2 citations · 5 across the 13 of their papers we have counts for
16 papers
Analysis of Different Losses for Deep Learning Image Colorization
Coloma Ballester, Aurélie Bugeau, Hernan Carrillo +4
Image colorization aims to add color information to a grayscale image in a realistic way. Recent methods mostly rely on deep learning strategies. While learning to automatically co…
Influence of Color Spaces for Deep Learning Image Colorization
Coloma Ballester, Aurélie Bugeau, Hernan Carrillo +4
Colorization is a process that converts a grayscale image into a color one that looks as natural as possible. Over the years this task has received a lot of attention. Existing col…
Patch-based field-of-view matching in multi-modal images for electroporation-based ablations
Luc Lafitte, Rémi Giraud, Cornel Zachiu +6
Various multi-modal imaging sensors are currently involved at different steps of an interventional therapeutic work-flow. Cone beam computed tomography (CBCT), computed tomography…
Generalized Shortest Path-based Superpixels for Accurate Segmentation of Spherical Images
Rémi Giraud, Rodrigo Borba Pinheiro, Yannick Berthoumieu
Most of existing superpixel methods are designed to segment standard planar images as pre-processing for computer vision pipelines. Nevertheless, the increasing number of applicati…
Multi-Scale Superpatch Matching using Dual Superpixel Descriptors
Rémi Giraud, Merlin Boyer, Michaël Clément
Over-segmentation into superpixels is a very effective dimensionality reduction strategy, enabling fast dense image processing. The main issue of this approach is the inherent irre…
Texture Superpixel Clustering from Patch-based Nearest Neighbor Matching
Rémi Giraud, Yannick Berthoumieu
Superpixels are widely used in computer vision applications. Nevertheless, decomposition methods may still fail to efficiently cluster image pixels according to their local texture…
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…