1 code implementation • 24 Dec 2023 • Kangning Cui, Ruoning Li, Sam L. Polk, Yinyi Lin, Hongsheng Zhang, James M. Murphy, Robert J. Plemmons, Raymond H. Chan
However, the high dimensionality, presence of noise and outliers, and the need for precise labels of HSIs present significant challenges to HSIs analysis, motivating the development of performant HSI clustering algorithms.
no code implementations • 22 Nov 2023 • Yanqi Cheng, Lipei Zhang, Zhenda Shen, Shujun Wang, Lequan Yu, Raymond H. Chan, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
In this work, we introduce Single-Shot PnP methods (SS-PnP), shifting the focus to solving inverse problems with minimal data.
no code implementations • 21 Nov 2023 • Zhenda Shen, Yanqi Cheng, Raymond H. Chan, Pietro Liò, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
Implicit neural representations (INRs) have garnered significant interest recently for their ability to model complex, high-dimensional data without explicit parameterisation.
1 code implementation • 22 Feb 2023 • Wei Tang, Kangning Cui, Raymond H. Chan
Diabetic retinopathy (DR) is a leading global cause of blindness.
no code implementations • 14 Feb 2023 • Dong Li, Shuisheng Zhou, Tieyong Zeng, Raymond H. Chan
Specifically, CM can obtain the optimal merging and estimate the correct k. By integrating these two techniques with K-Means algorithm, the proposed MCKM is an efficient and explainable clustering algorithm for escaping the undesirable local minima of K-Means problem without given k first.
1 code implementation • 19 Jun 2022 • Kangning Cui, Seda Camalan, Ruoning Li, Victor P. Pauca, Sarra Alqahtani, Robert J. Plemmons, Miles Silman, Evan N. Dethier, David Lutz, Raymond H. Chan
Artisanal and Small-scale Gold Mining (ASGM) is an important source of income for many households, but it can have large social and environmental effects, especially in rainforests of developing countries.
no code implementations • 28 Apr 2022 • Kangning Cui, Ruoning Li, Sam L. Polk, James M. Murphy, Robert J. Plemmons, Raymond H. Chan
DSIRC then locates high-density, high-purity pixels far in diffusion distance (a data-dependent distance metric) from other high-density, high-purity pixels and treats these as cluster exemplars, giving each a unique label.
no code implementations • 20 Apr 2022 • Raymond H. Chan, Ruoning Li
We demonstrate the superiority of our method against three state-of-the-art algorithms on six benchmark hyperspectral data sets with 10 to 50 training labels for each class.
1 code implementation • 29 Mar 2022 • Ruoning Li, Kangning Cui, Raymond H. Chan, Robert J. Plemmons
In this work, a novel algorithm called SVM with Shape-adaptive Reconstruction and Smoothed Total Variation (SaR-SVM-STV) is introduced to classify hyperspectral images, which makes full use of spatial and spectral information.
no code implementations • 17 Apr 2017 • Rui Zhao, Raymond H. Chan
Then a low-rank model is used to construct the reference frame in high-resolution by incorporating the information of the low-resolution frames.
no code implementations • 2 Jul 2014 • Haixia Liu, Raymond H. Chan, Yuan YAO
Then a forward stage-wise rank boosting is used to select a small set of features for more accurate classification so that van Gogh paintings are highly concentrated towards some center point while forgeries are spread out as outliers.