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

共 12035 条记录，以下是211-220 订阅

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Hyperspectral Image Analysis Using Maximum Abundance Classification

Hyperspectral Image Analysis Using Maximum Abundance Classif...

引用

international conference on Intelligent Control and Information Processing (ICICIP)

作者： Nian Zhang Sougre-Nonma Stephanie Rouamba Wagdy H. Mahmoud Lara A. Thompson Department of Electrical and Computer Engineering University of the District of Columbia Washington D.C. USA Department of Mechanical Engineering Biomedical Engineering Program University of the District of Columbia Washington D.C. USA

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

This paper presents a satellite hyperspectral image processing method that utilizes a maximum abundance classifier to categorize different regions of hyperspectral images into ground truth classes. First, the class names for each endmember and their corresponding columns in the signature matrix are identified, followed by the visualization of their spectral profiles. Abundance maps for the endmembers are then generated using the fully constrained least squares (FCLS) method. Afterward, the maximum abundance classifier is applied, and the resulting classified image is displayed with color-coded pixels. The abundance maps illustrate the spatial distribution of endmembers across the hyperspectral image, where the abundance values of each pixel represent the proportion of each endmember present. By determining the highest abundance value for each pixel and assigning it to the corresponding endmember class, the pixels within the hyperspectral images are classified. Experimental results demonstrate that the proposed MAC method effectively handles mixed pixels. In addition, it can effectively deal with the mixed pixel problem in hyperspectral images because it identifies components by calculating the abundance values for each pixel rather than relying solely on single spectral features.

关键词： visualization Image segmentation Satellites Graphical models Information processing Hyperspectral imaging Intelligent control Image classification Distribution functions

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Explainable AI-Driven Heart Disease Prediction

Explainable AI-Driven Heart Disease Prediction

引用

Visual Analytics and Data visualization (ICVADV), international conference on

作者： R Bhuvaneswari P Kumar S Kaviya Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The earliest detection is crucial both to prevention and treatment of heart disease, which ranks highly in the list of worldwide health concerns. Data gathering and processing are performed as the initial phase through several health indicators such as blood pressure, cholesterol, and lifestyle factors. All the inputs are processed and computed with the chances of suffering heart disease using a state-of-art model in machine learning. From, this the best performed machine learning model are integrated in the proposed methodology which results in the better accuracy with 83.9%. For the second step, with the help of explainable AI approaches, relevant elements for each prediction of the model are highlighted. Since the performance of the model is assessed in terms of the interpretation and accuracy of its predictions, the third phase comes as the final one, to deliver medical practitioners with an accurate tool that can be put to use for an early diagnosis and helpful insight. The main objective of our approach is to make EAI models in the health care sector more transparent and user-friendly. LIME (Local Interpretable Model-agnostic Explanations) is a popular method in Explainable Artificial Intelligence (EAI) that has been utilized in the proposed work since it interprets complex machine learning models by highlighting the most significant features contributing to a prediction. In the proposed method, LIME provides insights into the features influencing the model's output, such as whether a patient is likely to have heart disease along with the features which contribute to that prediction.

关键词： Heart Accuracy Explainable AI Computational modeling Visual analytics Medical services Predictive models Medical diagnostic imaging Tuning Diseases

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Proceedings of the Seventh IASTED international conference on computer graphics and imaging

Proceedings of the Seventh IASTED International Conference o...

引用

Proceedings of the Seventh IASTED international conference on computer graphics and imaging

The proceedings contain 81 papers from the Proceedings of the Seventh IASTED international conference on computer graphics and imaging. The topics discussed include: interactive graphic script for human motion;human face modeling by fitting a 3D anatomy-based model using dynamic deformable meshes;visual smoke simulation with adaptive octree refinement;automatic gait generation for various stairs;hodograph turtles;initial value specification in motion estimation for keyframe animation;and mesh segmentation using the object skeleton graph.

关键词： Animation

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Plant Leaf Disease Identification Based on Machine and Deep Learning Methods

Plant Leaf Disease Identification Based on Machine and Deep ...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： P. Kalaiarasi R. Ramalakshmi V. Kannan A. Bhuvanesh Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Agriculture Kalasalingam Academy of Research and Education Krishnankoil India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The substantial effects of plant diseases on agriculture, which result in losses in production of crops, financial losses, and food insecurity, highlight the necessity for plant leaf disease detection and categorization. In order to effectively control illnesses, stop them from spreading to other plants, and develop tailored treatment plans, early and precise disease identification is essential. Various strategies are used for plant leaf disease diagnosis, such as manual symptom assessment, computer image processing, and visual examination by skilled professionals. Traditional methods rely on human expertise to visually identify disease symptoms based on characteristic patterns, discolorations, or lesions on plant leaves. These techniques, however, are laborintensive, time-consuming, and sensitive to bias and mistakes. The development of automated and computer-based techniques for the identification of plant leaf disease through the use of ML (machine learning) algorithms and digital imaging technology has garnered increasing attention in recent years. There are still a number of issues with the DL (deep learning) and ML algorithms. In order to identify research gaps and address them in the future while developing a unique framework, this study presents an examination of ML and DL models.

关键词： Deep learning visualization Plant diseases Machine learning algorithms Microorganisms Plants (biology) Production Manuals Medical diagnosis Diseases

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TDMF: Text-Guided Denoising and Interactive Medical Image Fusion

TDMF: Text-Guided Denoising and Interactive Medical Image Fu...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Aimei Dong Jingyuan Xu Long Wang Guohua Lv Guixin Zhao Jinyong Cheng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Qilu University of Technology(Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal image fusion aims to merge features from different modalities to create a comprehensively representative image. However, existing medical image fusion methods often struggle to handle noise generated during image acquisition, significantly diminishing their impact on visual quality. To address these challenges, we propose a semantically text-guided medical image fusion model, named TDMF. Specifically, TDMF guides classical image fusion through textual semantics and effectively coordinates the resolution of degradation and interaction issues during the fusion process. By integrating text encoders and interactive fusion modules, TDMF establishes a unified framework for denoising and interactive fusion of medical images. Extensive experiments have demonstrated that our proposed text-guided image fusion strategy offers significant advantages over state-of-the-art methods in medical image fusion performance.

关键词： visualization Image resolution Semantics Noise reduction Noise Pipelines Noise measurement Speech processing Image fusion Biomedical imaging

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Signbridge-Audio to Sign Language Translator

Signbridge-Audio to Sign Language Translator

引用

IEEE Students' conference on Electrical, Electronics and computer Science (SCEECS)

作者： Kammadanam Shirisha E. Mani Deeksith M. S. S Sri Madhava S. Surendhar R. Karthikeyan Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of CSE(AI&M) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The lack of communication options for Deaf and hearing people, some may say creates a significant social disadvantage in accessing the often-bare essential services. In contrast to acoustically communicated sound patterns, sign language communicates ideas freely through manual and body language. It can be used by those who have trouble speaking, by those who can hear but cannot talk, and by regular people to interact with those with hearing impairments. By automatically converting spoken words into hand gestures, this project creates a web-based interface that allows hearing-impaired individuals to communicate with normal people in real time through sign language interpretation. Two major phases of the system Speech-to-text technology interprets oral input into textual output. Next, the text is run through Natural Language Processing (NLP) algorithms leveraging the Natural language toolkit (NLTK) to be syntactically parsed in the context of sign natural rules. The last phase translates the parsed text into sign language gestures which involve hand shapes, orientation, and body movements to convey the visual meaning of a message. This system could be used to greatly reduce the communication barriers experienced by people with hearing loss and deafness using Machine Learning (ML) to continuously improve accuracy, enhance quality of life, and provide a more inclusive society between the deaf community in our daily interactions within an ever-growing world.

关键词： Hands Sign language visualization Translation Semantics Auditory system Syntactics Rendering (computer graphics) Natural language processing Speech to text

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Sequential Optimization of Healthcare Processes using Reinforcement Learning and Sequential Function Models

Sequential Optimization of Healthcare Processes using Reinfo...

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Automation and Computation (AUTOCOM), international conference on

作者： Malatesh Akkur B.P. Singh Pachayappan R Takveer Singh Binayak Panda Pradeep Marwaha School of Sciences Physics & Electronics JAIN (Deemed to be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Applications (DCA) Presidency College Bengaluru India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The optimization of healthcare processes is so important, and it directly affects outcomes. However, traditional methods like Six Sigma and Lean cannot manage the complexities of complex and dynamic healthcare processes. Thus, alternative solutions must strive to enable the optimization of healthcare services. We intend to use reinforcement learning (RL) and sequential function model (SFM) models combined as a new Sequential optimization framework for healthcare process optimizations in this work. It is a machine learning tool that provides equivalent human actions by deciding to choose certain future moves independently. Results for the second experiment: SFM visually explains process flow and capiases dependency between different steps in a sort order. These two techniques are combined in the proposed framework to learn and optimize healthcare processes sequentially. Sequential optimization: the RL algorithms started learning from previous experiences to optimize the best decisions at different process steps. From there, the SFM provides a visual rendering of this streamlined process. The effect of the process changes can be experimented with via simulation and altered as such. This iterative process improves efficiency in processes and reduces costs to healthcare systems. The proposed framework may transform healthcare processes' optimization and, by extension, improve patient outcomes.

关键词： visualization Sequential analysis Medical services Reinforcement learning Transforms Rendering (computer graphics) Resource management Iterative methods Optimization Six sigma

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IoT Mobile Application for Alzheimer's Disease Caregivers

IoT Mobile Application for Alzheimer's Disease Caregivers

引用

IEEE international conference on Consumer Electronics (ICCE)

作者： Logan Pasternak Srijohn Roy Andrew Farag Monica Rubens Ameek Chadha Sasan Haghani Electrical and Computer Engineering Rutgers University Piscataway NJ USA

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Caring for a friend or family member diagnosed with Alzheimer's disease poses significant challenges due to the necessity for continuous supervision, attentive care, and support. Given the exorbitant expenses associated with professional nursing care, many families with limited financial resources find themselves relying on the unpaid assistance of friends and relatives to fulfill these caregiving responsibilities. Consequently, there is pressing demand for cost-effective health monitoring solutions and systems geared towards enhancing the quality of life for both patients and caregivers alike. This paper addresses this critical need through the development of an iOS mobile application. The application seamlessly integrates various Internet of Things (IoT) technologies, strategically designed to alleviate the burdens of caregiving. Central to its functionality, the application encompasses four key features: medication management, visual short-term memory assessment, facial recognition, and patient location tracking.

关键词： visualization Face recognition Memory management Pressing Medical services Mobile applications Internet of Things Alzheimer's disease Monitoring Medical diagnostic imaging

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TROI: Cross-Subject Pretraining with Sparse Voxel Selection for Enhanced fMRI Visual Decoding

TROI: Cross-Subject Pretraining with Sparse Voxel Selection ...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ziyu Wang Tengyu Pan Zhenyu Li Ji Wu Xiuxing Li Jianyong Wang Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

fMRI (functional Magnetic Resonance imaging) visual decoding involves decoding the original image from brain signals elicited by visual stimuli. This often relies on manually labeled ROIs (Regions of Interest) to select brain voxels. However, these ROIs can contain redundant information and noise, reducing decoding performance. Additionally, the lack of automated ROI labeling methods hinders the practical application of fMRI visual decoding technology, especially for new subjects. This work presents TROI (Trainable Region of Interest), a novel two-stage, data-driven ROI labeling method for cross-subject fMRI decoding tasks, particularly when subject samples are limited. TROI leverages labeled ROIs in the dataset to pretrain an image decoding backbone on a cross-subject dataset, enabling efficient optimization of the input layer for new subjects without retraining the entire model from scratch. In the first stage, we introduce a voxel selection method that combines sparse mask training and low-pass filtering to quickly generate the voxel mask and determine input layer dimensions. In the second stage, we apply a learning rate rewinding strategy to fine-tune the input layer for downstream tasks. Experimental results on the same small sample dataset as the baseline method for brain visual retrieval and reconstruction tasks show that our voxel selection method surpasses the state-of-the-art method MindEye2 with an annotated ROI mask.

关键词： Training visualization Noise Training data Functional magnetic resonance imaging Brain modeling Decoding Labeling Speech processing Optimization

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IASTED international conference on computer graphics and imaging

IASTED International Conference on Computer Graphics and Ima...

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Sixth IASTED international conference on computer graphics and imaging

ISBN: (纸本)0889863768

The proceedings contains 61 papers from the IASTED international conference on computer graphics and imaging. Topics discussed include: fuzzy-logical implementation of co-occurrence rules for combining AUS;human animation via hierarchical feature learning of motion data;augmented reality for viewing articulated structures;geometric refinement algorithms in collision-based electric sculpting;three-dimensional rendering of fractal clouds;fingerprint binarization using convex threshold;object classification and tracking in traffic scenes;and a kind of video image compression algorithm based on bivariable fractal function space.

关键词： Animation

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