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Article dans une revue

Bilateral K-Means for Superpixel Computation (the SLIC Method)

Abstract : As a substitute to a full segmentation of a digital image, or as preprocessing to a segmentation algorithm, superpixels provide an over-segmentation that offers several benefits: good adherence to edges, uniformity of color inside superpixels, a richer adjacency structure than the regular grid of pixels, and the fact that each node of the graph of superpixels has a shape, which can be used in subsequent processing. Moreover, their evaluation is less subjective than a full segmentation, which somehow always involves a semantic interpretation of the scene. The SLIC method (Simple Linear Iterative Clustering) has been a very popular algorithm to compute superpixels since its introduction. Its advantage is due to its simplicity and to its computing time performance. In essence, it consists in a K-means clustering in bilateral domain, involving both position and color. We study in detail this algorithm and propose a fast, simple postprocessing that ensures that superpixels are connected, a property not ensured by the original method. Source Code The commented C++ source code for SLIC and its documentation are available on the web page of this article 1. Usage instructions are detailed in the README.md file of the archive.
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https://hal-enpc.archives-ouvertes.fr/hal-03651336
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Soumis le : lundi 25 avril 2022 - 16:16:02
Dernière modification le : mardi 10 mai 2022 - 19:47:52
Archivage à long terme le : : mardi 26 juillet 2022 - 19:26:26

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Robin Gay, Jérémie Lecoutre, Nicolas Menouret, Arthur Morillon, Pascal Monasse. Bilateral K-Means for Superpixel Computation (the SLIC Method). Image Processing On Line, IPOL - Image Processing on Line, 2022, ⟨10.5201/ipol.2022.373⟩. ⟨hal-03651336⟩

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