on severely noised images photographed by smartphone, providing doctors in clinical facilities with outdated Ultrasound instruments a simple and feasible solution to diagnose BA with our online tool

关键词： Biliary atresia Visual transformer Medical image processing Ensemble model

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Integration of vision IoT, AI-based OCR and Blockchain Ledger for Immutable Tracking of Vehicle's Departure and Arrival Times 23

Integration of Vision IoT, AI-based OCR and Blockchain Ledge...

引用

8th international conference on Machine Learning Technologies (ICMLT)

作者： Sichinga, Marumbo Nizeyimana, Eric Gatare, Ignace Nsenga, Jimmy Univ Rwanda African Ctr Excellence Internet Things ACEIoT Kigali Rwanda

ISBN: (纸本)9781450398329

Vehicle based logistics are hinged on their ability to timely deliver goods, services, and people. The classical expression of "time is money" comes alive in the logistics industry yielding potentially huge financial and health consequences in case of missing deadlines. This is especially the case for time sensitive pharmaceuticals, delivery of perishable goods, delivery of people travelling, delivery of services in fault fixing/recovery sector. All these use cases motivate the need for an immutable, secure, and immortalized process of tracking time. To solve this challenge, this paper presents prototype-based research that integrates the 4th industrial revolution technologies of vision Internet of Things (IoT), Artificial Intelligence (AI)-based Optical Character Recognition (OCR) and blockchain. The developed prototype features a Raspberry-PI board embedding a camera, an Artificial Intelligence (AI) model to recognize plate letters from the image and a crypto wallet to sign the logging of plate number and time events on the NEAR blockchain, an emerging sharded, proof-of-stake, layer-one blockchain that is simple to use, secure and scalable. The effective operation of the developed prototype has been validated inside a campus parking and shows an accuracy of 80%. The benefits of transparency, security, and immutability of the blockchain combined with the intelligence, data capture, and processing of IoT will enable to develop accountability solutions trusted by all different logistic stakeholders.

关键词： Web3 Blockchain Dapp NEAR IoT YoloV4 OCR

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Intelligent Robotics and Industrial Applications using computer vision 2023

Intelligent Robotics and Industrial Applications using Compu...

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IS and T international Symposium on Electronic Imaging: Intelligent Robotics and Industrial Applications using computer vision, IRIACV 2023

The proceedings contain 174 papers. The topics discussed include: appearance segmentation and documentation applied to cultural heritage surfaces;end-to-end evaluation of practical video analytics systems for face detection and recognition;testing the role of vision spatial processing in color deficiency;a lightweight exposure bracketing strategy for HDR imaging without access to camera raw;style transfer for improved visualization of underdrawings and ghost paintings: an application to a work by Vincent van Gogh;tangible extended reality with sensor fusion;orca: an end-to-end video object removal framework with cropping interested region and quality assessment;simulation and design of a burst mode 20mfps global shutter high conversion gain CMOS image sensor in a standard 180nm CMOS image sensor process using sequential transfer gates;3D joist perception, detection, and climbing for hexapod robot;synthetic speech attribution using self-supervised audio spectrogram transformer;incidence of stereo blindness in a recent VR distance perception user study;mesh distance for dimension reduction and visualization of numerical simulation data;and teaching color science to EECS students using interactive tutorials: tools and lessons.

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OHMCPD: Optimized Hybrid Deep Learning Model for Classification of Cotton Plant Diseases 3

OHMCPD: Optimized Hybrid Deep Learning Model for Classificat...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Sreeja, S.P. Asha, V. Tressa, Neethu Harish Kumar, P. Jayasurya, R.N. Khatau, Kaberi Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798350343632

The quantity and quality of crop output heavily influences the economic prosperity of the country. Leaf disease detection early on can boost yield and profit. Many image processing methods have been developed to detect leaf diseases. Farmers may find general observations to be time-consuming, expensive, and sometimes unreliable. The image is scaled, converted to grayscale, and denoised by a combination of various Dividend and Weiner filters before undergoing further processing. Another technique for improving images is histogram equalization based on Gaussian mixtures. A thick convolutional neural network is used to assist in image feature recovery. The improved ResNet101 model adapted to Aquila is used here for classification. This model is used to evaluate the accuracy of cotton leaf pest and disease classification. With an accuracy of 99.09%, sensitivity of 97.84%, specificity of 98.10%, precision of 97.92%, and F1 score of 98.68%, the suggested model performs well. © 2023 IEEE.

关键词： Cotton disease DCNN Gaussian Mixture IAO ResNet

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Real Time Object Detection Using Deep Learning 2

Real Time Object Detection Using Deep Learning

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2nd IEEE international conference on vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Sornalakshmi, M. Sakthimohan, M. Elizabeth Rani, G. Aravindhan, V. Surya, B.K. Devadharshni, M. Arulmigu Kalasalingam College of Arts and Science Dept of Computer Applications Tamilnadu India KGiSL Institute of Technology Department of ECE Tamilnadu Coimbatore India

ISBN: (纸本)9798350347982

Real-time object detection using deep learning has emerged as a burgeoning field of study due to its potential for a wide range of applications, including autonomous driving, robotics, and surveillance systems. The primary goal of this method is to identify interesting objects in real-world situations quickly and accurately. By utilizing convolutional brain organizations (CNNs) for highlight extraction and article identification, advanced learning-based strategies have demonstrated exceptional outcomes in this area. CNNs are trained on large-scale image datasets to learn discriminative features that capture object appearance and context effectively. The features extracted by the CNN are then used to detect objects using a detection algorithm. The Region-based Convolutional Neural Network (R-CNN) framework is one popular approach, which first proposes a set of candidate regions and then applies a CNN to each region to extract features for classification and localization. Faster CNN architectures such as Single Shot Detector (SSD) and You Only Look Once (YOLO), as well as hardware acceleration strategies such as graphics processing units (GPUs) and field-programmable gate arrays (FPGAs), have been proposed as ways to improve real-time performance. These methods allow for high frame rates and real-time object detection, making them suitable for a wide range of real-world applications. © 2023 IEEE.

关键词： Object detection

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An AI Decision System to Predict Lung Nodules through Localization from Chest X-ray images 9

An AI Decision System to Predict Lung Nodules through Locali...

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9th international conference on Signal processing and Communication, ICSC 2023

作者： Koyyada, Shiva prasad Singh, Thipendra P. School of Computer Science UPES University Dehradun India uGDXIT Hyderabad India School of Computer Science Engineering and Technology Bennett University NCR Delhi India

ISBN: (纸本)9798350383201

The objective of this research is to design an Artificial Intelligence (AI) decision system that helps medical practitioners diagnose lung disease with more probability. Between the 1970s and 1990s, clinical image analysis was carried out by fusing low-level pixel processing (edge and line detectors, region expansion) with mathematical modeling (fitting lines, circles, and ellipses) to develop compound rule-based systems that dealt with particular tasks. In the 1990s the handwritten recognition task adapted Convolutional neural networks(CNN) and achieved the remarkable success that has laid the road for CNNs. The Chinese of Wuhan, in the Hubei Province, saw an epidemic of the coronavirus illness 2019 (COVID-19), which was unleashed by the SARS-CoV-2. The World Health Organization declared the pandemic a public health emergency of worldwide significance on January 30, 2020. Over three years have passed since the worldwide struggle against the COVID-19 epidemic began. One of the quickest methods to diagnose patients is to use computer-aided diagnosis (CAD) and deep learning techniques on radiography and radiology images. With this, everyone's focus has turned to lung disease detection such as tuberculosis and lung cancer through Chest X-ray images with state-of-the-art methods such as CNNs. However, these fail to explain the reason for making such a decision. This work focuses on this aspect of determining region of interest features with a process of localization while predicting lung nodules in two stages with the help of an explainable artificial method. © 2023 IEEE.

关键词： image segmentation

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