A multi-threshold segmentation approach based on Artificial Bee Colony optimization
Applied Intelligence
A comparison of nature inspired algorithms for multi-threshold image segmentation
Expert Systems with Applications: An International Journal
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Segmentation of images represents the first step in many of the tasks that pattern recognition or computer vision has to deal with. Therefore, the main goal of our paper is to describe a new method for image segmentation, taking into account some Mathematical Morphology operations and an adaptively updated threshold, what we call Morphological Gradient Threshold, to obtain the optimal segmentation. The key factor in our work is the calculation of the distance between the segmented image and the ideal segmentation. Experimental results show that the optimal threshold is obtained when the Morphological Gradient Threshold is around the 70% of the maximum value of the gradient. This threshold could be computed, for any new image captured by the vision system, using a properly designed binary metrics.