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Josiane Zerubia

Researcher at French Institute for Research in Computer Science and Automation

Publications -  338
Citations -  10506

Josiane Zerubia is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Image segmentation & Markov chain. The author has an hindex of 49, co-authored 329 publications receiving 9952 citations.

Papers
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Extension of phase correlation to subpixel registration

TL;DR: It is shown that for downsampled images the signal power in the phase correlation is not concentrated in a single peak, but rather in several coherent peaks mostly adjacent to each other.
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Gaussian approximations of fluorescence microscope point-spread function models.

TL;DR: The least-squares Gaussian approximations of the diffraction-limited 2D-3D paraxial-nonparaxial point-spread functions (PSFs) of the wide field fluorescence microscope (WFFM), the laser scanning confocal microscope (LSCM), and the disk scanning conf focal microscope (DSCM) are studied.
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Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution.

TL;DR: This work proposes to combine the Richardson–Lucy algorithm with a regularization constraint based on Total Variation, which suppresses unstable oscillations while preserving object edges and shows that this constraint improves the deconvolution results as compared with the unregularized Richardson– Lucy algorithm, both visually and quantitatively.
Book

Analyse d'images: filtrage et segmentation

TL;DR: In this paper, the authors present a comparative study of different techniques for segmentation of images, including filtrage du bruit, extraction of contours, and extraction of regions.
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A Level Set Model for Image Classification

TL;DR: A supervised classification model based on a variational approach to find an optimal partition composed of homogeneous classes with regular interfaces and shows how these forces can be defined through the minimization of a unique fonctional.