Depth Peeling Algorithm for the Distance Field Computation of Overlapping Objects

2013 ◽  
pp. 99-107
Author(s):  
Marcin Ryciuk ◽  
Joanna Porter-Sobieraj
Keyword(s):  
Distance Field ◽  
Field Computation

Hybrid Distance Field Computation

2001 ◽  
pp. 195-209 ◽  
Author(s):  
Richard Satherley ◽  
Mark W. Jones
Keyword(s):  
Distance Field ◽  
Field Computation ◽  
Hybrid Distance

GPU-Based Feature-Preserving Distance Field Computation

2008 ◽  
Author(s):  
Xiaoxiao Wu ◽  
Xiaohui Liang ◽  
Qidi Xu ◽  
Qinping Zhao
Keyword(s):  
Distance Field ◽  
Field Computation ◽  
Feature Preserving

DiFi: Fast 3D Distance Field Computation Using Graphics Hardware

2004 ◽  
Vol 23 (3) ◽  
pp. 557-566 ◽  
Author(s):  
Avneesh Sud ◽  
Miguel A. Otaduy ◽  
Dinesh Manocha
Keyword(s):  
Graphics Hardware ◽  
Distance Field ◽  
Field Computation

Distance field computation for geological slab surface data sets

2011 ◽  
Vol 14 (4) ◽  
pp. 143-156
Author(s):  
Marek Vančo ◽  
Bernd Hamann ◽  
Oliver Kreylos ◽  
Magali I. Billen ◽  
Margarete A. Jadamec
Keyword(s):  
Data Sets ◽  
Slab Surface ◽  
Distance Field ◽  
Field Computation ◽  
Surface Data

Electric Field Computation with Improved Charge Simulation Method Considering Volume and Surface Resistivities

2011 ◽  
Vol 131 (8) ◽  
pp. 717-718 ◽  
Author(s):  
Hiroyuki Iwabuchi ◽  
Taiki Donen ◽  
Akiko Kumada ◽  
Kunihiko Hidaka
Keyword(s):  
Electric Field ◽  
Simulation Method ◽  
Charge Simulation Method ◽  
Field Computation

scholarly journals Mask-R$$^{2}$$CNN: a distance-field regression version of Mask-RCNN for fetal-head delineation in ultrasound images

2021 ◽  
Author(s):  
Sara Moccia ◽  
Maria Chiara Fiorentino ◽  
Emanuele Frontoni
Keyword(s):  
Fetal Growth ◽  
A Priori ◽  
Absolute Difference ◽  
Ultrasound Images ◽  
Fetal Head ◽  
Mean Absolute Difference ◽  
Distance Field ◽  
Feature Pyramid ◽  
End To End ◽  
Ablation Study

Abstract Background and objectives Fetal head-circumference (HC) measurement from ultrasound (US) images provides useful hints for assessing fetal growth. Such measurement is performed manually during the actual clinical practice, posing issues relevant to intra- and inter-clinician variability. This work presents a fully automatic, deep-learning-based approach to HC delineation, which we named Mask-R$$^{2}$$ 2 CNN. It advances our previous work in the field and performs HC distance-field regression in an end-to-end fashion, without requiring a priori HC localization nor any postprocessing for outlier removal. Methods Mask-R$$^{2}$$ 2 CNN follows the Mask-RCNN architecture, with a backbone inspired by feature-pyramid networks, a region-proposal network and the ROI align. The Mask-RCNN segmentation head is here modified to regress the HC distance field. Results Mask-R$$^{2}$$ 2 CNN was tested on the HC18 Challenge dataset, which consists of 999 training and 335 testing images. With a comprehensive ablation study, we showed that Mask-R$$^{2}$$ 2 CNN achieved a mean absolute difference of 1.95 mm (standard deviation $$=\pm 1.92$$ = ± 1.92  mm), outperforming other approaches in the literature. Conclusions With this work, we proposed an end-to-end model for HC distance-field regression. With our experimental results, we showed that Mask-R$$^{2}$$ 2 CNN may be an effective support for clinicians for assessing fetal growth.

scholarly journals Integer-only cross field computation

2018 ◽  
Vol 37 (4) ◽  
pp. 1-13 ◽  
Author(s):  
Nahum Farchi ◽  
Mirela Ben-Chen
Keyword(s):  
Field Computation
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