Search Results for author:
Found 7 papers, 4 papers with code
Spectral Clustering of Categorical and Mixed-type Data via Extra Graph Nodes
This paper explores a more natural way to incorporate both numerical and categorical information into the spectral clustering algorithm, avoiding the need for data preprocessing or the use of sophisticated similarity functions.
Examining Pathological Bias in a Generative Adversarial Network Discriminator: A Case Study on a StyleGAN3 Model
Generative adversarial networks (GANs) generate photorealistic faces that are often indistinguishable by humans from real faces.
Patch-Based Deep Unsupervised Image Segmentation using Graph Cuts
In this work, we propose a patch-based unsupervised image segmentation strategy that bridges advances in unsupervised feature extraction from deep clustering methods with the algorithmic help of classical graph-based methods.
Using Neural Networks for Fast SAR Roughness Estimation of High Resolution Images
We show that this approach leads to an estimator that is quicker, yields less estimation error and is less prone to failures than the traditional estimation procedures for this problem, even when we use a simple network.
Improving Log-Cumulant Based Estimation of Roughness Information in SAR imagery
Synthetic Aperture Radar (SAR) image understanding is crucial in remote sensing applications, but it is hindered by its intrinsic noise contamination, called speckle.
A Sparse Graph Formulation for Efficient Spectral Image Segmentation
In this paper, we adopt a sparse graph formulation based on the inclusion of extra nodes to a simple grid graph.
Estimating Appearance Models for Image Segmentation via Tensor Factorization
Image Segmentation is one of the core tasks in Computer Vision and solving it often depends on modeling the image appearance data via the color distributions of each it its constituent regions.
