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Le Touquet – Paris-Plage, France

Verna D.,Epita Research and Development Laboratory
Journal of Universal Computer Science | Year: 2010

While software design patterns are a generally useful concept, they are often (and mistakenly) seen as ready-made universal recipes for solving common problems. In a way, the danger is that programmers stop thinking about their actual problem, and start looking for pre-cooked solutions in some design pattern book instead. What people usually forget about design patterns is that the underlying programming language plays a major role in the exact shape such or such pattern will have on the surface. The purpose of this paper is twofold: we show why design pattern expression is intimately linked to the expressiveness of the programming language in use, and we also demonstrate how a blind application of them can in fact lead to very poorly designed code. © J.UCS. Source


Widynski N.,University of Montreal | Geraud T.,Epita Research and Development Laboratory | Garcia D.,University of Montreal
IEEE International Ultrasonics Symposium, IUS | Year: 2014

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. © 2014 IEEE. Source


Xu Y.,Epita Research and Development Laboratory | Xu Y.,Laboratoire dInformatique Gaspard Monge | Monasse P.,ParisTech National School of Bridges and Roads | Geraud T.,Epita Research and Development Laboratory | Najman L.,University Paris Est Creteil
IEEE Transactions on Image Processing | Year: 2014

This paper introduces a topological approach to local invariant feature detection motivated by Morse theory. We use the critical points of the graph of the intensity image, revealing directly the topology information as initial interest points. Critical points are selected from what we call a tree-based shape-space. In particular, they are selected from both the connected components of the upper level sets of the image (the Max-tree) and those of the lower level sets (the Min-tree). They correspond to specific nodes on those two trees: 1) to the leaves (extrema) and 2) to the nodes having bifurcation (saddle points). We then associate to each critical point the largest region that contains it and is topologically equivalent in its tree. We call such largest regions the tree-based Morse regions (TBMRs). The TBMR can be seen as a variant of maximally stable extremal region (MSER), which are contrasted regions. Contrarily to MSER, TBMR relies only on topological information and thus fully inherit the invariance properties of the space of shapes (e.g., invariance to affine contrast changes and covariance to continuous transformations). In particular, TBMR extracts the regions independently of the contrast, which makes it truly contrast invariant. Furthermore, it is quasi-parameter free. TBMR extraction is fast, having the same complexity as MSER. Experimentally, TBMR achieves a repeatability on par with state-of-the-art methods, but obtains a significantly higher number of features. Both the accuracy and robustness of TBMR are demonstrated by applications to image registration and 3D reconstruction. © 1992-2012 IEEE. Source


Carlinet E.,Epita Research and Development Laboratory | Carlinet E.,University Paris Est Creteil | Geraud T.,Epita Research and Development Laboratory | Geraud T.,University Paris Est Creteil
IEEE Transactions on Image Processing | Year: 2014

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 representations 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. © 1992-2012 IEEE. Source

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