Baldissera Diego, Nanni Loris, Brahnam Sheryl, Lumini Alessandra
Department of Information Engineering (DEI), University of Padova, 35131 Padova, Italy.
Department of Information Technology and Cybersecurity, Missouri State University, Springfield, MO 65804, USA.
J Imaging. 2021 Jun 3;7(6):95. doi: 10.3390/jimaging7060095.
Skin detectors play a crucial role in many applications: face localization, person tracking, objectionable content screening, etc. Skin detection is a complicated process that involves not only the development of apposite classifiers but also many ancillary methods, including techniques for data preprocessing and postprocessing. In this paper, a new postprocessing method is described that learns to select whether an image needs the application of various morphological sequences or a homogeneity function. The type of postprocessing method selected is learned based on categorizing the image into one of eleven predetermined classes. The novel postprocessing method presented here is evaluated on ten datasets recommended for fair comparisons that represent many skin detection applications. The results show that the new approach enhances the performance of the base classifiers and previous works based only on learning the most appropriate morphological sequences.
面部定位、人员跟踪、不良内容筛选等。皮肤检测是一个复杂的过程，不仅涉及合适分类器的开发，还涉及许多辅助方法，包括数据预处理和后处理技术。本文描述了一种新的后处理方法，该方法学习选择图像是否需要应用各种形态学序列或均匀性函数。所选后处理方法的类型是基于将图像分类到11个预定类别之一来学习的。这里提出的新颖后处理方法在推荐用于公平比较的十个数据集上进行了评估，这些数据集代表了许多皮肤检测应用。结果表明，新方法提高了基础分类器的性能，并且优于仅基于学习最合适形态学序列的先前工作。
