Model Based Probabilistic Piecewise Curve Approximation

Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201

Zone-based verification of timed automata: extrapolations, simulations and what next?

Timed automata have been introduced by Rajeev Alur and David Dill in the early 90's. In the last decades, timed automata have become the de facto model for the verification of real-time systems. Algorithms for timed automata are based on the traversal of their state-space using zones as a symbolic representation. Since the state-space is infinite, termination relies on finite abstractions that yield a finite representation of the reachable states. The first solution to get finite abstractions was based on extrapolations of zones, and has been implemented in the industry-strength tool Uppaal. A different approach based on simulations between zones has emerged in the last ten years, and has been implemented in the fully open source tool TChecker. The simulation-based approach has led to new efficient algorithms for reachability and liveness in timed automata, and has also been extended to richer models like weighted timed automata, and timed automata with diagonal constraints and updates. In this article, we survey the extrapolation and simulation techniques, and discuss some open challenges for the future.Comment: Invited contribution at FORMATS'2

3D Face Recognition Benchmarks on the Bosphorus Database with Focus on Facial Expressions

This paper presents an evaluation of several 3D face recognizers on the Bosphorus database, which was gathered for studies on expression and pose invariant face analysis. We provide identification results of three 3D face recognition algorithms, namely generic face template based ICP approach, one-to-all ICP approach, and depth image-based Principal Component Analysis (PCA) method. All of these techniques treat faces globally and are usually accepted as baseline approaches. In addition, 2D texture classifiers are also incorporated in a fusion setting. Experimental results reveal that even though global shape classifiers achieve almost perfect identification in neutral-to-neutral comparisons, they are sub-optimal under extreme expression variations. We show that it is possible to boost the identification accuracy by focusing on the rigid facial regions and by fusing complementary information coming from shape and texture modalities