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Titlebook: Advances in Visual Computing; 18th International S George Bebis,Golnaz Ghiasi,Luv Kohli Conference proceedings 2023 The Editor(s) (if appli

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發(fā)表于 2025-3-21 19:55:02 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Advances in Visual Computing
期刊簡稱18th International S
影響因子2023George Bebis,Golnaz Ghiasi,Luv Kohli
視頻videohttp://file.papertrans.cn/151/150123/150123.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Advances in Visual Computing; 18th International S George Bebis,Golnaz Ghiasi,Luv Kohli Conference proceedings 2023 The Editor(s) (if appli
影響因子.This volume LNCS 14361?and?14362?constitutes the refereed proceedings of the, 16th International Symposium, ISVC 2023, in October 2023, held at Lake Tahoe, NV, USA..The 42 full papers and 13 poster papers were carefully reviewed and?selected from 120 submissions.?A total of 25 papers were also accepted for?oral? presentation in special tracks from 34 submissions. The following topical sections followed as:.Part 1:?ST: Biomedical Image Analysis Techniques for Cancer Detection,?Diagnosis and Management;?Visualization;?Video Analysis and Event Recognition;?ST: Innovations in Computer Vision & Machine Learning for Critical?& Civil Infrastructures;?ST: Generalization in Visual Machine Learning;?Computer Graphics;?Medical Image Analysis;?Biometrics;?Autonomous Anomaly Detection in Images;?ST: Artificial Intelligence in Aerial and Orbital Imagery;?ST: Data Gathering, Curation, and Generation for Computer Vision?and Robotics in Precision Agriculture..Part 2:?Virtual Reality;?Segmentation;?Applications;?Object Detection and Recognition;?Deep Learning;?Poster.. . . . . . .
Pindex Conference proceedings 2023
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Deep Learning Based GABA Edited-MRS Signal Reconstructiondata from real GABA-edited ground truths. Our model achieves a 95% decrease in Mean Squared Error (MSE), a 70% decrease in Linewidth, a 450% increase in Signal to Noise Ratio (SNR), and a 42% increase in Peak Shape Score compared to the current existing method on the test set. We also illustrate our
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ReFit: A Framework for?Refinement of?Weakly Supervised Semantic Segmentation Using Object Border Fit be used to construct a boundary map, which enables . to predict object locations with sharper boundaries. By applying our method to WSSS predictions, we achieved up to 10% improvement over the current state-of-the-art WSSS methods for medical imaging. The framework is open-source, to ensure that ou
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A Data-Centric Approach for?Pectoral Muscle Deep Learning Segmentation Enhancements in?Mammography Inhance the accuracy of the deep-learning-based mammography segmentation model. In the first stage, we introduce a shape-based label analysis to automatically identify pectoral muscle labels with possible inconsistencies for a posterior manual review and correction. Then, in the second stage, we down
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