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
In Proceedings of the IAPR international workshop on document analysis systems (DAS)
Smartphones have became an easy and convenient mean to acquire documents. In this paper, we focus on the automatic segmentation of identity documents in smartphone photos or videos using visual saliency (VS). VS-based approaches, which pertain to computer vision, have not be considered yet for this particular task. Here we compare different VS methods, and we propose a new VS scheme, based on a recent distance belonging to the scope of mathematical morphology. We show that the saliency maps we obtain are competitive with state-of-the-art visual saliency methods and, that such approaches are very promising for use in identity document detection and segmentation, even without taking into account any prior knowledge about document contents. In particular they can work in real-time on smartphones.
In Proceedings of the 12th IAPR international workshop on graphics recognition (GREC)
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.
In Journal of Mathematical Imaging and Vision
Due to digitization, usual discrete signals generally present topological paradoxes, such as the connectivity paradoxes of Rosenfeld. To get rid of those paradoxes, and to restore some topological properties to the objects contained in the image, like manifoldness, Latecki proposed a new class of images, called well-composed images, with no topological issues. Furthermore, well-composed images have some other interesting properties: for example, the Euler number is locally computable, boundaries of objects separate background from foreground, the tree of shapes is well-defined, and so on. Last, but not the least, some recent works in mathematical morphology have shown that very nice practical results can be obtained thanks to well-composed images. Believing in its prime importance in digital topology, we then propose this state-of-the-art of well-composedness, summarizing its different flavours, the different methods existing to produce well-composed signals, and the various topics that are related to well-composedness.
In Actes du 26e colloque GRETSI
L’imagerie par résonance magnétique (IRM) du cerveau est utilisée sur les nouveau-nés pour évaluer l’évolution du cerveau et diagnostiquer des maladies neurologiques. Ces examens nécessitent souvent une analyse quantitative des différents tissus du cerveau, de sorte qu’avoir une segmentation précise est essentiel. Dans cet article, nous proposons une méthode automatique rapide de segmentation en différents tissus des images IRM 3D de cerveaux de nouveau-nés ; elle utilise un réseau de neurones totalement convolutif (FCN) et du transfert d’apprentissage. Par rapport aux approches similaires qui reposent soit sur des patchs 2D ou 3D, soit sur des FCN totalement 3D, notre méthode est beaucoup plus rapide : elle ne prend que quelques secondes, et une seule modalité (T2) est nécessaire. Afin de prendre les informations 3D en compte, trois coupes 2D successives sont empilées pour former une image 2D en couleurs, dont l’ensemble sur tout le volume sert d’entrée à un FCN, pré-entraîné sur ImageNet pour la classification d’images naturelles. Nos expériences sur un ensemble de données de référence montrent que notre méthode obtient des résultats du niveau de ceux de l’état de l’art.
In Proceedings of the 23rd IEEE international conference on image processing (ICIP)
Brain magnetic resonance imaging (MRI) is widely used to assess brain developments in neonates and to diagnose a wide range of neurological diseases in adults. Such studies are usually based on quantitative analysis of different brain tissues, so it is essential to be able to classify them accurately. In this paper, we propose a fast automatic method that segments 3D brain MR images into different tissues using fully convolutional network (FCN) and transfer learning. As compared to existing deep learning-based approaches that rely either on 2D patches or on fully 3D FCN, our method is way much faster: it only takes a few seconds, and only a single modality (T1 or T2) is required. In order to take the 3D information into account, all 3 successive 2D slices are stacked to form a set of 2D color images, which serve as input for the FCN pre-trained on ImageNet for natural image classification. To the best of our knowledge, this is the first method that applies transfer learning to segment both neonatal and adult brain 3D MR images. Our experiments on two public datasets show that our method achieves state-of-the-art results.
In Discrete geometry for computer imagery – proceedings of the 20th IAPR international conference on discrete geometry for computer imagery (DGCI)
In digital topology, it is well-known that, in 2D and in 3D, a digital set $X \subseteq Z^n$ is digitally well-composed (DWC), i.e., does not contain any critical configuration, if its immersion in the Khalimsky grids $H^n$ is well-composed in the sense of Alexandrov (AWC), i.e., its boundary is a disjoint union of discrete $(n-1)$-surfaces. We show that this is still true in $n$-D, $n \geq 2$, which is of prime importance since today 4D signals are more and more frequent.
In Actes d’ORASIS
La distance de la barrière minimum est définie comme le plus petit intervalle de l’ensemble des niveaux de gris le long d’un chemin entre deux points dans une image. Pour cela, on considère que l’image est un graphe à valeurs sur les sommets. Cependant, cette définition ne correspond pas à l’interprétation d’une image comme étant une carte d’élévation, c’est-à-dire, un paysage continu d’une manière ou d’une autre. En se plaçant dans le cadre des fonctions multivoques, nous présentons une nouvelle définition pour cette distance. Cette définition, compatible avec l’interprétation paysagère, est dénuée de problèmes topologiques bien qu’en restant dans un monde discret. Nous montrons que la distance proposée est reliée à la structure morphologique d’arbre des formes, qui permet de surcroît un calcul rapide et exact de cette distance. Cela se démarque de sa définition classique, pour laquelle le seul calcul rapide n’est qu’approximatif.
In Mathematical morphology and its application to signal and image processing – proceedings of the 13th international symposium on mathematical morphology (ISMM)
The minimum barrier (MB) distance is defined as the minimal interval of gray-level values in an image along a path between two points, where the image is considered as a vertex-valued graph. Yet this definition does not fit with the interpretation of an image as an elevation map, i.e. a somehow continuous landscape. In this paper, based on the discrete set-valued continuity setting, we present a new discrete definition for this distance, which is compatible with this interpretation, while being free from digital topology issues. Amazingly, we show that the proposed distance is related to the morphological tree of shapes, which in addition allows for a fast and exact computation of this distance. That contrasts with the classical definition of the MB distance, where its fast computation is only an approximation.
In Mathematical morphology and its application to signal and image processing – proceedings of the 13th international symposium on mathematical morphology (ISMM)
A method of text detection in natural images, to be turn into an effective embedded software on a mobile device, shall be both efficient and lightweight. We observed that a simple method based on the morphological Laplace operator can do the trick: we can construct in quasi-linear time a hierarchical image decomposition / simplification based on its 0-crossings, and search for some text in the resulting tree. Yet, for this decomposition to be sound, we need “0-crossings” to be Jordan curves, and to that aim, we rely on some discrete topology tools. Eventually, the hierarchical representation is the morphological tree of shapes of the Laplacian sign. Moreover, we provide an algorithm with linear time complexity to compute this representation. We expect that the proposed hierarchical representation can be useful in some applications other than text detection.
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