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Found 6 papers, 3 papers with code
IM: An R-Package for Computation of Image Moments and Moment Invariants
In this report, we introduce a package for R-language, named IM, that implements the calculation of moments for images and allows the reconstruction of images from moments within an object-oriented framework.
Generative modeling of the enteric nervous system employing point pattern analysis and graph construction
We describe a generative network model of the architecture of the enteric nervous system (ENS) in the colon employing data from images of human and mouse tissue samples obtained through confocal microscopy.
A novel statistical methodology for quantifying the spatial arrangements of axons in peripheral nerves
In this study, we employed spatial statistics and point process models to describe the spatial arrangement of axons and Sinkhorn distances to compute the similarities between these arrangements (in terms of first- and second-order statistics) in various vagus and pelvic nerve cross-sections.
Bayesian Nonparametrics for Non-exhaustive Learning
Non-exhaustive learning (NEL) is an emerging machine-learning paradigm designed to confront the challenge of non-stationary environments characterized by anon-exhaustive training sets lacking full information about the available classes. Unlike traditional supervised learning that relies on fixed models, NEL utilizes self-adjusting machine learning to better accommodate the non-stationary nature of the real-world problem, which is at the root of many recently discovered limitations of deep learning.
The Infinite Mixture of Infinite Gaussian Mixtures
Dirichlet process mixture of Gaussians (DPMG) has been used in the literature for clustering and density estimation problems.
Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices
We demonstrate the efficacy of these algorithms on terabyte-sized synthetic matrices and real-world matrices from scientific computing and bioinformatics.
