Publications

Speckle spot detection in ultrasound images: Application to speckle reduction and speckle tracking

Abstract

This paper investigates the speckle spot detection task in ultrasound images. Speckle spots are described by structural criteria: dimensions, shape, and topology. We propose to represent the image using a morphological inclusion tree, from which speckle spots are detected using their structural appearance. This makes the method independent of contrast, and hence robusts to intensity correction. The detection was applied to speckle reduction and speckle tracking, and experiments showed that this approach performs well compared to state-of-the-art methods.

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Espaces des formes basés sur des arbres : Définition et applications en traitement d’images et vision par ordinateur

Abstract

Le cadre classique des filtres connexes consiste à enlever d’un graphe certaines de ses composantes connexes. Pour appliquer ces filtres, il est souvent utile de transformer une image en un arbre de composantes, et on élague cet arbre pour simplifier l’image de départ. Les arbres ainsi formés ont des propriétés remarquables pour la vision par ordinateur. Une première illustration de leur intérêt est la définition d’un détecteur de zones d’intérêt, vraiment invariant aux changements de contraste, qui nous permet d’obtenir des résultats à l’état de l’art en recalage d’images et en reconstruction 3D à base d’images. Poursuivant dans l’utilisation de ces arbres, nous proposons d’élargir le cadre des filtres connexes. Pour cela, nous introduisons la notion d’espaces des formes basés sur des arbres : au lieu de filtrer des composantes connexes du graphe correspondant à l’image, nous proposons de filtrer des composantes connexes du graphe donné par l’arbre des composantes de l’image. Ce cadre général, que nous appelons morphologie basée sur les formes, peut être utilisé pour la détection et la segmentation d’objets, l’obtention de segmentations hiérarchiques, et le filtrage d’images. De nombreuses applications et illustrations montrent l’intérêt de ce cadre.

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A comparative review of component tree computation algorithms

Abstract

Connected operators are morphological tools that have the property of filtering images without creating new contours and without moving the contours that are preserved. Those operators are related to the max-tree and min-tree repre- sentations of images, and many algorithms have been proposed to compute those trees. However, no exhaustive comparison of these algorithms has been proposed so far, and the choice of an algorithm over another depends on many parameters. Since the need for fast algorithms is obvious for production code, we present an in-depth comparison of the existing algorithms in a unique framework, as well as variations of some of them that improve their efficiency. This comparison involves both sequential and parallel algorithms, and execution times are given with respect to the number of threads, the input image size, and the pixel value quantization. Eventually, a decision tree is given to help the user choose the most appropriate algorithm with respect to the user requirements. To favor reproducible research, an online demo allows the user to upload an image and bench the different algorithms, and the source code of every algorithms has been made available.

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GMM weights adaptation based on subspace approaches for speaker verification

Abstract

In this paper, we explored the use of Gaussian Mixture Model (GMM) weights adaptation for speaker verifica- tion. We compared two different subspace weight adap- tation approaches: Subspace Multinomial Model (SMM) and Non-Negative factor Analysis (NFA). Both techniques achieved similar results and seemed to outperform the retraining maximum likelihood (ML) weight adaptation. However, the training process for the NFA approach is substantially faster than the SMM technique. The i-vector fusion between each weight adaptation approach and the classical i-vector yielded slight improvements on the tele- phone part of the NIST 2010 Speaker Recognition Eval- uation dataset.

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Is there a best Büchi automaton for explicit model checking?

Abstract

LTL to Büchi automata (BA) translators are traditionally optimized to produce automata with a small number of states or a small number of non-deterministic states. In this paper, we search for properties of Büchi automata that really influence the performance of explicit model checkers. We do that by manual analysis of several automata and by experiments with common LTL-to-BA translators and realistic verification tasks. As a result of these experiences, we gain a better insight into the characteristics of automata that work well with Spin.

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The MIT lincoln laboratory / JHU / EPITA-LSE LRE17 system

Abstract

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A morphological method for music score staff removal

Abstract

Removing the staff in music score images is a key to improve the recognition of music symbols and, with ancient and degraded handwritten music scores, it is not a straightforward task. In this paper we present the method that has won in 2013 the staff removal competition, organized at the International Conference on Document Analysis and Recognition (ICDAR). The main characteristics of this method is that it essentially relies on mathematical morphology filtering. So it is simple, fast, and its full source code is provided to favor reproducible research.

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On making $n$D images well-composed by a self-dual local interpolation

Abstract

Natural and synthetic discrete images are generally not well-composed, leading to many topological issues: connectivities in binary images are not equivalent, the Jordan Separation theorem is not true anymore, and so on. Conversely, making images well-composed solves those problems and then gives access to many powerful tools already known in mathematical morphology as the Tree of Shapes which is of our principal interest. In this paper, we present two main results: a characterization of 3D well-composed gray-valued images; and a counter-example showing that no local self-dual interpolation with a classical set of properties makes well-composed images with one subdivision in 3D, as soon as we choose the mean operator to interpolate in 1D. Then, we briefly discuss various constraints that could be interesting to change to make the problem solvable in nD.

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A first parallel algorithm to compute the morphological tree of shapes of $n$D images

Abstract

The tree of shapes is a self-dual tree-based image representation belonging to the field of mathematical morphology. This representation is highly interesting since it is invariant to contrast changes and inversion, and allows for numerous and powerful applications. A new algorithm to compute the tree of shapes has been recently presented: it has a quasi-linear complexity; it is the only known algorithm that is also effective for nD images with n > 2; yet it is sequential. With the increasing size of data to process, the need of a parallel algorithm to compute that tree is of prime importance; in this paper, we present such an algorithm. We also give some benchmarks that show that the parallel version is computationally effective. As a consequence, that makes possible to process 3D images with some powerful self-dual morphological tools.

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A precise skew estimation algorithm for document images using KNN clustering and fourier transform

Abstract

In this article, we propose a simple and precise skew estimation algorithm for binarized document images. The estimation is performed in the frequency domain. To get a precise result, the Fourier transform is not applied to the document itself but the document is preprocessed: all regions of the document are clustered using a KNN and contours of grouped regions are smoothed using the convex hull to form more regular shapes, with better orientation. No assumption has been made concerning the nature or the content of the document. This method has been shown to be very accurate and was ranked first at the DISEC’13 contest, during the ICDAR competitions.

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