Image multiprocessing method for vision systems

Abstract

The present disclosure relates to a method for image classes definition and to a method for image multiprocessing and related vision system, which implement said method for image classes definition. The latter comprising an image splitting operation for each image of M input images, the image splitting operation comprising the steps of: a) splitting the image into image portions; b) executing the algorithm onto each image portion with at least one processing unit; c) identifying the image portion associated with a maximum execution time of said algorithm; d) splitting said identified image portion into further image portions; e) checking if a stop criterion is met: e1) if the stop criterion is met, iterating steps a) to e) onto another one of the M input images; e2) if the stop criterion is not met, executing a predefined image processing algorithm onto each of the further image portions; identifying the image portion or further image portion associated with a maximum execution time of said algorithm; and iterating steps d) to e) on the so identified image portions portion or further image portion; wherein after executing steps a) to e) on all of the M input images, the method for image classes definition further comprises the steps of: f) identifying in an image space, for all the M input images, the positions of each split image portion/further image portion and defining clusters (A, B, C) based thereon; g) defining a set of Q image classes (A′, B′, C, AC) based on said clusters (A, B, C), each class (A′, B′, C, AC) being univocally associated with a splitting pattern representing in the image space a plurality of regions to be allocated to a corresponding plurality of processing units of the vision system for image multiprocessing.