Design of Experiments for Performance Evaluation and Parameter Tuning of a Road Image Processing Chain
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Design of Experiments for Performance Evaluation and Parameter Tuning of a Road Image Processing Chain Yves Lucas,1 Antonio Domingues,2 Driss Driouchi,3 and Sylvie Treuillet4 1 Laboratoire
Vision et Robotique, IUT Mesures Physiques, Universit´e d’Orl´eans, 63 avenue de Lattre, 18020 Bourges cedex, France Vision et Robotique, ENSIB 10 Bd Lahitolle, 18000 Bourges, France 3 Laboratoire de Statistiques Th´ eoriques et Appliqu´ees, Universit´e Pierre & Marie Curie, 175 rue du Chevaleret, 75013 Paris, France 4 Laboratoire Vision et Robotique, Polytech Orl´ eans 12, rue de Blois BP 6744 45067 Orleans, France 2 Laboratoire
Received 1 March 2005; Revised 20 November 2005; Accepted 28 November 2005 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. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
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ADAPTIVE PROCESSING IN VISION SYSTEMS
Designing an image processing application involves a sequence of low- and medium-level operators (filtering, edge detection and linking, corner detection, region growing, etc.) in order to extract relevant data for decision purposes (pattern recognition, classification, inspection, etc.). At each step of the processing, tuning parameters have a significant influence on the algorithm behavior and the ultimate quality of results. Thanks to the emergence of extremely powerful and low cost processors, artificial vision systems now exist for demanding applications such as video surveillance or car driving where the scene contents are uncontrolled, versatile, and rapidly changing. The automatic tuning of the IPC has to be solved, as the quality of low-level vision processes needs to be continuously preserved to guarantee high-level task robustness. The first problem to be tackled in order to design adaptive vision systems is the evaluation of image processing tasks. Within the last few years, researchers have proposed rather empirical solutions [1–7]. When confirmed a ground truth is available, it is possible to compare directly this reference
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