Learning morphological operators for skin detection
This work addresses the specific problem of enhancing skin detection accuracy for computer vision applications, presenting an incremental improvement over existing methods.
The paper tackles the problem of refining skin detection masks by proposing a novel post-processing approach using trained morphological operators, achieving improved segmentation quality across 10 datasets with two different detection methods.
In this work we propose a novel post processing approach for skin detectors based on trained morphological operators. The first step, consisting in skin segmentation is performed according to an existing skin detection approach is performed for skin segmentation, then a second step is carried out consisting in the application of a set of morphological operators to refine the resulting mask. Extensive experimental evaluation performed considering two different detection approaches (one based on deep learning and a handcrafted one) carried on 10 different datasets confirms the quality of the proposed method.