Automatic tuning for the segmentation of infrared images considering uncertain ground truth

Image segmentation is one of the most important tasks of image processing, as it provides information used to interpret and analyze image contents. The tuning of the parameters of the segmentation method can be considered an optimization problem by defining an objective function based on the similarity of the segmented image and the ground truth. The problem becomes harder to solve when the ground truth is known only under uncertainty. A solution is proposed for the design and the automatic tuning of a real-time segmentation method for infrared images where the ground truth is uncertain. The proposed solution consists of three steps: the proposal of a segmentation method adapted for the considered images, the definition of an objective function that takes the uncertainty of the ground truth into account, and the automatic tuning of the segmentation method by means of genetic algorithms.

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