Search Results for author:
Found 26 papers, 1 papers with code
POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring
Semi-supervised learning (SSL) uses unlabeled data to compensate for the scarcity of annotated images and the lack of method generalization to unseen domains, two usual problems in medical segmentation tasks.
Learning local regularization for variational image restoration
In this work, we propose a framework to learn a local regularization model for solving general image restoration problems.
On the Existence of Optimal Transport Gradient for Learning Generative Models
Training of WGAN relies on a theoretical background: the calculation of the gradient of the optimal transport cost with respect to the generative model parameters.
GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays
The creation of which is a heavily expensive and time consuming task, and especially imposes a great challenge for a novel disease.
Wasserstein Generative Models for Patch-based Texture Synthesis
First, a pixel-based optimization method is proposed, relying on discrete optimal transport.
Multi-task deep learning for image segmentation using recursive approximation tasks
The subproblems are handled by a framework that consists of 1) a segmentation task that learns from pixel-level ground truth segmentation masks of a small fraction of the images, 2) a recursive approximation task that conducts partial object regions learning and data-driven mask evolution starting from partial masks of each object instance, and 3) other problem oriented auxiliary tasks that are trained with sparse annotations and promote the learning of dedicated features.
Block based refitting in $\ell_{12}$ sparse regularisation
This is done through the use of refitting block penalties that only act on the support of the estimated solution.
Variational Osmosis for Non-linear Image Fusion
We propose a new variational model for non-linear image fusion.
GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision
The task of classifying X-ray data is a problem of both theoretical and clinical interest.
Learning to segment microscopy images with lazy labels
In this paper, we introduce a deep convolutional neural network for microscopy image segmentation.
When Labelled Data Hurts: Deep Semi-Supervised Classification with the Graph 1-Laplacian
We consider the task of classifying when a significantly reduced amount of labelled data is available.
Evaluation Framework of Superpixel Methods with a Global Regularity Measure
To measure the regularity aspect, we propose a new global regularity measure (GR), which addresses the non-robustness of state-of-the-art metrics.
Robust Shape Regularity Criteria for Superpixel Evaluation
Regular decompositions are necessary for most superpixel-based object recognition or tracking applications.
SCALP: Superpixels with Contour Adherence using Linear Path
In this paper, we propose a fast method to compute Superpixels with Contour Adherence using Linear Path (SCALP) in an iterative clustering framework.
An Optimized PatchMatch for Multi-scale and Multi-feature Label Fusion
On the EADC-ADNI dataset, we compare the hippocampal volumes obtained by manual and automatic segmentation.
SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches
Superpixels have become very popular in many computer vision applications.
Robust superpixels using color and contour features along linear path
During the decomposition, we propose to consider color features along the linear path between the pixel and the corresponding superpixel barycenter.
Superpixel-based Color Transfer
In this work, we propose a fast superpixel-based color transfer method (SCT) between two images.
Texture-Aware Superpixel Segmentation
Most superpixel algorithms compute a trade-off between spatial and color features at the pixel level.
Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification
In this paper, we present a graph-based semi-supervised framework for hyperspectral image classification.
Parameter Estimation in Finite Mixture Models by Regularized Optimal Transport: A Unified Framework for Hard and Soft Clustering
The proposed framework unifies hard and soft clustering methods for general mixture models.
Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence
Though, it is of importance when tuning the regularization parameter as it allows fixing an upper-bound on the grid for which the optimal parameter is sought.
Regularized Optimal Transport and the Rot Mover's Distance
This paper presents a unified framework for smooth convex regularization of discrete optimal transport problems.
Convex Histogram-Based Joint Image Segmentation with Regularized Optimal Transport Cost
We investigate in this work a versatile convex framework for multiple image segmentation, relying on the regularized optimal mass transport theory.
Convex Color Image Segmentation with Optimal Transport Distances
This work is about the use of regularized optimal-transport distances for convex, histogram-based image segmentation.
Regularized Discrete Optimal Transport
The resulting transportation plan can be used as a color transfer map, which is robust to mass variation across images color palettes.
