Search Results for author: Jean-yves Guillemaut

Found 7 papers, 0 papers with code

HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis

no code implementations26 Nov 2023 Daniel L Ayuba, Belen Marti-Cardona, Jean-yves Guillemaut, Oscar Mendez Maldonado

The exceptional spectral resolution of hyperspectral imagery enables material insights that are not possible with RGB or multispectral images.

Contrastive Learning Hyperspectral image analysis +2

Adaptive sampling for scanning pixel cameras

no code implementations27 Jul 2022 Yusuf Duman, Jean-yves Guillemaut, Simon Hadfield

A scanning pixel camera is a novel low-cost, low-power sensor that is not diffraction limited.

Image Classification Semantic Segmentation

Temporally Coherent General Dynamic Scene Reconstruction

no code implementations18 Jul 2019 Armin Mustafa, Marco Volino, Hansung Kim, Jean-yves Guillemaut, Adrian Hilton

Existing techniques for dynamic scene reconstruction from multiple wide-baseline cameras primarily focus on reconstruction in controlled environments, with fixed calibrated cameras and strong prior constraints.

Segmentation Semantic Segmentation

4D Temporally Coherent Light-field Video

no code implementations30 Apr 2018 Armin Mustafa, Marco Volino, Jean-yves Guillemaut, Adrian Hilton

Evaluation of the proposed light-field scene flow against existing multi-view dense correspondence approaches demonstrates a significant improvement in accuracy of temporal coherence.

Scene Flow Estimation

Temporally coherent 4D reconstruction of complex dynamic scenes

no code implementations CVPR 2016 Armin Mustafa, Hansung Kim, Jean-yves Guillemaut, Adrian Hilton

Sparse-to-dense temporal correspondence is integrated with joint multi-view segmentation and reconstruction to obtain a complete 4D representation of static and dynamic objects.

4D reconstruction Camera Calibration +2

General Dynamic Scene Reconstruction from Multiple View Video

no code implementations ICCV 2015 Armin Mustafa, Hansung Kim, Jean-yves Guillemaut, Adrian Hilton

The primary contributions of this paper are twofold: an automatic method for initial coarse dynamic scene segmentation and reconstruction without prior knowledge of background appearance or structure; and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes from multiple wide-baseline static or moving cameras.

Scene Segmentation Segmentation

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