Publications

From text detection to text segmentation: A unified evaluation scheme

By Stefania Calarasanu, Jonathan Fabrizio, Séverine Dubuisson

2016-10-01

In Proceedings of the 2nd international workshop on robust reading conference (IWRR-ECCV)

Abstract

Current text segmentation evaluation protocols are often incapable of properly handling different scenarios (broken/merged/partial characters). This leads to scores that incorrectly reflect the segmentation accuracy. In this article we propose a new evaluation scheme that overcomes most of the existent drawbacks by extending the EvaLTex protocol (initially designed to evaluate text detection at region level). This new unified platform has numerous advantages: it is able to evaluate a text understanding system at every detection stage and granularity level (paragraph/line/word and now character) by using the same metrics and matching rules; it is robust to all segmentation scenarios; it provides a qualitative and quantitative evaluation and a visual score representation that captures the whole behavior of a segmentation algorithm. Experimental results on nine segmentation algorithms using different evaluation frameworks are also provided to emphasize the interest of our method.

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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.

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Derived-term automata for extended weighted rational expressions

By Akim Demaille

2016-07-06

In Proceedings of the thirteenth international colloquium on theoretical aspects of computing (ICTAC)

Abstract

We present an algorithm to build an automaton from a rational expression. This approach introduces support for extended weighted expressions. Inspired by derived-term based algorithms, its core relies on a different construct, rational expansions. We introduce an inductive algorithm to compute the expansion of an expression from which the automaton follows. This algorithm is independent of the size of the alphabet, and actually even supports infinite alphabets. It can easily be accommodated to generate deterministic (weighted) automata. These constructs are implemented in Vcsn, a free-software platform dedicated to weighted automata and rational expressions.

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Heuristics for checking liveness properties with partial order reductions

By Alexandre Duret-Lutz, Fabrice Kordon, Denis Poitrenaud, Étienne Renault

2016-06-17

In Proceedings of the 14th international symposium on automated technology for verification and analysis (ATVA’16)

Abstract

Checking liveness properties with partial-order reductions requires a cycle proviso to ensure that an action cannot be postponed forever. The proviso forces each cycle to contain at least one fully expanded state. We present new heuristics to select which state to expand, hoping to reduce the size of the resulting graph. The choice of the state to expand is done when encountering a dangerous edge. Almost all existing provisos expand the source of this edge, while this paper also explores the expansion of the destination and the use of SCC-based information.

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Spot 2.0 — a framework for LTL and $\omega$-automata manipulation

By Alexandre Duret-Lutz, Alexandre Lewkowicz, Amaury Fauchille, Thibaud Michaud, Étienne Renault, Laurent Xu

2016-06-17

In Proceedings of the 14th international symposium on automated technology for verification and analysis (ATVA’16)

Abstract

We present Spot 2.0, a C++ library with Python bindings and an assortment of command-line tools designed to manipulate LTL and $\omega$-automata in batch. New automata-manipulation tools were introduced in Spot 2.0; they support arbitrary acceptance conditions, as expressible in the Hanoi Omega Automaton format. Besides being useful to researchers who have automata to process, its Python bindings can also be used in interactive environments to teach $\omega$-automata and model checking.

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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.

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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.

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Derived-term automata of multitape rational expressions

By Akim Demaille

2016-04-26

In Proceedings of implementation and application of automata, 21st international conference (CIAA’16)

Abstract

We introduce (weighted) rational expressions to denote series over Cartesian products of monoids. To this end, we propose the operator $\mid$ to build multitape expressions such as $(a^+\mid x + b^+\mid y)^*$. We define expansions, which generalize the concept of derivative of a rational expression, but relieved from the need of a free monoid. We propose an algorithm based on expansions to build multitape automata from multitape expressions.

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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.

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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.

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