Calibration is an essential task for setting up camera parameters, especially when cameras are used for industrial applications like object recognition and picking that require a fine-grained location of the observed object. However, this process is time-consuming and requires specific image processing skills, which are not always available: an operator often needs to use the equipment rapidly without costly setup operations. The calibration system needs a coherent set of images of a given model, called a mire, positioned in different ways. In this paper, we propose to automate and to optimize the calibration system by eliminating the requirement for the user to select a suitable set of images. Thus, an optimized calibration can be obtained in a minimum of time. First, we propose to retrieve the set of points of each input image in order to avoid a renewed search at each calibration. Second, we define Al-Thocb, a Harmony Search Calibration algorithm, based on Harmony Search Optimization. The algorithm optimizes the selection of the best images. The satisfaction criterion is defined by a fitness function based on the projection error. The method allows to retrieve coherent camera parameters with no need for specific user skills. It also significantly improves the accuracy of calibration through the use of the reprojection error as fitness function. To demonstrate the applicability of Al-Thocb, we evaluate the accuracy and the responsiveness of the proposed algorithm and compare it to other existing methods.
Machine Vision and Applications – Springer Journals
Published: Jan 29, 2018
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