Digital Images Augmentation Using Mathematical Morphology Operators

Computer vision can be defined as a converter used by artificial systems to understand the information captured in digital images and videos sequences. Video sequences are digital images disposed at certain frames per second. To achieve excellent performance in the process of computer vision, it is necessary to compile a large amount of data which can be time-consuming and/or expensive. To alleviate such issues which consist in insufficient digital images to be processed during deep learning, different augmentation algorithms were developed, being classified in three types of categories: model-based, optimizing policy-based and model-free. Different approaches were carried out in the matter of images augmentation, but most did not consider a solution that would also reduce the dataset size. In this current approach, we evaluate the impact of mathematical morphology operators (MMO) on original digital images to achieve synthesized images using a model-based augmentation algorithm. Deep learning (DL) algorithms consisting in two trained deep convolutional neural networks (CNN) were involved in image processing. Using one CNN trained on original images dataset and the second CNN trained on augmentation images dataset with MMO, relevant results will be provided and will allow valid comparison.