1 citations · 1 across the 6 of their papers we have counts for
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H-SPAM: Hierarchical Superpixel Anything Model
Julien Walther, Rémi Giraud, Michaël Clément
Superpixels offer a compact image representation by grouping pixels into coherent regions. Recent methods have reached a plateau in terms of segmentation accuracy by generating noi…
Superpixel Anything: A general object-based framework for accurate yet regular superpixel segmentation
Julien Walther, Rémi Giraud, Michaël Clément
Superpixels are widely used in computer vision to simplify image representation and reduce computational complexity. While traditional methods rely on low-level features, deep lear…
Superpixel Segmentation: A Long-Lasting Ill-Posed Problem
Rémi Giraud, Michaël Clément
For many years, image over-segmentation into superpixels has been essential to computer vision pipelines, by creating homogeneous and identifiable regions of similar sizes. Such co…
Deep Spherical Superpixels
Rémi Giraud, Michaël Clément
Over the years, the use of superpixel segmentation has become very popular in various applications, serving as a preprocessing step to reduce data size by adapting to the content o…
Brain Structure Ages -- A new biomarker for multi-disease classification
Huy-Dung Nguyen, Michaël Clément, Boris Mansencal +1
Age is an important variable to describe the expected brain's anatomy status across the normal aging trajectory. The deviation from that normative aging trajectory may provide some…
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…