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Modeling, Evaluation and Control of a Road Image Processing Chain

  • Yves Lucas
  • Antonio Domingues
  • Driss Driouchi
  • Pierre Marché
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

Tuning a complete image processing chain (IPC) remains a tricky step. Until now researchers focused on the evaluation of single algorithms, based on a small number of test images and ad hoc tuning independent of input data. In this paper we explain how, by combining statistical modeling with design of experiments, numerical optimization and neural learning, it is possible to elaborate a powerful and adaptive IPC. To succeed, it is necessary to build a large image database, to describe input images and finally to evaluate the IPC output. By testing this approach on an IPC dedicated to road obstacle detection, we demonstrate that this experimental methodology and software architecture ensure a steady efficiency. The reason is simple: the IPC is globally optimized, from a large number of real images (180 out of a sequence of 30 000) and with adaptive processing of input data.

Keywords

Input Image Optimal Tuning Homogeneity Measure Large Image Database Adaptive Tuning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yves Lucas
    • 1
  • Antonio Domingues
    • 2
  • Driss Driouchi
    • 2
  • Pierre Marché
    • 3
  1. 1.Vision and Robotics Lab, IUT Mesures PhysiquesOrleans UniversityBourges cedexFrance
  2. 2.Vision and Robotics LabENSI of BourgesBourgesFrance
  3. 3.Theoretical and Applied Statistics LabPierre & Marie Curie UniversityParisFrance

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