Learning diffusion on global graph: a PDE-directed approach for feature detection on geometric shapes

DOI10.1016/j.cagd.2019.04.020zbMath1505.65128OpenAlexW2950031164WikidataQ127683122 ScholiaQ127683122MaRDI QIDQ2010320

Ziqiao Guan, Nannan Li, Shengfa Wang, Hong Qin, Risheng Liu, Zhong-xuan Luo, Zhi-Xun Su

Publication date: 27 November 2019

Published in: Computer Aided Geometric Design (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cagd.2019.04.020

zbMATH Keywords

submodularityfeature detectionpartial differential equations (PDEs)global graphsmall-sample learning

Mathematics Subject Classification ID

Numerical aspects of computer graphics, image analysis, and computational geometry (65D18)

Cites Work

• Nonlinear total variation based noise removal algorithms
• Multi-scale mesh saliency based on low-rank and sparse analysis in shape feature space
• Partial Shape Matching Without Point-Wise Correspondence
• Best Algorithms for Approximating the Maximum of a Submodular Set Function
• Mesh saliency via spectral processing
• Unnamed Item

This page was built for publication: Learning diffusion on global graph: a PDE-directed approach for feature detection on geometric shapes