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Aymeric Histace

Researcher at École nationale supérieure de l'électronique et de ses applications

Publications -  126
Citations -  1807

Aymeric Histace is an academic researcher from École nationale supérieure de l'électronique et de ses applications. The author has contributed to research in topics: Active contour model & Computer science. The author has an hindex of 14, co-authored 116 publications receiving 1126 citations. Previous affiliations of Aymeric Histace include University of Angers & Centre national de la recherche scientifique.

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Journal ArticleDOI

Active Learning for Real Time Detection of Polyps in Videocolonoscopy

TL;DR: This approach allows us to detect approximately 90% of polyps on a freely available database introduced to the community in 2012, for a F2 score of 65%, and matches real-time constraint by making possible the analysis of a frame in only 0.023s on a standard computer not necessarily dedicated to that kind of application.
Journal ArticleDOI

Segmentation of myocardial boundaries in tagged cardiac MRI using active contours: a gradient-based approach integrating texture analysis

TL;DR: The work described in this paper aims to automate the myocardial contours detection in order to optimize the detection and the tracking of the grid of tags within myocardium and is based on the use of texture analysis and active contours models.
Journal ArticleDOI

Development and validation of an automated algorithm to evaluate the abundance of bubbles in small bowel capsule endoscopy.

TL;DR: A computed algorithm based on a GLCM detector strategy had high diagnostic performance allowing assessment of the abundance of bubbles in SB-CE still frames and could be of interest for clinical use (quality reporting) and for research purposes (objective comparison tool of different preparations).
Proceedings ArticleDOI

Confocal microscopy segmentation using active contour based on alpha(α)-divergence

TL;DR: The experimental results demonstrate that the proposed method outperforms previously proposed histogram based methods in terms of segmentation accuracy and robustness with respect to type and level of noise.