In this article, we identify limitations in the existing hit-or-miss neural definitions and formulate an optimization problem to learn the transform relative to ...

Section III introduces the optimization problem, learning algorithm, our generaliza- tion of the hit-or-miss transform, and our extension of convo- lution, ...

While most deep learning architectures are built on convolution, alternative foundations like morphology are being explored for purposes like interpretability ...

The morphological hit-or-miss operation has the advantage that it considers both foreground information and background information when evaluating the target ...

This paper proposed an interpretable morphological convolutional neural network called Morph-CNN for pattern recognition, where morphological operations were ...

This paper proposes an extension to mathematical morphology, based on reduced ordering, specifically the morphological Hit-or-Miss Transform which is used for ...

Dec 4, 2019 · Herein, we investigate new deep networks based on the morphological hit-or-miss transform. The hit-or-miss takes into account both foreground ...

Extending the Morphological Hit-or-Miss Transform to Deep Neural Networks ... While most deep learning architectures are built on convolution, alternative ...

Missing: Extending | Show results with:Extending

Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks ... Extending the Morphological Hit-or-Miss Transform to Deep Neural Networks.