activity
20162020
most citedMorphological segmentation of hyperspectral images

63 citations · 85 across the 8 of their papers we have counts for

collaborators

11 papers

eess.IV202063 cited

Morphological segmentation of hyperspectral images

Guillaume Noyel, Jesus Angulo, Dominique Jeulin

The present paper develops a general methodology for the morphological segmentation of hyperspectral images, i.e., with an important number of channels. This approach, based on wat…

cs.DM20207 cited

Fast computation of all pairs of geodesic distances

Guillaume Noyel, Jesus Angulo, Dominique Jeulin

Computing an array of all pairs of geodesic distances between the pixels of an image is time consuming. In the sequel, we introduce new methods exploiting the redundancy of geodesi…

cs.CV20207 cited

Retinal vessel segmentation by probing adaptive to lighting variations

Guillaume Noyel, Christine Vartin, Peter Boyle +1

We introduce a novel method to extract the vessels in eye fun-dus images which is adaptive to lighting variations. In the Logarithmic Image Processing framework, a 3-segment probe…

eess.IV20193 cited

Multivariate mathematical morphology for DCE-MRI image analysis in angiogenesis studies

Guillaume Noyel, Jesus Angulo, Dominique Jeulin +2

We propose a new computer aided detection framework for tumours acquired on DCE-MRI (Dynamic Contrast Enhanced Magnetic Resonance Imaging) series on small animals. In this approach…

cs.CV2019

Functional Asplund metrics for pattern matching, robust to variable lighting conditions

Guillaume Noyel, Michel Jourlin

In this paper, we propose a complete framework to process images captured under uncontrolled lighting and especially under low lighting. By taking advantage of the Logarithmic Imag…

cs.CV20191 cited

A Link Between the Multiplicative and Additive Functional Asplund's Metrics

Guillaume Noyel

Functional Asplund's metrics were recently introduced to perform pattern matching robust to lighting changes thanks to double-sided probing in the Logarithmic Image Processing (LIP…