Image segmentation can be defined as the detection of closed contours surrounding objects of interest. Given a family of closed curves obtained by some means, a difficulty is to extract the relevant ones. A classical approach is to define an energy minimization framework, where interesting contours correspond to local minima of this energy. Active contours, graph cuts or minimum ratio cuts are instances of such approaches. In this article, we propose a novel, efficient ratio-cut estimator, which is both context-based and can be interpreted as an active contour. As a first example of the effectiveness of our formulation, we consider the tree of shapes, which provides a family of level lines organized in a tree hierarchy through an inclusion relationship. Thanks to the tree structure, the estimator can be computed incrementally in an efficient fashion. Experimental results on synthetic and real images demonstrate the robustness and usefulness of our method.

Out of a concern for focus and concision, domain-specific languages (DSLs) are usually very different from general purpose programming languages (GPLs), both at the syntactic and the semantic levels. One approach to DSL implementation is to write a full language infrastructure, including parser, interpreter or even compiler. Another approach however, is to ground the DSL into an extensible GPL, giving you control over its own syntax and semantics. The DSL may then be designed merely as an <i>extension</i> to the original GPL, and its implementation may boil down to expressing only the differences with it. The task of DSL implementation is hence considerably eased. The purpose of this chapter is to provide a tour of the features that make a GPL extensible, and to demonstrate how, in this context, the distinction between DSL and GPL can blur, sometimes to the point of complete disappearance.

Digital Topology software should reflect the generality of the underlying mathematics: mapping the latter to the former requires genericity. By designing generic solutions, one can effectively reuse digital topology data structures and algorithms. We propose an image processing framework focused on the Generic Programming paradigm in which an algorithm on the paper can be turned into a single code, written once and usable with various input types. This approach enables users to design and implement new methods at a lower cost, try cross-domain experiments and help generalize results.

Frequently organized by NIST, Speaker Recognition evaluations (SRE) show high accuracy rates. This demonstrates that this field of research is mature. The latest progresses came from the proposition of low dimensional i-vectors representation and new classifiers such as Probabilistic Linear Discriminant Analysis (PLDA) or Cosine Distance classifier. In this paper, we study some variants of Boltzmann Machines (BM). BM is used in image processing but still unexplored in Speaker Verification (SR). Given two utterances, the SR task consists to decide whether they come from the same speaker or not. Based on this definition, we can illustrate SR as two-classes (same vs. different speakers classes) classification problem. Our first attempt of using BM is to model each class with one generative Restricted Boltzmann Machine (RBM) with symmetric Log-Likelihood Ratio on both models as decision score. This new approach achieved an Equal Error Rate (EER) of 7% and a minimum Detection Cost Function (DCF) of 0.035 on the female content of the NIST SRE 2008. The objective of this research is mainly to explore a new paradigm i.e. BM without necessarily obtaining better performance than the state-of-the-art system.

We present Climb, an experimental generic image processing library written in Common Lisp. Most image processing libraries are developed in static languages such as C or C++ (often for performance reasons). The motivation behind Climb is to provide an alternative view of the same domain, from the perspective of dynamic languages. More precisely, the main goal of Climb is to explore the dynamic way(s) of addressing the question of genericity, while applying the research to a concrete domain. Although still a prototype, Climb already features several levels of genericity and ships with a set of built-in algorithms as well as means to combine them.

Geldenhuys and Hansen showed that a kind of $\omega$-automata known as <i>Testing Automata</i> (TA) can, in the case of stuttering-insensitive properties, outperform the Buchi automata traditionally used in the automata-theoretic approach to model checking (<b>geldenhuys.06.spin?</b>). In previous work (<b>bensalem.sumo.2011?</b>), we compared TA against <i>Transition-based Generalized Buchi Automata</i> (TGBA), and concluded that TA were more interesting when counterexamples were expected, otherwise TGBA were more efficient. In this work we introduce a new kind of automata, dubbed <i>Transition-based Generalized Testing Automata</i> (TGTA), that combine ideas from TA and TGBA. Implementation and experimentation of TGTA show that they outperform other approaches in most of the cases.

We present a new algorithm for motion compensation that uses a motion estimation method based on tangent distance. The method is compared with a Block-Matching based approach in various common situations. Whereas Block-Matching algorithms usually only predict positions of blocks over time, our method also predicts the evolution of pixels into these blocks. The prediction error is then drastically decreased. The method is implemented into the Theora codec proving that this algorithm improves the video codec performances.

In a time where the differences between static and dynamic languages are starting to fade away, this paper brings one more element to the "convergence" picture by showing that thanks to the novelties from the recent C++0x standard, it is relatively easy to implement a JavaScript layer on top of C++. By that, we not only mean to implement the language features, but also to preserve as much of its original notation as possible. In doing so, we provide the programmer with a means to freely incorporate highly dynamic JavaScript-like code into a regular C++ program.

While TeX is unanimously praised for its typesetting capabilities, it is also regularly blamed for its poor programmatic offerings. A macro-expansion system is indeed far from the best choice in terms of general-purpose programming. Several solutions have been proposed to modernize TeX on the programming side. All of them is mixed with a full-blown programming language. This paper advocates another, homogeneous approach in which TeX is first rewritten in a modern language, , which serves both at the core of the program and at the scripting level. All programmatic macros of TeX are hence rendered obsolete, as the underlying language itself can be used for user-level programming.