1 citations · 1 across the 2 of their papers we have counts for
9 papers · 1 filter
POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring
Reda Abdellah Kamraoui, Vinh-Thong Ta, Nicolas Papadakis +3
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…
Robust superpixels using color and contour features along linear path
Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis
Superpixel decomposition methods are widely used in computer vision and image processing applications. By grouping homogeneous pixels, the accuracy can be increased and the decreas…
SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches
Rémi Giraud, Vinh-Thong Ta, Aurélie Bugeau +2
Superpixels have become very popular in many computer vision applications. Nevertheless, they remain underexploited since the superpixel decomposition may produce irregular and non…
An Optimized PatchMatch for Multi-scale and Multi-feature Label Fusion
Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis +4
Automatic segmentation methods are important tools for quantitative analysis of Magnetic Resonance Images (MRI). Recently, patch-based label fusion approaches have demonstrated sta…
Evaluation Framework of Superpixel Methods with a Global Regularity Measure
Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis
In the superpixel literature, the comparison of state-of-the-art methods can be biased by the non-robustness of some metrics to decomposition aspects, such as the superpixel scale.…
SCALP: Superpixels with Contour Adherence using Linear Path
Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis
Superpixel decomposition methods are generally used as a pre-processing step to speed up image processing tasks. They group the pixels of an image into homogeneous regions while tr…