Section: Overall Objectives

Highlights of the Year

• Pierrick Legrand was finalist of the Humies award (Human-Competitive Results Produced by Genetic and Evolutionary Computation) for his work on "Evolving estimators of the pointwise Holder exponent with Genetic Programming" at Genetic and Evolutionary Computation Conference (GECCO) July 6-10, 2013 In Amsterdam. The jugging committee was:

  • Erik Goodman

  • Una-May O'Reilly

  • Wolfgang Banzhaf

  • Darrell Whitley

  • Lee Spector

  The regularity of a signal can be numerically expressed using Holder exponents, which characterize the singular structures a signal contains. In particular, within the domains of image processing and image understanding, regularity-based analysis can be used to describe local image shape and appearance. However, estimating the Holder exponent is not a trivial task, and current methods tend to be computationally slow and complex. This work presents an approach to automatically synthesize estimators of the pointwise Holder exponent for digital images. This task is formulated as an optimization problem and Genetic Programming (GP) is used to search for operators that can approximate a traditional estimator, the oscillations method. Experimental results show that GP can generate estimators that achieve a low error and a high correlation with the ground truth estimation. Furthermore, most of the GP estimators are faster than traditional approaches, in some cases their runtime is orders of magnitude smaller. This result allowed us to implement a real-time estimation of the Holder exponent on a live video signal, the first such implementation in current literature. Moreover, the evolved estimators are used to generate local descriptors of salient image regions, a task for which a stable and robust matching is achieved, comparable with state-of-the-art methods. In conclusion, the evolved estimators produced by GP could help expand the application domain of Holder regularity within the fields of image analysis and signal processing.

• The IRSES FP7 Marie Curie project ACOBSEC presented by the team ALEA was accepted.

  Over the last decade, Human-Computer Interaction (HCI) has grown and matured as a field. Gone are the days when only a mouse and keyboard could be used to interact with a computer. The most ambitious of such interfaces are Brain-Computer Interaction (BCI) systems. The goal in BCI is to allow a person to interact with an artificial system using only brain activity. The most common approach towards BCI is to analyse, categorize and interpret Electroencephalography (EEG) signals, in such a way that they alter the state of a computer. The objective of the present project is to study the development of computer systems for the automatic analysis and classification of mental states of vigilance; i.e., a person's state of alertness. Such a task is relevant to diverse domains, where a person is expected or required to be in a particular state. However, this problem is by no means a trivial one. In fact, EEG signals are known to be highly noisy, irregular and tend to vary significantly from person to person, making the development of general techniques a very difficult scientific endeavour.

  List of Beneficiaries

  • Beneficiary 1 (coordinator) Institut National de Recherche en Informatique et Automatique Inria France

  • Beneficiary 2 Universite Victor Segalen Bordeaux II UB2 France

  • Beneficiary 3 Instituto de Engenharia de Sistemas e Computadores, Investigacao e Desenvolvimento em Lisboa INESC-ID Portugal

  • Beneficiary 4 Universidad de Extremadura UNEX Spain

  • Partner 5 Instituto Tecnologico de Tijuana ITT Mexico

  • Partner 6 Centro de Investigacion Cientifica y educacion Superior de Ensenada, Baja California CICESE Mexico