2 citations · 5 across the 13 of their papers we have counts for
10 papers · 1 filter
AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation
Pierrick Coupé, Boris Mansencal, Michaël Clément +5
Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images.…
AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation
Pierrick Coupé, Boris Mansencal, Michaël Clément +5
Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images.…
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…
Robust Shape Regularity Criteria for Superpixel Evaluation
Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis
Regular decompositions are necessary for most superpixel-based object recognition or tracking applications. So far in the literature, the regularity or compactness of a superpixel…
Superpixel-based Color Transfer
Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis
In this work, we propose a fast superpixel-based color transfer method (SCT) between two images. Superpixels enable to decrease the image dimension and to extract a reduced set of…
Texture-Aware Superpixel Segmentation
Remi Giraud, Vinh-Thong Ta, Nicolas Papadakis +1
Most superpixel algorithms compute a trade-off between spatial and color features at the pixel level. Hence, they may need fine parameter tuning to balance the two measures, and hi…