Thierry Géraud

Morphology-based hierarchical representation with application to text segmentation in natural images

By Lê Duy Huỳnh, Yongchao Xu, Thierry Géraud

2016-07-13

In Proceedings of the 23st international conference on pattern recognition (ICPR)

Abstract

Many text segmentation methods are elaborate and thus are not suitable to real-time implementation on mobile devices. Having an efficient and effective method, robust to noise, blur, or uneven illumination, is interesting due to the increasing number of mobile applications needing text extraction. We propose a hierarchical image representation, based on the morphological Laplace operator, which is used to give a robust text segmentation. This representation relies on several very sound theoretical tools; its computation eventually translates to a simple labeling algorithm, and for text segmentation and grouping, to an easy tree-based processing. We also show that this method can also be applied to document binarization, with the interesting feature of getting also reverse-video text.

Continue reading

A challenging issue: Detection of white matter hyperintensities in neonatal brain MRI

By Baptiste Morel, Yongchao Xu, Alessio Virzi, Thierry Géraud, Catherine Adamsbaum, Isabelle Bloch

2016-05-20

In Proceedings of the annual international conference of the IEEE engineering in medicine and biology society

Abstract

The progress of magnetic resonance imaging (MRI) allows for a precise exploration of the brain of premature infants at term equivalent age. The so-called DEHSI (diffuse excessive high signal intensity) of the white matter of premature brains remains a challenging issue in terms of definition, and thus of interpretation. We propose a semi-automatic detection and quantification method of white matter hyperintensities in MRI relying on morphological operators and max-tree representations, which constitutes a powerful tool to help radiologists to improve their interpretation. Results show better reproducibility and robustness than interactive segmentation.

Continue reading

Hierarchical image simplification and segmentation based on Mumford-Shah-salient level line selection

By Yongchao Xu, Thierry Géraud, Laurent Najman

2016-05-20

In Pattern Recognition Letters

Abstract

Hierarchies, such as the tree of shapes, are popular representations for image simplification and segmentation thanks to their multiscale structures. Selecting meaningful level lines (boundaries of shapes) yields to simplify image while preserving intact salient structures. Many image simplification and segmentation methods are driven by the optimization of an energy functional, for instance the celebrated Mumford-Shah functional. In this paper, we propose an efficient approach to hierarchical image simplification and segmentation based on the minimization of the piecewise-constant Mumford-Shah functional. This method conforms to the current trend that consists in producing hierarchical results rather than a unique partition. Contrary to classical approaches which compute optimal hierarchical segmentations from an input hierarchy of segmentations, we rely on the tree of shapes, a unique and well-defined representation equivalent to the image. Simply put, we compute for each level line of the image an attribute function that characterizes its persistence under the energy minimization. Then we stack the level lines from meaningless ones to salient ones through a saliency map based on extinction values defined on the tree-based shape space. Qualitative illustrations and quantitative evaluation on Weizmann segmentation evaluation database demonstrate the state-of-the-art performance of our method.

Continue reading

Region-based classification of remote sensing images with the morphological tree of shapes

By Gabriele Cavallaro, Mauro Dalla Mura, Edwin Carlinet, Thierry Géraud, Nicola Falco, Jón Atli Benediktsson

2016-04-12

In Proceedings of the IEEE international geoscience and remote sensing symposium (IGARSS)

Abstract

Satellite image classification is a key task used in remote sensing for the automatic interpretation of a large amount of information. Today there exist many types of classification algorithms using advanced image processing methods enhancing the classification accuracy rate. One of the best state-of-the-art methods which improves significantly the classification of complex scenes relies on Self-Dual Attribute Profiles (SDAPs). In this approach, the underlying representation of an image is the Tree of Shapes, which encodes the inclusion of connected components of the image. The SDAP computes for each pixel a vector of attributes providing a local multiscale representation of the information and hence leading to a fine description of the local structures of the image. Instead of performing a pixel-wise classification on features extracted from the Tree of Shapes, it is proposed to directly classify its nodes. Extending a specific interactive segmentation algorithm enables it to deal with the multi-class classification problem. The method does not involve any statistical learning and it is based entirely on morphological information related to the tree. Consequently, a very simple and effective region-based classifier relying on basic attributes is presented.

Continue reading

Hierarchical segmentation using tree-based shape spaces

By Yongchao Xu, Edwin Carlinet, Thierry Géraud, Laurent Najman

2016-04-11

In IEEE Transactions on Pattern Analysis and Machine Intelligence

Abstract

Current trends in image segmentation are to compute a hierarchy of image segmentations from fine to coarse. A classical approach to obtain a single meaningful image partition from a given hierarchy is to cut it in an optimal way, following the seminal approach of the scale-set theory. While interesting in many cases, the resulting segmentation, being a non-horizontal cut, is limited by the structure of the hierarchy. In this paper, we propose a novel approach that acts by transforming an input hierarchy into a new saliency map. It relies on the notion of shape space: a graph representation of a set of regions extracted from the image. Each region is characterized with an attribute describing it. We weigh the boundaries of a subset of meaningful regions (local minima) in the shape space by extinction values based on the attribute. This extinction-based saliency map represents a new hierarchy of segmentations highlighting regions having some specific characteristics. Each threshold of this map represents a segmentation which is generally different from any cut of the original hierarchy. This new approach thus enlarges the set of possible partition results that can be extracted from a given hierarchy. Qualitative and quantitative illustrations demonstrate the usefulness of the proposed method.

Continue reading

MToS: A tree of shapes for multivariate images

By Edwin Carlinet, Thierry Géraud

2015-10-26

In IEEE Transactions on Image Processing

Abstract

The Tree of Shapes (ToS) is a morphological tree that provides an high-level hierarchical representation of the image suitable for many image processing tasks. When dealing with color images, one cannot use the ToS because its definition is ill-formed on multivariate data. Common workarounds such as marginal processing, or imposing a total order on data are not satisfactory and yield many problems (color artifacts, loss of invariances…) In this paper, we highlight the need for a self-dual and contrast invariant representation of the image and provide a method that builds a single ToS by merging the shapes computed marginally and preserving the most important properties of the ToS. This method does not try to impose an arbitrary total ordering on values but uses only the inclusion relationship between shapes and the merging strategy works in a shape space. Eventually, we show the relevance of our method and our structure through several applications involving color and multispectral image analysis.

Continue reading

Morphological object picking based on the color tree of shapes

By Edwin Carlinet, Thierry Géraud

2015-06-29

In Proceedings of 5th international conference on image processing theory, tools and applications (IPTA’15)

Abstract

The Tree of Shapes is a self-dual and contrast invariant morphological tree that provides a high-level hierarchical representation of images, suitable for many image processing tasks. Despite its powerfulness and its simplicity, it is still under-exploited in pattern recognition and computer vision. In this paper, we show that both interactive and automatic image segmentation can be achieved with some simple tree processings. To that aim, we rely on the “Color Tree of Shapes”, recently defined. We propose a method for interactive segmentation that does not involve any statistical learning, yet yielding results that compete with state-of-the-art approaches. We further extend this algorithm to unsupervised segmentation and give some results. Although they are preliminary, they highlight the potential of such an approach that works in the shape space.

Continue reading

Une approche morphologique de segmentation interactive avec l’arbre des formes couleur

By Edwin Carlinet, Thierry Géraud

2015-06-16

In Actes du 15e colloque GRETSI

Abstract

L’arbre des formes est un arbre morphologique à la fois auto-dual et invariant par changement de contraste. Il fournit une représentation haut-niveau de l’image, intéressante pour de nombreuses tâches de traitement d’images. Malgré son potentiel et sa simplicité, il reste largement sous-utilisé en reconnaissance des formes et vision par ordinateur. Dans cet article, nous présentons une méthode de segmentation interactive qui s’effectue simplement en manipulant cet arbre. Pour cela, nous nous appuierons sur une représentation récemment définie : l’Arbre des Formes Couleur . La méthode de segmentation interactive que nous proposons ne requiert aucun apprentissage statistique ; néanmoins elle obtient des résultats qui rivalisent avec ceux de l’état de l’art. Bien que préliminaires, les résultats obtenus mettent en avant le potentiel et l’intérêt des méthodes travaillant dans l’espace des formes.

Continue reading

Connected filtering on tree-based shape-spaces

By Yongchao Xu, Thierry Géraud, Laurent Najman

2015-06-05

In IEEE Transactions on Pattern Analysis and Machine Intelligence

Abstract

Connected filters are well-known for their good contour preservation property. A popular implementation strategy relies on tree-based image representations: for example, one can compute an attribute characterizing the connected component represented by each node of the tree and keep only the nodes for which the attribute is sufficiently high. This operation can be seen as a thresholding of the tree, seen as a graph whose nodes are weighted by the attribute. Rather than being satisfied with a mere thresholding, we propose to expand on this idea, and to apply connected filters on this latest graph. Consequently, the filtering is performed not in the space of the image, but in the space of shapes built from the image. Such a processing of shape-space filtering is a generalization of the existing tree-based connected operators. Indeed, the framework includes the classical existing connected operators by attributes. It also allows us to propose a class of novel connected operators from the leveling family, based on non-increasing attributes. Finally, we also propose a new class of connected operators that we call morphological shapings. Some illustrations and quantitative evaluations demonstrate the usefulness and robustness of the proposed shape-space filters.

Continue reading

How to make $n$D images well-composed without interpolation

By Nicolas Boutry, Thierry Géraud, Laurent Najman

2015-05-14

In Proceedings of the IEEE international conference on image processing (ICIP)

Abstract

Latecki et al. have introduced the notion of well-composed images, i.e., a class of images free from the connectivities paradox of discrete topology. Unfortunately natural and synthetic images are not a priori well-composed, usually leading to topological issues. Making any $n$D image well-composed is interesting because, afterwards, the classical connectivities of components are equivalent, the component boundaries satisfy the Jordan separation theorem, and so on. In this paper, we propose an algorithm able to make $n$D images well-composed without any interpolation. We illustrate on text detection the benefits of having strong topological properties.

Continue reading

Search

Tags