Data-driven and Automatic Surface Texture Analysis Using Persistent Homology

@article{Yesilli2021DatadrivenAA,
  title={Data-driven and Automatic Surface Texture Analysis Using Persistent Homology},
  author={Melih C. Yesilli and Firas A. Khasawneh},
  journal={2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)},
  year={2021},
  pages={1350-1356}
}
Surface roughness plays an important role in analyzing engineering surfaces. It quantifies the surface topography and can be used to determine whether the resulting surface finish is acceptable or not. Nevertheless, while several existing tools and standards are available for computing surface roughness, these methods rely heavily on user input thus slowing down the analysis and increasing manufacturing costs. Therefore, fast and automatic determination of the roughness level is essential to… 

References

SHOWING 1-10 OF 25 REFERENCES
Processing roughness of sanded wood surfaces
Any quantitative evaluation of a sanded surface requires that the data be filtered to remove form errors and waviness. Wood surfaces contain irregularities due to both the sanding process and the
Texture recognition by the methods of topological data analysis
TLDR
This work checks efficiency of structure of structure, called persistent images, for base containing 800 images of high resolution representing 20 texture classes, and finds out that average efficiency of separate image recognition in the classes is 84%, and in 11 classes, it is not less than 90%.
Approximating Continuous Functions on Persistence Diagrams Using Template Functions
TLDR
This paper describes a mathematical framework for featurizing the persistence diagram space using template functions, and discusses two example realizations of these functions: tent functions and Chybeyshev interpolating polynomials.
Persistence Images: A Stable Vector Representation of Persistent Homology
TLDR
This work converts a PD to a finite-dimensional vector representation which it is called a persistence image, and proves the stability of this transformation with respect to small perturbations in the inputs.
A User’s Guide to Topological Data Analysis
TLDR
This article introduces two of the most commonly used topological signatures, the persistence diagram and the mapper graph, which represent loops and holes in the space by considering connectivity of the data points for a continuum of values rather than a single fixed value.
Evaluation of alternative spectral feature extraction methods of textural images for multivariate modelling
Fast and automatic strategies for extraction of characteristic feature spectra from digital images are investigated. We present a study based on images from confocal laser scanning microscopy (CLSM)
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