Scale Equivariant Neural Networks with Morphological Scale-Spaces

Families of Generalised Morphological Scale Spaces

Morphological and linear scale spaces are well-established instruments in image analysis. They display interesting analogies which make a deeper insight into their mutual relation desirable. A contribution to the understanding of this relation is presented here. We embed morphological dilation and erosion scale spaces with paraboloid structure functions into families of scale spaces which are f...

Colour Morphological Scale-Spaces for Image Segmentation

Morphological scale-spaces have become an important tool for analysing greyscale images. However, their extension to colour images has proven elusive until recently. In this paper an original evaluation of two recently proposed colour sieves is presented, both algorithmically and in terms of their computational and segmentation performance. A new colour sieve structure is also proposed, motivat...

Decentralized Interaction Estimators in Large Scale Power Systems with Neural Networks.

Morphological Semigroups and Scale-Spaces on Ultrametric Spaces

Morphological semigroups for functions on length spaces have recently studied [2], whose basic ingredients are the convolution in the (max,+)-algebra (or supremal convolution), the metric distance and a convex shape function. Links with Hamilton–Jacobi PDE on length spaces appears also naturally. These semigroups lead to powerful scale-space properties for multiscale filtering, regularization a...

Scale-Invariant Convolutional Neural Networks

Even though convolutional neural networks (CNN) has achieved near-human performance in various computer vision tasks, its ability to tolerate scale variations is limited. The popular practise is making the model bigger first, and then train it with data augmentation using extensive scale-jittering. In this paper, we propose a scaleinvariant convolutional neural network (SiCNN), a model designed...