Hierarchical Segmentation Based Upon Multi-resolution Approximations and the Watershed Transform

Figliuzzi, Bruno; Chang, Kaiwen; Faessel, Matthieu

doi:10.1007/978-3-319-57240-6_15

Cited by 2 publications

(1 citation statement)

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“…The neural network produces a segmentation mask M and an image C with bi-dimensional Gaussian functions placed at locations corresponding to the centers of the detected particles. We obtain a labeled image of the detected particles by applying a watershed algorithm (Figliuzzi et al, 2017;Vincent and Soille, 1991) to the segmentation mask M , previously thresholded at the value 1/2, with the local maxima of C selected as markers.…”

Section: Network Architecturementioning

confidence: 99%

A supervised algorithm entirely trained on a synthetic dataset to segment granular suspension images

Paulovics¹,

Figliuzzi

Dumont

et al. 2023

Preprint

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We present in this article a novel methodology for segmenting experimental images of granular suspensions, which uses a convolutional neural network trained on synthetic images generated with a morphological model. In many image processing problems related to physical applications, the lack of annotated data prevents the use of state-of-the-art supervised algorithms. Our solution to overcome this issue is to alleviate the need for annotated images by using a generative morphological model to construct synthetic images subsequently used as training samples. When applied to actual images of a suspension, the convolutional neural network presents good generalization properties and surpasses the performances of traditional segmentation algorithms. This gain in accuracy is crucial to improve the estimation of the local concentration field in the suspension.

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Section: Network Architecturementioning

confidence: 99%