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Our reflection, on the elements of existing Image Processing systems (currently Image Processing, Symbol interpretation level, control mode, level of extracted features) and corresponding use of Artificial Intelligence, leads us to the definition of the SARPI system. This system performs the extraction of features of intermediate level. In the present first step of implementation, we limit ourself to line segments. They are associated to a descriptor including several parameters: position, angle, length, cross contrast, ... and precision on all of these parameters. SARPI applies to single or multiple features detection, it finds the requested feature(s) and produces its (their) total or partial (as requested) description. SARPI takes as input the set of requested parameters and available values of some feature parameters (typically: qualitative measure of contrast). Its main part is a control module automatically generating an Image Processing sequence to solve the problem (extraction of requested feature parameters). Rules allow to divide the problem in elementary ones with respect to the kind of input parameters. They allow the selection of an elementary function set according to the requested feature parameters and the known parameters; in this way, if the known information is insufficient, the control module selects and executes elementary functions that look for the missing information. Each of these elementary functions is pre-associated to Image Procedures and heuristics that select the appropriate procedures according to thc values of the input parameters. The parameters of the image processes are controlled automatically by the precision on the requested feature parameters. Particularly, the sampling steps of the parameters ρ and θ of the 'lough transform are calculated from the requested precision of the feature parameters. The selected Image Processings are applied on a region of the image that is calculated from the approximated position of the features, if given, or on the entire image, if not. The system has been tested on images of industrial objects with different conditions of illumination and contrast.
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