Joseph Chazalon

In Proceedings of the IAPR international conference on discrete geometry and mathematical morphology (DGMM)

Abstract The digitization of historical maps enables the study of ancient, fragile, unique, and hardly accessible information sources. Main map features can be retrieved and tracked through the time for subsequent thematic analysis. The goal of this work is the vectorization step, i.e., the extraction of vector shapes of the objects of interest from raster images of maps. We are particularly interested in closed shape detection such as buildings, building blocks, gardens, rivers, etc.

Two stages CNN-based segmentation of gliomas, uncertainty quantification and prediction of overall patient survival

By Thibault Buatois, Élodie Puybareau, Guillaume Tochon, Joseph Chazalon

2020-09-03

In International MICCAI brainlesion workshop

Abstract

Using separated inputs for multimodal brain tumor segmentation with 3D U-Net-like architectures

By Nicolas Boutry, Joseph Chazalon, Élodie Puybareau, Guillaume Tochon, Hugues Talbot, Thierry Géraud

2020-06-01

In Proceedings of the 4th international workshop, BrainLes 2019, held in conjunction with MICCAI 2019

Abstract The work presented in this paper addresses the MICCAI BraTS 2019 challenge devoted to brain tumor segmentation using mag- netic resonance images. For each task of the challenge, we proposed and submitted for evaluation an original method. For the tumor segmentation task (Task 1), our convolutional neural network is based on a variant of the U-Net architecture of Ronneberger et al. with two modifications: first, we separate the four convolution parts to decorrelate the weights corresponding to each modality, and second, we provide volumes of size 240 * 240 * 3 as inputs in these convolution parts.

Segmentation of gliomas and prediction of patient overall survival: A simple and fast procedure

By Élodie Puybareau, Guillaume Tochon, Joseph Chazalon, Jonathan Fabrizio

2018-11-05

In Proceedings of the workshop on brain lesions (BrainLes), in conjunction with MICCAI

Abstract In this paper, we propose a fast automatic method that seg- ments glioma without any manual assistance, using a fully convolutional network (FCN) and transfer learning. From this segmentation, we predict the patient overall survival using only the results of the segmentation and a home made atlas. The FCN is the base network of VGG-16, pretrained on ImageNet for natural image classification, and fine tuned with the training dataset of the MICCAI 2018 BraTS Challenge.

Segmentation des hyperintensités de la matière blanche en quelques secondes à l’aide d’un réseau de neurones convolutif et de transfert d’apprentissage

By Élodie Puybareau, Yongchao Xu, Joseph Chazalon, Isabelle Bloch, Thierry Géraud

2018-05-04

In Actes du congrès reconnaissance des formes, image, apprentissage et perception (RFIAP), session spéciale “deep learning, deep in france”

Abstract Dans cet article, nous proposons une méthode automatique et rapide pour segmenter les hyper-intensités de la matière blanche (WMH) dans des images IRM cérébrales 3D, en utilisant un réseau de neurones entièrement convolutif (FCN) et du transfert d’apprentissage. Ce FCN est le réseau neuronal du Visual Geometry Group (VGG) pré-entraîné sur la base ImageNet pour la classification des images naturelles, et affiné avec l’ensemble des données d’entraînement du concours MICCAI WMH.

White matter hyperintensities segmentation in a few seconds using fully convolutional network and transfer learning

By Yongchao Xu, Thierry Géraud, Élodie Puybareau, Isabelle Bloch, Joseph Chazalon

2018-02-06

In Brainlesion: Glioma, multiple sclerosis, stroke and traumatic brain injuries— 3rd international workshop, BrainLes 2017, held in conjunction with MICCAI 2017, quebec city, QC, canada, september 14 2017, revised selected papers

Abstract

Extraction of ancient map contents using trees of connected components

By Jordan Drapeau, Thierry Géraud, Mickaël Coustaty, Joseph Chazalon, Jean-Christophe Burie, Véronique Eglin, Stéphane Bres

2017-10-20

In Proceedings of the 12th IAPR international workshop on graphics recognition (GREC)

Abstract Ancient maps are an historical and cultural heritage widely recognized as a very important source of information, but exploiting such maps is complicated. In this project, we consider the Linguistic Atlas of France (ALF), built between 1902 and 1910. This cartographical heritage produces firstrate data for dialectological researches. In this paper, we focus on the separation of the content in layers for facilitating the extraction, the analysis, the visualization and the diffusion of the data contained in these ancient linguistic atlases.

SmartDoc 2017 video capture: Mobile document acquisition in video mode

By Joseph Chazalon, P. Gomez-Krämer, J.-C. Burie, M. Coustaty, S. Eskenazi, M. Luqman, N. Nayef, M. Rusiñol, N. Sidère, J. M. Ogier.

2017-07-21

In Proceedings of the 1st international workshop on open services and tools for document analysis (ICDAR-OST)

Abstract As mobile document acquisition using smartphones is getting more and more common, along with the continuous improvement of mobile devices (both in terms of computing power and image quality), we can wonder to which extent mobile phones can replace desktop scanners. Modern applications can cope with perspective distortion and normalize the contrast of a document page captured with a smartphone, and in some cases like bottle labels or posters, smartphones even have the advantage of allowing the acquisition of non-flat or large documents.

Benchmarking keypoint filtering approaches for document image matching

By E. Royer, Joseph Chazalon, M. Rusiñol, F. Bouchara

2017-07-04

In Proceedings of the 14th international conference on document analysis and recognition (ICDAR)

Abstract Reducing the amount of keypoints used to index an image is particularly interesting to control processing time and memory usage in real-time document image matching applications, like augmented documents or smartphone applications. This paper benchmarks two keypoint selection methods on a task consisting of reducing keypoint sets extracted from document images, while preserving detection and segmentation accuracy. We first study the different forms of keypoint filtering, and we introduce the use of the CORE selection method on keypoints extracted from document images.

Augmented songbook: An augmented reality educational application for raising music awareness

By Marçal Rusiñol, Joseph Chazalon, Katerine Diaz-Chito

2017-06-29

In Multimedia Tools and Applications

Abstract This paper presents the development of an Augmented Reality mobile application which aims at sensibilizing young children to abstract concepts of music. Such concepts are, for instance, the musical notation or the concept of rythm. Recent studies in Augmented Reality for education suggest that such technologies have multiple benefits for students, including younger ones. As mobile document image acquisition and processing gains maturity on mobile platforms, we explore how it is possible to build a markerless and real-time application to augment the physical documents with didactical animations and interactive content.

Combining deep learning and mathematical morphology for historical map segmentation

Two stages CNN-based segmentation of gliomas, uncertainty quantification and prediction of overall patient survival

Using separated inputs for multimodal brain tumor segmentation with 3D U-Net-like architectures

Segmentation of gliomas and prediction of patient overall survival: A simple and fast procedure

Segmentation des hyperintensités de la matière blanche en quelques secondes à l’aide d’un réseau de neurones convolutif et de transfert d’apprentissage

White matter hyperintensities segmentation in a few seconds using fully convolutional network and transfer learning

Extraction of ancient map contents using trees of connected components

SmartDoc 2017 video capture: Mobile document acquisition in video mode

Benchmarking keypoint filtering approaches for document image matching

Augmented songbook: An augmented reality educational application for raising music awareness

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