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Design of Experiments for Performance Evaluation and Parameter Tuning of a Road Image Processing Chain

  • Yves LucasEmail author
  • Antonio Domingues
  • Driss Driouchi
  • Sylvie Treuillet
Open Access
Research Article
Part of the following topical collections:
  1. Performance Evaluation in Image Processing

Abstract

Tuning a complete image processing chain (IPC) is not a straightforward task. The first problem to overcome is the evaluation of the whole process. Until now researchers have 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 the design of experiments applied on a large image database enables statistical modeling for IPC significant parameter identification. The second problem is then considered: how can we find the relevant tuning and continuously adapt image processing to input data? After the tuning of the IPC on a typical subset of the image database using numerical optimization, we develop an adaptive IPC based on a neural network working on input image descriptors. By testing this approach on an IPC dedicated-to-road obstacle detection, we demonstrate that this experimental methodology and software architecture can ensure continuous efficiency. The reason is simple: the IPC is globally optimized, from a large number of real images and with adaptive processing of input data.

Keywords

Input Image Parameter Tuning Image Database Software Architecture Numerical Optimization 

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Copyright information

© Lucas et al. 2006

Authors and Affiliations

  • Yves Lucas
    • 1
    Email author
  • Antonio Domingues
    • 2
  • Driss Driouchi
    • 3
  • Sylvie Treuillet
    • 4
  1. 1.Laboratoire Vision et Robotique, IUT Mesures PhysiquesUniversité d'OrléansBourgesFrance
  2. 2.Laboratoire Vision et RobotiqueBourgesFrance
  3. 3.Laboratoire de Statistiques Théoriques et AppliquéesUniversité Pierre & Marie CurieParisFrance
  4. 4.Laboratoire Vision et RobotiqueOrleansFrance

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