David Helbert
University of Poitiers
44 Papers
94 Citations
David Helbert is an academic researcher from University of Poitiers. The author has contributed to research in topics: Computer science & Wavelet transform. The author has an hindex of 8, co-authored 33 publications. Previous affiliations of David Helbert include Centre national de la recherche scientifique.
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Papers
Support vector regression-based 3D-wavelet texture learning for hyperspectral image compression
TL;DR: A lossy hyperspectral image compression system based on the regression of 3D wavelet coefficients that has high performances in terms of rate distortion and spectral fidelity and evaluation of detection and compression over various bands shows that spectral information is preserved using the compression method.
Advanced concepts for intelligent vision systems, 19th international conference, ACIVS 2018, proceedings
Jacques Blanc-Talon,David Helbert,Wilfried Philips,Dan C. Popescu,Paul Scheunders +4 more
- 01 Sep 2018
TL;DR: This book constitutes the refereed proceedings of the 19th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2018, held in Poitiers, France, in September 2018.
Photometric reconstruction of a dynamic textured surface from just one color image acquisition
TL;DR: This work presents a three-dimensional recovery approach for real textured surfaces based on photometric stereo that uses a single color image, instead of a sequence of gray-scale images, to recover the surface of the three dimensions.
24
Pseudo-Divergence and Bidimensional Histogram of Spectral Differences for Hyperspectral Image Processing
TL;DR: It is shown that divergence are not adapted to direct use on spectra, and the efficiency of the spectral similarity measure and of the bidimensional histogram of spectral differences on artificial and Cultural Heritage spectral images is proved.
20
3-D Discrete Analytical Ridgelet Transform
TL;DR: The experimental results show that the simple thresholding of the 3-D DART coefficients is efficient, and the potentiality of this new discrete transform is illustrated by the denoising of3-D image and color video.