Integration of Morphological Operators in Neural Networks

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Abstract

Image processing is a very broad field of study that encompasses a multitude of operations, each of them with different purposes and circumstances of use, complexities and results. Nowadays, the bests results for automatic image study (image segmentation, image classification, object detection, etc.) are obtained using deep learning, and more specifically convolutional neural networks. We explore and conduct experiments on a specific part of image processingmathematical morphology, investigating on the best way of circumventing operations' complexities regarding their integration in a supervised learning pipeline.