A FRAMEWORK FOR MORPHOLOGICAL OPERATIONS USING COUNTER HARMONIC MEAN
Bojan Knežević, Radovan Jalic, Dragan Erceg · Proceedings on Engineering Sciences · 2024
In this article, we have a tendency to embrace a novel framework for learning morphological operations using counter-harmonic mean.It combines the conception of morphology with convolutional neural networks.Similarly, the elemental morphological operators of dilation and erosion, opening and closing, as well as the more refined top-hat transform, for which we disclose a real-world application from the steel industry, are all subjected to a rigorous experimental validation.Our system learns about the structuring element and the operator's composition via online learning and stochastic gradient descent.It works effectively with massive datasets and in online environments.