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.
Highlights
To obtain the optimal parameter tuning for the image processing chain (IPC), we look for methods not based on the local gradient computing as it is not available here
After the preceding steps devoted to statistical modeling, numerical optimization, and learning, the IPC is toggled into an operational mode, and the image processing tuning parameters are continuously adapted to the characteristics of new input images
An intuitive graphical interpretation exists for the covering rate: it is the part of the region of interest (ROI) which is covered by the superimposition of the masks associated to the set of segments detected by the IPC; it will be expressed in this paper as a percentage
Summary
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.). Sometimes no ground truth exists or data are uncertain and either application experts are needed for qualitative visual assessment or empirical numerical criteria are searched for. All these methods consider only one operator at a time [8,9,10,11]. We show how to overcome these problems using an experimental approach combining statistical modeling, numerical optimization, and learning We illustrate this approach in the case of an IPC dedicated to line extraction for road obstacle detection
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