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Found 7 papers, 2 papers with code
Semi-Supervised Machine Learning: a Homological Approach
In this paper we describe the mathematical foundations of a new approach to semi-supervised Machine Learning.
A Topological Approach for Semi-Supervised Learning
In the former, we have used a homological approach that consists in studying the persistence diagrams associated with the data using the Bottleneck and Wasserstein distances.
Neuron detection in stack images: a persistent homology interpretation
Automation and reliability are the two main requirements when computers are applied in Life Sciences.
SynapCountJ --- a Tool for Analyzing Synaptic Densities in Neurons
The quantification of synapses is instrumental to measure the evolution of synaptic densities of neurons under the effect of some physiological conditions, neuronal diseases or even drug treatments.
Effective persistent homology of digital images
In this paper, three Computational Topology methods (namely effective homology, persistent homology and discrete vector fields) are mixed together to produce algorithms for homological digital image processing.
Defining and computing persistent Z-homology in the general case
By general case we mean methods able to process simplicial sets and chain complexes not of finite type.
Computational Geometry Symbolic Computation Algebraic Topology
Verifying a platform for digital imaging: a multi-tool strategy
Fiji is a Java platform widely used by biologists and other experimental scientists to process digital images.
