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Real-Time Respiration Monitoring of Neonates from Thermography images Using Deep learning 21st

Real-Time Respiration Monitoring of Neonates from Thermograp...

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

作者： Lyra, Simon Gross-Weege, Ines Leonhardt, steffen Lueken, Markus Rhein Westfal TH Aachen Helmholtz Inst Biomed Engn Chair Med Informat Technol Aachen Germany

ISBN: (纸本)9783031064272;9783031064265

In this work, we present an approach for non-contact automatic extraction of respiration in infants using infrared thermography video sequences, which were recorded in a neonatal intensive care unit. The respiratory signal was extracted in real-time on low-cost embedded GPUs by analyzing breathing-related temperature fluctuations in the nasal region. The automatic detection of the patient's nose was performed using the Deep learning-based YOLOv4-Tiny object detector. Additionally, the head was detected for movement tracking. A leave-one-out cross validation showed a mean intersection over union of 79% and a mean average precision of 93% for the detection algorithm. Since no clinical reference was provided, the extracted respiratory activity was validated for video sequences without motion artifacts using Farneback's Optical Flow algorithm. A mean MAE of 8.5 breaths per minute and a mean F-1-score of 80% for respiration detection were achieved. The model inference on NVIDIA Jetson modules showed a performance of 32 fps on the Xavier NX and 62 fps on the Xavier AGX. These outcomes showed promising results for the real-time extraction of respiratory activity from thermography recordings of neonates using Deep learning-based techniques on embedded GPUs.

关键词： Deep learning Camera-based respiration monitoring NICU Infrared thermography

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Traffic Congestion Management based on Vehicle Density Using image processing Techniques 1

Traffic Congestion Management based on Vehicle Density Using...

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1st international conference on Futuristic Technologies, INCOFT 2022

作者： Sadrisjain, B.V. Anushree, T.B. Manohar, N. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru Campus India

ISBN: (纸本)9781665450461

India is one of the world's most densely populated country, with more vehicles being used in urban areas. The ability to follow or track individual vehicles as well as public transport frameworks is useful. We can address complex challenges with effective results using computer vision and machine learning. The major purpose of our project is to deal with the concept of real-time-vehicle tracking is based on video stream by a surveillance camera to track vehicles. We would focus on the type of vehicle i.e., car or bike, Firstly, the software would slice the frames from the streaming video so that would be easier to detect the attributes precisely. Secondly, slice the area of the required part, for instance if we take the frame, the car in it would be cropped for focusing on the required output as correctly as possible. Lastly, the software would store the required data in the database for future usage. © 2022 IEEE.

关键词： Traffic congestion

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Landmark-Guided Conditional GANs for Face Aging 21st

Landmark-Guided Conditional GANs for Face Aging

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

作者： Huang, Xin Gong, Minglun Mem Univ Newfoundland St John NL A1C 5S7 Canada Univ Guelph Guelph ON N1G 2W1 Canada

ISBN: (纸本)9783031064272;9783031064265

Face aging, which alters a person's facial photo to the appearance at a different age, is a popular topic in multimedia applications. Recently, conditional Generative Adversarial Networks (cGANs) have achieved visually impressive progress in this area. However, generating a convincing aging appearance while preserving the person's identity is still a challenging task. In this paper, we propose a novel Landmark-Guided cGAN (LGcGAN), which not only synthesizes texture changes related to aging, but also alters facial structures accordingly. We adapt a built-in attention mechanism to emphasize the most discriminative regions relevant to aging and minimize changes that affect personal identity and background. Conditioned with age vectors, the primal cGAN in our symmetric network converts input faces to target ages, and the dual cGAN inverts the previous task, which feeds synthesized target faces back to the original input age scope for enhancing age consistency. Both qualitative and quantitative experiments show that our method can generate appealing results in terms of image quality, personal identity, and age accuracy.

关键词： Face aging Conditional GANs Dual-learning Landmark attention

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Advanced machine learning Islanding Detection Scheme in PV Grid Tied System by Scalogram image Based Features

Advanced Machine Learning Islanding Detection Scheme in PV G...

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Green Industrial Electronics and Sustainable Technologies (GIEst), IEEE international conference on

作者： Appurva Rajendra Kapil Kumaresh Pal Subhadip Goswami Pranendu Manna Bumba Nandy Iram Rahmat Kadri Department of Information Technology Aditya Silver Oak Institute of Technology Silver Oak University Ahmedabad India Department of Electrical Engineering National Institute of Technology Jamshedpur India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and Technology Hyderabad India Department of Electrical Engineering Sanaka Educational Trust’s Group of Institution Durgapur India Department of Electrical Engineering Techno India University Kolkata India Department of Electrical Engineering PES College of Engineering Aurangabad India

ISBN: (数字)9798331540142

ISBN: (纸本)9798331540159

The rapid growth of Distributed Generation (DG) is driven by the persistent need for electricity. Over time, nearly all DG sources are sustainable. Islanding is a significant issue that arises when there is an excessive integration of photovoltaic (PV) inverter-based DG sources. Islanding poses a potential risk to both the clients and their equipment. This study presents a novel approach using machine learning (ML) classifiers to detect islanding by categorising images. In order to distinguish between non-islanding and islanding events, images are utilised to generate the histogram of orientated gradient (HOG) features. The scalogram images are obtained from the time-series signal derived from the point of common coupling (PCC). The Histogram of Orientated Gradients (HOG) algorithm is employed to extract the distinctive characteristics from the images. The generated feature vector is then utilised as an input for the ML classifier during both the training and testing phases. The scalogram image is generated by utilising the total harmonics distortion (THD) characteristic of the voltage. We are assessing the efficacy of the ML classifier through the utilisation of 5-fold cross-validation. The classification results demonstrate that employing a ML classifier with the HOG feature for islanding detection when classifying images yields excellent outcomes and low validation error.

关键词： Support vector machines Training Histograms Visualization Total harmonic distortion machine learning Voltage Feature extraction Vectors pattern recognition

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A Systematic Review on Cotton Plant Disease Detection & Classification Using machine & Deep learning Approach

A Systematic Review on Cotton Plant Disease Detection & Clas...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER international conference on

作者： Umashankar Dewangan R H Talwekar Swagota Bera Department of Electronics & Telecomm. Engineering Government Engineering College Bilaspur India Department of Electronics & Telecomm. Engineering Government Engineering College Raipur India Department of Computer Science & Engineering Shri Shankaracharya Technical Campus Bhilai India

Crop diseases, which have a negative impact on food supply as well as economic losses, have a significant impact on crop production and quality yields. In most rural areas of India, agriculture is the main profession. Therefore, it is crucial to use cutting-edge and precise computer vision-based algorithms for automatic crop disease identification and categorization so that prompt prophylactic steps may be advised. Numerous computer vision-based strategies have been reviewed in the literature, along with their benefits and drawbacks. These techniques use a variety of machine learning, deep learning, CNN, and image processing methods. In this study, we thoroughly analyzed recent research studies on the identification and categorization of various cotton plant diseases, conducted by a variety of academics and researchers, and summarized them based on crucial factors, such as the deep learning/machine learning algorithm used, the dataset used for experiments, performance matrices, types of disease detected and classified, and the highest accuracy achieved by the model. As per the analysis carried out, Custom CNN technique along with ResNet 50 / GoogLeNet provides maximum accuracy of 98.53%. In general, the average accuracy of deep learning model is quite high as compare to machine learning model.

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Biometric Facial Driven Digital Attendance System using machine learning Approach Based on Haar Cascade Technique

Biometric Facial Driven Digital Attendance System using Mach...

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Cognitive, Green and Ubiquitous Computing (IC-CGU), international conference on

作者： Apala Mahana Nikhil Agrawal Anup Kumar Sahu Satyabrat Sahoo Sohan Kumar Pande Ashish Singh Saluja Computer Science and Engineering Silicon Institute of Technology Sambalpur Odisha India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

It becomes exceedingly difficult to keep an eye on every student's whereabouts in huge classes. Although biometric technologies, like fingerprint scanning, provide a means of managing attendance, they also present certain practical challenges. There are often long wait times for students as they wait their turn to undertake the process of scanning, thereby exacerbating inefficiencies. An inventive method that makes use of deep learning's capabilities-more especially, Convolutional Neural Networks (CNNs)-has been developed to overcome these innate difficulties. The procedure of identifying students in a classroom setting is made easier by this advanced technology. To further this technical progress, a Python programming framework-based Graphical User Interface (GUI) with a focus on user needs has been created. This tasteful fusion of cutting-edge technology with an approachable design philosophy aims to improve the effectiveness and efficiency of attendance tracking.

关键词： Technological innovation Leadership Philosophical considerations machine learning algorithms image processing Organizations Ubiquitous computing

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Proceedings of the 1st international conference on Advances in Computing and Future Communication Technologies, ICACFCT 2021

Proceedings of the 1st International Conference on Advances ...

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1st international conference on Advances in Computing and Future Communication Technologies, ICACFCT 2021

ISBN: (纸本)9781665420761

The proceedings contain 50 papers. The topics discussed include: performance evaluation of a hybrid medium access control protocol for machine-to-machine networks;metamorphic cryptography using 8-way dynamic DNA sequencing & KIMLA method;tomato leaf disease detection and classification based on deep convolutional neural networks;medical data classification using velocity enhanced whale optimization algorithm;crop disease classification through image processing and machine learning techniques using leaf images;COVID-19 diagnosis from cough acoustics using convnets and data augmentation;aspect based sentiment analysis using rule based approach;a comparatively study of machine learning approaches to predict service of disease hemophilia a;analysis of various smart traffic lighting systems;and identification of anxiety and depression using DASS-21 questionnaire and machine learning.

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Combining Deep learning and Mathematical Morphology for Historical Map Segmentation 1st

Combining Deep Learning and Mathematical Morphology for Hist...

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1st IAPR international Joint conference on Discrete Geometry and Mathematical Morphology (DGMM)

作者： Chen, Yizi Carlinet, Edwin Chazalon, Joseph Mallet, Clement Dumenieu, Bertrand Perret, Julien EPITA EPITA Res & Dev Lab LRDE Le Kremlin Bicetre France Univ Gustave Eiffel LaSTIG IGN ENSG St Mande France EHESS CRH LaDeHiS Paris France

ISBN: (纸本)9783030766566;9783030766573

The digitization of historical maps enables the study of ancient, fragile, unique, and hardly accessible information sources. Main map features can be retrieved and tracked through the time for subsequent thematic analysis. The goal of this work is the vectorization step, i.e., the extraction of vector shapes of the objects of interest from raster images of maps. We are particularly interested in closed shape detection such as buildings, building blocks, gardens, rivers, etc. in order to monitor their temporal evolution. Historical map images present significant pattern recognition challenges. The extraction of closed shapes by using traditional Mathematical Morphology (MM) is highly challenging due to the overlapping of multiple map features and texts. Moreover, state-of-the-art Convolutional Neural Networks (CNN) are perfectly designed for content image filtering but provide no guarantee about closed shape detection. Also, the lack of textural and color information of historical maps makes it hard for CNN to detect shapes that are represented by only their boundaries. Our contribution is a pipeline that combines the strengths of CNN (efficient edge detection and filtering) and MM (guaranteed extraction of closed shapes) in order to achieve such a task. The evaluation of our approach on a public dataset shows its effectiveness for extracting the closed boundaries of objects in historical maps.

关键词： Deep learning Convolutional neural networks Mathematical morphology Historical map segmentation Object extraction

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3D Key-Points Estimation from Single-View RGB images 21st

3D Key-Points Estimation from Single-View RGB Images

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

作者： Zohaib, Mohammad Taiana, Matteo Padalkar, Milind Gajanan Del Bue, Alessio Italian Inst Technol Pattern Anal & Comp Vis PAVIS Genoa Italy Univ Genoa Dept Marine Elect Elect & Telecommun Engn Genoa Italy

ISBN: (纸本)9783031064302;9783031064296

The paper presents an end-to-end approach that leverages images for estimating an ordered list of 3D key-points. Most of the existing methods either use point clouds or multiple RGB/depth images to estimate 3D key-points, whereas the proposed approach requires only a single-view RGB image. It is based on three steps: extracting latent codes, computing pixel-wise features, and estimating 3D key-points. It also computes a confidence score of every key-point that enables it to predict a different number of key-points based on an object's shape. Therefore, unlike existing approaches, the network can be trained to address several categories at once. For evaluation, we first estimate 3D key-points for two views of an object and then use them for finding a relative pose between the views. The results show that the average angular distance error of our approach (6.39 degrees) is 8.01 degrees lower than that of KP-Net (14.40 degrees) [1].

关键词： 3D Key-points Single-view RGB images Pose estimation

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Deep learning image Transfer by Simulation 1

Deep Learning Image Transfer by Simulation

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1st IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-stA 2021

作者： Ammar, Abdussalam Masaud Youssef Ellafi, Amira Bozed, Kenz A. Zerek, Amer R. Faculty of Electronic Technology Tripoli Libya Benghazi University Faculty of Information Technology Computer System Design Dept. Benghazi Libya Zawia University Faculty of Engineering Ee Department Zawia Libya

ISBN: (纸本)9781665418560

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning with Neural style transfer that has been a high risk application for deep learning, make attention from and advertising the effectiveness to both the academic prisons and the general public. However, we have found by removal experiments that optimizing an image in the way neural style transfer does, we can even factor out the deepness (multiple layers of exchange linear and nonlinear transformations) all together and have neural style transfer working to a certain range. We present the VGG-19 Artistic Type Neural Algorithm, which can convert and recombine the image quality and style of natural images. This algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous well known portrait. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks (CNN) and demonstrate their potential for high level image synthesis and manipulation. © 2021 IEEE.

关键词： image classification

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