Search Results for author:
Found 11 papers, 4 papers with code
Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering
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.
Single-Shot Plug-and-Play Methods for Inverse Problems
In this work, we introduce Single-Shot PnP methods (SS-PnP), shifting the focus to solving inverse problems with minimal data.
TRIDENT: The Nonlinear Trilogy for Implicit Neural Representations
Implicit neural representations (INRs) have garnered significant interest recently for their ability to model complex, high-dimensional data without explicit parameterisation.
A Global and Patch-wise Contrastive Loss for Accurate Automated Exudate Detection
Diabetic retinopathy (DR) is a leading global cause of blindness.
Multi-Prototypes Convex Merging Based K-Means Clustering Algorithm
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.
Semi-supervised Change Detection of Small Water Bodies Using RGB and Multispectral Images in Peruvian Rainforests
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.
Unsupervised Spatial-spectral Hyperspectral Image Reconstruction and Clustering with Diffusion Geometry
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.
A 3-stage Spectral-spatial Method for Hyperspectral Image Classification
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.
Classification of Hyperspectral Images Using SVM with Shape-adaptive Reconstruction and Smoothed Total Variation
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.
A Nuclear-norm Model for Multi-Frame Super-Resolution Reconstruction from Video Clips
Then a low-rank model is used to construct the reference frame in high-resolution by incorporating the information of the low-resolution frames.
Geometric Tight Frame based Stylometry for Art Authentication of van Gogh Paintings
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.
