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Thresholding

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13 documents available in our comprehensive collection of Thresholding resources. Find practical guides, tutorials, and documentation to enhance your knowledge.

Preview of A boosting-inspired personalized thresholding method for sepsis screening
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A boosting-inspired personalized thresholding method for sepsis screening (p_FGKM_2019.pdf)

1.49 MBEN 22 pages
Researchers propose a personalized threshold method for sepsis screening, inspired by boosting, a machine learning technique.
Preview of Contact Mechanics
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Contact Mechanics (1_s2.0_S2352573821000202_mmc1.pdf)

16.43 MBEN 2 pages
Contact area is measured from microscopy images using ImageJ with 'Otsu' or 'Minimum' thresholding, converting contact pixels to real area using a pre-measured
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Manuscript (1507.08377v1.pdf)

632 KBEN 48 pages
We proposed a general Principal Orthogonal complEment Thresholding (POET) framework for large-scale covariance matrix estimation based on an approximate factor
Preview of 1-Bit Compressed Sensing
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1-Bit Compressed Sensing (221_main_paper.pdf)

563 KBEN 10 pages
One-step hard thresholding solves the non-convex program efficiently
Preview of SF-2014-38 Abstract
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SF-2014-38 Abstract (SF-2014-38-Abstract.pdf)

201 KBM Abdul RahmanEN 1 page
A Comparison Based Survey on Image Segmentation Techniques: Image segmentation is crucial in computer vision, involving feature extraction for analysis,...
Preview of TV-L1 Flow
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TV-L1 Flow (article_lr.pdf)

662 KBJavier Sánchez, Enric Meinhardt-Llopis, Gabriele FaccioloEN 14 pages
TV-L1 optical flow estimation is based on minimizing a functional with a data term using the L1 norm and a regularization term using the total variation of the...
Preview of Traffic Control System
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Traffic Control System (E6139018520.pdf)

583 KBshabnam shaikEN 5 pages
A system for vehicle detection and traffic signal control using image processing is proposed, which captures images from a webcam, calculates traffic density,...
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Technical Report (Fundana-Heyden-Gosch-Schnoerr-2008.pdf)

414 KBEN 4 pages
A novel prior-based variational object segmentation method is proposed, which unifies image segmentation and denoising in a global minimization framework.
Preview of Download (1MB)
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Download (1MB) (1-s2.0-S1574954123001565-main.pdf)

5.05 MBMartin JungEN 10 pages
An integrated species distribution modelling framework is presented to address the challenge of heterogeneous biodiversity data.
Preview of Modeling and Measuring Expressed (Dis)belief in (Mis)information
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Modeling and Measuring Expressed (Dis)belief in (Mis)information (Jiangetal2020ICWSM.pdf)

485 KBEN 12 pages
Researchers propose using social media comments to model and measure public belief in online misinformation, aiming to quantify susceptibility and mitigate potential societal impacts.
Preview of Comparing Traditional and Deep Learning Methods for Defect Detection in XCT Images
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Comparing Traditional and Deep Learning Methods for Defect Detection in XCT Images (contribution_117_final.pdf)

2.37 MBYosifov, Miroslav; Weinberger, Patrick; Reiter, Michael; Beenhouwer, Jan De; Sijbers, Jan; Kastner,EN 10 pages
Defect detectability analysis compares traditional and deep learning methods in numerical simulations for X-ray computed tomography (XCT) in non-destructive testing (NDT).
Preview of Neural Networks Computations with DOMINATION Functions
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Neural Networks Computations with DOMINATION Functions (etr151.pdf)

250 KBEN 6 pages
Researchers Kordag Mehmet Kilic and Jehoshua Bruck propose a novel neural network representation using DOMINATION functions, demonstrated through unweighted bipartite graphs connected to universal gates. They derive circuit-size bounds for these functions, showing that sparser...
Preview of Independent Component Analysis as a Preprocessing step for Data Compression of Neonatal EEG
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Independent Component Analysis as a Preprocessing step for Data Compression of Neonatal EEG (Bogdan_Mijovic_IEEE_EMBC.pdf)

385 KBEN 5 pages
Independent Component Analysis (ICA) is used as a preprocessing step to compress neonatal EEG data, reducing reconstruction time by 33%-50% and enhancing compression rates by 33%. This method assumes the EEG data is a linear mixture of few source signals that are nearly-sparse...

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