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MOBDrone: A Drone Video Dataset for Man OverBoard Rescue 21st

MOBDrone: A Drone Video Dataset for Man OverBoard Rescue

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

作者： Cafarelli, Donato Ciampi, Luca Vadicamo, Lucia Gennaro, Claudio Berton, Andrea Paterni, Marco Benvenuti, Chiara Passera, Mirko Falchi, Fabrizio CNR Inst Informat Sci & Technol Pisa Italy CNR Inst Clin Physiol Pisa Italy CNR Inst Geosci & Earth Resources Via G Moruzzi 1 I-56124 Pisa Italy

ISBN: (纸本)9783031064302;9783031064296

Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an essential role in speeding up the identification and rescue of people who have fallen overboard, i.e., man overboard (MOB). To this end, Artificial Intelligence techniques can be leveraged for the automatic understanding of visual data acquired from drones. However, detecting people at sea in aerial imagery is challenging primarily due to the lack of specialized annotated datasets for training and testing detectors for this task. To fill this gap, we introduce and publicly release the MOBDrone benchmark, a collection of more than 125K drone-view images in a marine environment under several conditions, such as different altitudes, camera shooting angles, and illumination. We manually annotated more than 180K objects, of which about 113K man overboard, precisely localizing them with bounding boxes. Moreover, we conduct a thorough performance analysis of several state-of-the-art object detectors on the MOBDrone data, serving as baselines for further research.

关键词： Man overboard Object detection Unmanned Aerial Vehicles Drone Benchmark

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Lung Cancer Detection Using Ensemble learning 1st

Lung Cancer Detection Using Ensemble Learning

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1st international conference on Advancements in Interdisciplinary Research, AIR 2022

作者： Fatima, Fayeza Sifat Jaiswal, Arunima Sachdeva, Nitin Indira Gandhi Delhi Technical University for Women Delhi India Galgotia’s College of Engineering and Technology Greater Noida India

ISBN: (纸本)9783031237232

From time to time, lung cancer has appeared in the category of nearly the most lethal maladies since humankind existed. It is even among the most incessant fatalities and major reasons of mortality among all cancers. Lung cancer cases are significantly growing. In India, there are around 70 thousand instances each year. Because the condition is usually asymptomatic in its early stages, it is practically extremely difficult to identify. As a result, early cancer identification is beneficial for preserving lives. Early discovery can improve a patient’s chances of rehabilitation and recovery. Technology has a critical part in accurately identifying cancers. Based on these findings, several researchers have offered various ways. Several Computer-aided diagnostics (CAD) methodologies and systems have been suggested, developed and created in recent years to handle this problem using computer technology. Also, there are some factors like smoking, taking alcohol, anxiety etc. that also helps us to detect if patient is having cancer or not has also been taken into considerations by many researchers. Taking on an assortment of techniques such as machine learning, Ensemble learning and deep learning approaches and numerous ways based on image processing techniques and text information, those systems contribute to a great extent to ascertain the cancer malignancy degree. We had in view integrating or putting together the Ensemble learning algorithms like stacking, blinding, Max voting, boosting and XGBoost through this so as to build an advanced method to assess and scrutinize the out-turn. On comparing Blinding ensemble learning technique proves to be the most efficient technique based on the performance metrics like accuracy, F1 score, precision and recall. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diseases

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Alzheimer's disease detection through Deep learning techniques: A study

Alzheimer's disease detection through Deep learning techniqu...

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international conference on Computational Science and Technology (ICCst)

作者： M. Thamizharasi M. Lakshmi Computer Science and Engineering SRM Institute of Science and Technology Chennai India Data Science and Business System SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9781665476560

Alzheimer's disease is one of the commonly occurring disease in which the common cause of dementia called as memory loss will happen resiliently and damage the brain activity. It also produces cognitive abilities and serious impact of interference with the day today life. Disease contains 60% to 80% of dementia cases in the early stages are predicted and further actions are initiated to alert the patient behaviour from abnormal activity. The direct impact of Alzheimer's disease (AD) interfere with the regular activity and make complexity in making decisions, thinking capability, problem solving and speaking. The evaluation of artificial intelligence (AI) created numerous ways of analysing strategies, helpful for making the early prediction. In spite of image processing technology, machine learning models are created to analyse the disease features, symptoms, Chronic records to perform AD detection. The role of deep learning algorithm incorporated with image processing, deep feature extraction, and deep feature fusion enhances the scope of research in AD analysis. Further the benefit of deep learning algorithm to provide search detection mechanism. The presented study discusses various criteria of AD detection and tabulated the findings.

关键词： Deep learning Analytical models machine learning algorithms Scientific computing Magnetic resonance imaging Interference Feature extraction

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Graph-Based Supervoxel Computation from Iterative Spanning Forest 1st

Graph-Based Supervoxel Computation from Iterative Spanning F...

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

作者： Jeronimo, Carolina Belem, Felipe Carneiro, Sarah A. Patrocinio, Zenilton K. G., Jr. Najman, Laurent Falcao, Alexandre Guimaraes, Silvio Jamil F. Pontificia Univ Catolica Minas Gerais Lab Image & Multimedia Data Sci ImSci BR-31980110 Belo Horizonte MG Brazil Univ Gustave Eiffel ESIEE Paris CNRS LIGM F-77454 Marne La Vallee France Univ Estadual Campinas Lab Image Data Sci LIDS BR-13083852 Campinas Brazil

ISBN: (纸本)9783030766566;9783030766573

Supervoxel segmentation leads to major improvements in video analysis since it generates simpler but meaningful primitives (i.e., supervoxels). Thanks to the flexibility of the Iterative Spanning Forest (ISF) framework and recent strategies introduced by the Dynamic Iterative Spanning Forest (DISF) for superpixel computation, we propose a new graph-based method for supervoxel generation by using iterative spanning forest framework, so-called ISF2SVX, based on a pipeline composed by four stages: (a) graph creation;(b) seed oversampling;(c) IFT-based superpixel delineation;and (d) seed set reduction. Moreover, experimental results show that ISF2SVX is capable of effectively describing the video's color variation through its supervoxels, while being competitive for the remaining metrics considered.

关键词： Graph-based method Supervoxel computation Iterative spanning forest

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1st international Virtual conference on Industry 4.0, IVCI4.0 2020

1st International Virtual Conference on Industry 4.0, IVCI4....

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1st international Virtual conference on Industry 4.0, IVCI4.0 2020

ISBN: (纸本)9789811612435

The proceedings contain 39 papers. The special focus in this conference is on Industry. The topics include: Intelligent Self-Protection Solution Against COVID-19;detecting Suicidal Ideation from Online Texts;Novel Drug Discovery Using Generative Model for COVID-19;structural strength Improvement of 3D Printing Parts from Topology Optimised Design Using Anisotropic Material Modelling;uncovering of Depression Through User Profiling;a study on Work–Life Balance and Marital Satisfaction of Faculty Members;evolutionary Computing-Based Feature Selection for Cardiovascular Disease: A Review;readiness and Maturity Assessment Model to Measure the Industry 4.0 Ecosystem;An Insight on Context-Aware Mobile Application Execution in Mobile Cloud IoT (MCIoT);breast Cancer Detection in Histology images Using Convolutional Neural Network;A Novel Approach on Auto-Scaling for Resource Scheduling Using AWS;A Blockchain-Based COVID-19 Protection Framework;preface;pre-diagnosis, Prediction and Report Generation of a Disease;a state of Art of machine learning Algorithms Applied Over Language Identification and Speech recognition Models;feedback-Based Real—Time Surveillance for MidDay Meal Scheme;compact Planar Retrodirective Antenna with a Rat-Race Coupler for Autonomous Car Applications;digital Watermarking Technique in Transform Domain Using Meta-Heuristic Algorithm;centralized Smart Air Purifier System for Industrial Applications;rapid Forest Cover Detection Using Ensemble learning;analysis of Connectivity Model to study the Neurophysiological Process for Autism Detection;an image processing Based American Sign Language Fingerspelling Interpreter;security for User Profile Matching in Social Networks;AI and ML-Based Crop Detection Application;an Audio-Aided Face and Text recognition System for Visually Impaired;decentralized Classroom Using Blockchain;application of Neuroevolution in Autonomous Cars.

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Disease Detection Based on Nail Color Analysis Using image processing

Disease Detection Based on Nail Color Analysis Using Image P...

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international conference on Computational Science and Technology (ICCst)

作者： D.Sathish Kumar J.Deepthi Sherly S. Indira Priyadharshini Department of CSE Sri Sai Ram Engineering College Chennai Tamilnadu

ISBN: (纸本)9781665476560

There have been countless advancements in medical research, and a wide variety of diagnostic procedures for human body problems have been developed. Pathological testing, on the other hand, is time-consuming, painful, and difficult to employ in illness detection. Currently, the patient's nail is scanned and the image is divided. Experiment results demonstrate a considerable impact. The major drawback of this technique is that variations in blood flow may be detected by looking at the color of the image rather than segmentation. Dermatology studies have shown that nail color changes are a good indicator of a variety of health issues, and our suggested method is to use nail color analysis to look for signs of illness. An abnormality in one's health is indicated by a change in the color of one's nails. Heavy equipment and scanning processes are not necessary to take pictures of human nails for use in detection. In contrast to human sight, computer image processing can distinguish even the tiniest differences in color and hence provides more exact and accurate findings. machine learning algorithms such as the CNN (Convolution Neural Network) algorithm are used in the suggested system, which takes an image and assigns value to distinct items in the picture. Using Picture processing, an image is digitally transformed and a few procedures are carried out to extract valuable information. To identify nail traits, image analysis and digital image processing techniques such picture acquisition, image pre-processing, segmentation, and feature extraction are utilized.. Based on variations in nail color, this web tool allows the doctor to detect diseases at an early stage.

关键词： image segmentation Pathology machine learning algorithms image color analysis Scientific computing Nails Medical services

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Key Landmarks Detection of Cleft Lip-Repaired Partially Occluded Facial images for Aesthetics Outcome Assessment 21st

Key Landmarks Detection of Cleft Lip-Repaired Partially Occl...

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

作者： Bakaki, Paul Richard, Bruce Pereira, Ella Tagalakis, Aristides Ness, Andy Behera, Ardhendu Liu, Yonghuai Edge Hill Univ Fac Arts & Sci Ormskirk L39 4QP Lancs England Birmingham Childrens Hosp Steelhouse Lane Birmingham B4 6NH W Midlands England Univ Bristol British Dent Sch Bristol BS1 2LY Avon England Makerere Univ Dept Comp Sci POB 7062 Kampala Uganda

ISBN: (纸本)9783031064302;9783031064296

This paper proposes a novel method for the detection of the symmetrical axis of the cropped face required for the aesthetic outcome estimation from the facial images of patients after their cleft treatment. It firstly applies the Gaussian filter to smooth the images in order to compress noise on the subsequent tasks, then the bilateral semantic segmentation network is applied to segment the facial components out and each region is assigned a distinct colour, thirdly the Canny edge detector is applied to detect the facial feature points and all the contours are further detected and classified into three thirds according to their height. Fourthly, the centres of mass of detected feature points on the contours and the average of all these centres are used to estimate four potential symmetrical axes of the face, the one with minimum Manhattan distance from all the detected feature points is finally selected as the optimal one and used to estimate the aesthetic numerical score through the shape analysis in structural similarity measure. The experimental results based on a publicly accessible dataset shows that it performs well and better than one existing method.

关键词： Cleft Facial image Aesthetic outcome estimation Symmetrical axis Shape analysis

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Indoor Visible Light Positioning System Based on the image Sensor and CNN-GRU Fusion Neural Network 1st

Indoor Visible Light Positioning System Based on the Image S...

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1st international conference on pattern Analysis and machine Intelligence, ICPAMI 2024

作者： Liu, Guoqing Qin, Ling Du, Yongxing Zhao, Desheng School of Digital and Intelligence Industry Inner Mongolia University of Science and Technology Baotou China

ISBN: (纸本)9789819633487

With the continuous development of artificial information technology, visible light positioning (VLP) based on deep learning has emerged as a hotspot for research on indoor localization technology. To improve the accuracy of indoor positioning, this paper proposes a convolutional neural network-gated recurrent unit (CNN-GRU) fusion neural network-based indoor VLP system. The system uses a single circular LED to transmit ID data and a common smartphone to capture feature images. To obtain light-emitting diode (LED) position information, this system adopts a method of extracting pixel values of the LED projection ellipse’s major axis for demodulation. In the coordinate prediction process, the feature parameters such as the location of the centre of mass, the length of the long and short axes, and the area of the ellipse in the image projection are input into the network for training, and the trained model is used to predict the coordinates of the test set. The results of this experiment show that the average positioning error is 3.79 cm in the 2.25 m × 2.25 m × 2.25 m experimental area, which can meet the positioning requirements of the indoor environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Relevant Tag Extraction Based on image Visual Content 1st

Relevant Tag Extraction Based on Image Visual Content

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1st international conference on Applied Intelligence, ICAI 2023

作者： Fazal, Nancy Fränti, Pasi School of Computing University of Eastern Finland Joensuu Finland

ISBN: (纸本)9789819708260

Social media web services like Flickr allow users to share and freely annotate images with textual tags. These tags play a crucial part for text-based social image retrieval and browsing tasks. However, these tags are usually irrelevant and incomplete which limits their effectiveness and use. One fundamental problem is to interpret the relevance of tags with respect to the image visual content. Existing solutions have targeted either image visual content or user contributed tags separately to address the issue. Our proposed method Tag-tag, however, combines both aspects and exploit their semantic relationship. We use state of the art pretrained machine learning models in Tensorflow for object detection and NLP (Natural Language processing) for semantic analysis. Our experiments on a dataset of 219 randomly collected Flickr images demonstrates the applicability of our method. images with missing tags were excluded from the experiments. We identified two reasons where relevant tag was not found as: (1) relevant tag itself was missing from user contributed tag list, (2) relevant tag got skipped because it was either not found in the Wordnet dictionary or has no pretrained vector in word2vec-google-news-300 model. We identify this as one limitation of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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Skeleton Based Dynamic Hand Gesture recognition using LstM and CNN 2020

Skeleton Based Dynamic Hand Gesture Recognition using LSTM a...

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2nd international conference on image processing and machine Vision / international conference on pattern recognition and machine learning (IPMV)

作者： Ikram, Aaahm Liu, Yue Beijing Inst Technol Sch Opt & Photon Beijing Engn Res Ctr Mixed Real & Adv Display 5 South Zhongguancun St Haidian Beijing Peoples R China CFVE Beijing Film Acad 4 Xitucheng Rd Beijing Peoples R China

ISBN: (纸本)9781450388412

Dynamic Hand Gestures offer a natural, non-verbal way of communication that can substitute other communication modalities like verbal speech and script writing. Not only for the voice command, hand gestures also play significant role in Augmented Reality (AR), Virtual Reality (VR) and games. There are some factors like computational cost, flexibility and recognition accuracy that can impact the incorporation of hand gestures in these fields. In this paper, a Dynamic Hand Gesture recognition (DHGR) approach is propose that is based on Convolutional Neural Network (CNN) and long-short term memory (LstM). This system is trained to execute the sequence of 3D input data along with the velocities and positions information learned from Leap Motion Controller (LMC).When evaluated and compared with state-of-art DHGR methods, this architecture shows relative high accuracy of 98%.

关键词： Dynamic Hand Gestures recognition (DHGR) Leap Motion Controller (LMC) Convolutional Neural Network (CNN)

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