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检索条件"任意字段=International Conference on Computer Graphics, Imaging and Visualization"

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User, customer and consumer experience: Highlighting the heterogeneity in the literature 16

User, customer and consumer experience: Highlighting the het...

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16th international Joint conference on computer Vision, imaging and computer graphics Theory and Applications, VISIGRAPP 2021

作者： Sellier, Quentin Poncin, Ingrid Vanderdonckt, Jean Louvain Research Institute in Management and Organizations UCLouvain Place des Doyens Louvain-la-Neuve Belgium

ISBN: (纸本)9789897584886

The notion of experience has gained in popularity both in management and in computer science. To assess the quality of an information system, specialists in human-computer interaction are now referring to the user experience. On the marketing side, the concept of experience has also become key to describe the relationship between an individual and a brand. Several streams of research exist, some privileging the notion of customer experience and others of consumer experience. However, the multiplication of those works also created fragmentation and theoretical heterogeneity, as emerged through our analyses. This situation is particularly noxious to the communication between the disciplines of human-computer interaction and marketing, becoming more and more necessary. In order to promote this multidisciplinary communication, we clearly define and differentiate the constructs of experience. We also highlight the current heterogeneity in the literature through a systematic literature review and we end by formulating some suggestions to researchers and practitioners. This work contributes to a better communication between the disciplines of human-computer interaction and marketing, and more particularly to the unification of the constructs of experience. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Human computer interaction

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Uav Detect, Track and Follow (DTF) of Non-Stationary Targets in Aerial Thermal Videos

Uav Detect, Track and Follow (DTF) of Non-Stationary Targets...

引用

international conference on computer and Automation Engineering, ICCAE

作者： Shivpal Singh K Sathya Babu Vasit Sagan Chandrakanth Vipparla K Palaniappan Hadi AliAkbarpour Computer Science Department IIIT - Design and Manufacturing Kurnool India Department of Earth Environmental and Geospatial Sciences Taylor Geospatial Institute St. Louis USA Electrical and Computer Science Department University of Missouri Columbia USA Department of Computer Science Saint Louis University St. Louis USA

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Evolution of portable graphics Processing Units (GPUs) have rejuvenated the research on real-time target tracking in aerial systems. These devices are highly optimized in terms of volume, size, power, weight and are effectively employed in UAV based applications. Tracking a target can be broadly divided into two sub-categories: (a) Surveillance and Tracking of the target from a distance and (b) Track and Follow towards the target(e.g. medical and e-commerce deliveries), where the UAV has to intercept the target as closely as possible. Surveillance applications are extensively studied and successfully implemented in real-time. However, present day target follow applications rely completely on the GPS data for intercepting the target. In this work, we propose a novel convolutional neural network (CNN) based target follow in GPS denied environments. The proposed application presents novel challenges in real-time with changes in aspect ratio, target orientation, size of the target in the Field of View (FOV) and track loss. This is further exacerbated in case of thermal systems due to limited information and low thermal contrast ratios between foreground and background. In this work, we address the problem of target follow specifically in thermal videos. We deployed the proposed solution on a portable NVIDIA GPU integrated on a custom developed UAV. The algorithm proposed is successfully tested and validated and the results obtained demonstrate the efficiency of the algorithm for aerial thermal datasets.

关键词： Target tracking Surveillance graphics processing units Infrared imaging Data collection Autonomous aerial vehicles Cameras Real-time systems Convolutional neural networks Global Positioning System

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Using Scrollytelling to Explain Voting Power in Ecuador 21

Using Scrollytelling to Explain Voting Power in Ecuador

引用

14th international Symposium on Visual Information Communication and Interaction, VINCI 2021

作者： Méndez, Gonzalo Gabriel Mendoza, Patricio Escuela Superior Politécnica Del Litoral Guayaquil Ecuador

ISBN: (纸本)9781450386470

We present a scrollytelling visualization that explains the concept of "voting power", which refers to the influence that a group of voters have, relative to the geographical area they live in, on an election's outcome. We explain this in the context of Ecuador, a country with electoral districts of varied sizes and populations (and, thus, varied voting power). Our visualization is designed to explain that a bigger territory does not necessarily imply more votes. Understanding this concept is particularly important in Ecuador, a country that officially depicts elections results through maps that use color hue to indicate the winning candidate of a given geographical area. © 2021 Owner/Author.

关键词： visualization

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A Review: Low-Dose Computed Tomography Image Reconstruction Using Deep Learning Techniques

A Review: Low-Dose Computed Tomography Image Reconstruction ...

引用

Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE international conference on

作者： K. Balakrishna A. Nagaraja Rao School of Computer Science and Engineering VIT Velloore Tamilnadu India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

In Medical Healthcare System the Computed Tomography (CT) scan plays a vital role to generate the visualization of structural details of human body. In clinical procedure, the normal dose CT as well as high dose CT scan can produce high quality image than low dose CT scan, but the human body exposed to the Radiation. The low-dose CT scan can minimize the radiation, but it compromises the quality of image and it may not sufficient to make a decision by radiologist. So, the low-dose CT image has to undergo denoising and reconstruction phases to gain the quality of the image for right diagnosis. Image processing, Deep Learning (DL), and Machine Learning (ML) techniques are had been applied in a diversity of strategies. The aim of this investigation study is to conduct a systematic review of Deep Learning approaches to acquire reconstructions of low dose images in CT scans, identify their limitations, and suggest future research directions.

关键词： Deep learning visualization Computed tomography Noise reduction Medical services Cognition Image reconstruction Medical diagnostic imaging Cybernetics Systematic literature review

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A YOLO-Based Approach for Detection of Olive Knot Disease through UAV and computer Vision Technologies

A YOLO-Based Approach for Detection of Olive Knot Disease th...

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international Carpathian Control conference (ICCC)

作者： Maurício Herche Fófano De Morais João Mendes Murillo Ferreira Dos Santos Fernanda Mara Fernandes José Lima Ana Isabel Pereira Department of Electroelectronics CEFET-MG Leopoldina Brasil IPB Research Centre in Digitalization and Intelligent Robotics (CeDRI) Bragança Portugal Faculty of Minas Gerais FAMINAS Muriaé Brasil

ISBN: (数字)9798331501273

ISBN: (纸本)9798331501280

This work presents an approach for detecting olive knot disease in olive trees, utilizing computer Vision (CV), Unmanned Aerial Vehicle (UAV) based imagery, and Machine Learning (ML) within the context of Precision Agriculture (PA). The study focuses on applying the You Only Look Once (YOLO) deep learning architecture to develop a model capable of identifying trees affected by the disease with accuracy and speed. By integrating UAV technology with object detection algorithms, this approach enables real-time monitoring of olive plantations, supporting early detection and targeted interventions. This study emphasizes the potential of combining drone imaging and ML to drive sustainable and practical solutions in PA. Results show that this method can potentially improve crop management by reducing human labor and contributing to the enhancement of disease control strategies.

关键词： YOLO computer vision visualization Computational modeling Plantations Writing Autonomous aerial vehicles Real-time systems Robustness Diseases

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Evaluating Pre-trained CNN Models for COVID-19 and Pneumonia Diagnosis Using Enhanced Medical imaging

Evaluating Pre-trained CNN Models for COVID-19 and Pneumonia...

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IEEE international conference on Signal Processing, Computing and Control (ISPCC)

作者： Shivam Gangwar Anushka Chaurasia Vaibhav Tiwari Department of Electronics & Communication Engineering University Institute of Engineering & Technology Kurukshetra University Haryana India Computer Science and Engineering National Institute of Technology Shillong Meghalaya India School of Computer Science Engineering and Technology (SCSET) Bennett University Greater Noida India

ISBN: (数字)9798331538934

ISBN: (纸本)9798331538941

The COVID-19 pandemic has underscored the critical need for diagnostic methods that are both rapid and accurate. Chest X-ray (CXR) imaging is a valuable resource for detecting respiratory illnesses, including COVID-19, but its reliance on subjective visual analysis underscores the necessity for automated solutions. This research presents an innovative approach to image enhancement, designed to enhance the effectiveness of deep convolutional neural networks (CNNs) in tasks involving multiple class classifications. To evaluate its effectiveness, five pre-trained models—‘VGG16,’ ‘VGG19,’ ‘ResNet50,’ ‘ResNet101,’ and ‘EfficientNetB0’—have been tested on an enhanced dataset comprising three balanced categories: ’COVID-19’, ’normal’, and ’viral pneumonia’, each with 1,500 X-ray images. The proposed enhancement method addresses challenges such as low contrast and poor visibility, optimizing image quality for improved classification accuracy. Among the evaluated models, EfficientNetB0 demonstrated superior performance, achieving the highest ’precision’, ’accuracy’, ’F1-score’, and ’recall’. The findings highlight the effectiveness of integrating advanced image enhancement with pre-trained models to enable reliable and efficient COVID-19 diagnosis from CXR images.

关键词： COVID-19 visualization Pneumonia Accuracy Computational modeling Signal processing Convolutional neural networks X-ray imaging Image enhancement Biomedical imaging

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Automatic Detection of Epileptic Spikes in Intracerebral EEG with Convolutional Kernel Density Estimation 15

Automatic Detection of Epileptic Spikes in Intracerebral EEG...

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15th international Joint conference on computer Vision, imaging and computer graphics Theory and Applications (VISIGRAPP) / 4th international conference on Human computer Interaction Theory and Applications

作者： Gardy, Ludovic Barbeau, Emmanuel J. Hurter, Christophe Univ Toulouse Ctr Rech Cerveau & Cognit UPS Toulouse France Purpan Hosp CerCo CNRS Toulouse France French Civil Aviat Univ ENAC Ave Edouard Belin Toulouse France

ISBN: (纸本)9789897584022

Analyzing the electroencephalographic (EEG) signal of epileptic patients as part of their diagnosis is a very long and tedious operation. The most common technique used by medical teams is to visualize the raw signal in order to find pathological events such as interictal epileptic spikes (IESs) or abnormal oscillations. More and more efforts are being adopted to try to facilitate the work of doctors by automating this process. Our goal was to analyze signal density fields to improve the visualization and automatic detection of pathological events. We transformed the EEG signal into images on which we applied a convolution filter based on a Kernel Density Estimation (KDE). This method that we propose to call CKDE for Convolutional Kernel Density Estimation allowed the emergence of local density fields leading to a better visualization as well as automatic detection of IESs. Future work will be necessary to make this technique more efficient, but preliminary results are very encouraging and show a high performance compared to a visual inspection of the data or some other automatic detection techniques.

关键词： Electroencephalography EEG Time Series visualization Signal Processing Kernel Density Estimation Convolution Noisy Signal Event Detection Epilepsy Accessibility

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Constructing 3D scenes of scientific visualization using CinemaScience format 31

Constructing 3D scenes of scientific visualization using Cin...

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31st international conference on computer graphics and Vision, GraphiCon 2021

作者： Vasev, Pavel Porshnev, Sergey Forghani, Majid Manakov, Dmitry Bakhterev, Mikhail Starodubtsev, Ilya N.N. Krasovskii Institute Russian Academy of Sciences 16 S.Kovalevskaya str. Ekaterinburg620108 Russia Ural Federal University 19 Mira str. Ekaterinburg620002 Russia

To visualize any new entity, a visualization should be designed and programmed. Investigating approaches for programming new scientific visualizations, we come to the following idea: utilize CinemaScience format to describe 3D scenes. CinemaScience is developed for storing and visualizing supercomputer and physical modelling results, and differs with simplicity both for human and machine. It has a set of interesting features, for example it allows to specify dynamics in views dependent on parameters. However its current known applications are of 2D graphics, and in this paper we extend it for 3D. It’s main idea is to treat Cinema artifacts as visual objects of explicit type. We successfully used the suggested approach in various visualization tasks, examples are presented in the paper. We developed the open-source web application that implements the suggested approach. © 2021 Copyright for this paper by its authors.

关键词： visualization

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Segmentation of agricultural images using vegetation indices 16

Segmentation of agricultural images using vegetation indices

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16th international Joint conference on computer Vision, imaging and computer graphics Theory and Applications, VISIGRAPP 2021

作者： Santos, Jean Fabrício Batista Dias, Jocival Dantas Backes, André Ricardo Escarpinati, Maurício Cunha School of Computer Science Federal University of Uberlândia Brazil

ISBN: (纸本)9789897584886

Identifying and segmenting plants from the background in agricultural images is of great importance for precision agriculture. It serves as a basis for several tasks such as identification of planting lines, identification of weed plants, agricultural automation, among others. Given this importance, in this paper, we evaluated the application of five vegetation indices for RGB images together with two binarization techniques for the plant/background segmentation process. The results showed promising performance in all evaluated indices. It was also possible to identify a relationship between the performance obtained in each index and the capture conditions in each dataset. Copyright © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： Precision agriculture

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Soft Computing Approaches for Medical Diagnosis and Prognosis of Heart Disease with Feature Analysis 2

Soft Computing Approaches for Medical Diagnosis and Prognosi...

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2nd international conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Ravichandran, S. Pradeepa, M. Padmaja, D Lakshmi Neetha, T. Venkatakiran, S. Gitam University School of Technology Department of Computer Science and Engineering Telangana Rudraram Hyderabad502 329 India Vellore Institute of Technology School of Information Technology and Engineering Tamil Nadu Vellore632 014 India Anurag University Department of Information Technology Telangana Hyderabad India Anurag University Department of Artificial Intelligence Telangana Hyderabad India Sri Venkatesa Perumal College of Engineering and Technology Department of Ece Andhra Pradesh Puttur India

ISBN: (纸本)9798350325799

In the field of medical diagnosis and prognosis, particularly for heart disease, deep learning techniques, specifically those utilizing convolutional neural networks (CNNs), have shown significant promise. With a focus on feature analysis to improve accuracy and interpretability, this abstract provides an overview of deep learning models using CNNs for the study of heart disease. This study aims to build reliable CNN-based models that can precisely diagnose and forecast the prognosis of heart disease using medical data. Large datasets containing various cardiac data, including patient demographics, medical imaging scans, and electrocardiograms (ECGs), are used to train the models to accomplish. The proposed deep learning methods heavily rely on feature analysis. The most instructive and pertinent features from the input data are extracted using feature extraction techniques, and these features are then fed into the CNN models. The CNN models are trained to learn complicated patterns and correlations contained in the data, enabling precise categorization and prediction of cardiac disease. Additionally, feature analysis improves the CNN models' interpretability. To comprehend the areas and aspects of the input data that contribute the most to the model's decision-making process, methods including saliency maps, gradient-based visualization, and class activation mapping are used. This improves clinical decision-making by giving doctors and researchers important insights into the diagnostic and predictive variables of cardiac disease. © 2023 IEEE.

关键词： Diagnosis

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