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Found 10 papers, 1 papers with code
An Approach to Colour Morphological Supremum Formation using the LogSumExp Approximation
These are based on the construction of a supremum or infimum with respect to an order over the tonal range in a certain section of the image.
Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentation
Both the patch-size and the loss function are parameters with direct influence on the class imbalance and their optimization leads to a 3\% increase of the Dice score and 22% reduction of the 95% Hausdorff distance compared to the baseline, finally reaching $0. 8\pm0. 15$ and $3. 17\pm1. 7$ mm for the segmentation of seven HAN organs using a single and simple neural network.
Multivariate Medians for Image and Shape Analysis
Having been studied since long by statisticians, multivariate median concepts found their way into the image processing literature in the course of the last decades, being used to construct robust and efficient denoising filters for multivariate images such as colour images but also matrix-valued images.
Stable Backward Diffusion Models that Minimise Convex Energies
Furthermore, we establish a comprehensive theory for the time-continuous evolution and we show that stability carries over to a simple explicit time discretisation of our model.
Graph entropies in texture segmentation of images
In the present paper we focus on texture descriptors in which the graph indices are entropy-based, and use them in a geodesic active contour framework for image segmentation.
Multivariate Median Filters and Partial Differential Equations
Multivariate median filters have been proposed as generalisations of the well-established median filter for grey-value images to multi-channel images.
Amoeba Techniques for Shape and Texture Analysis
Morphological amoebas are image-adaptive structuring elements for morphological and other local image filters introduced by Lerallut et al. Their construction is based on combining spatial distance with contrast information into an image-dependent metric.
A Robust Variational Model for Positive Image Deconvolution
In this paper, an iterative method for robust deconvolution with positivity constraints is discussed.
Analysis of Amoeba Active Contours
They achieve similar results as the well-known geodesic active contour and self-snakes PDEs.
Algorithmic Optimisations for Iterative Deconvolution Methods
Both approaches are demonstrated in the context of Richardson-Lucy deconvolution but are not restricted to this method.
