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Found 6 papers, 1 papers with code
Can auto-encoders help with filling missing data?
This paper introduces an approach to filling in missing data based on deep auto-encoder models, adequate to high-dimensional data exhibiting complex dependencies, such as images.
Unsupervised Features Learning for Sampled Vector Fields
In this paper we introduce a new approach to computing hidden features of sampled vector fields.
Persistence Bag-of-Words for Topological Data Analysis
Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs).
Persistence Codebooks for Topological Data Analysis
Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs) which are 2D multisets of points.
A Study on Topological Descriptors for the Analysis of 3D Surface Texture
Methods from computational topology are becoming more and more popular in computer vision and have shown to improve the state-of-the-art in several tasks.
Topological descriptors for 3D surface analysis
We investigate topological descriptors for 3D surface analysis, i. e. the classification of surfaces according to their geometric fine structure.
