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Image filtering using morphological amoebas

Abstract : This paper presents morphological operators with non-fixed shape kernels, or amoebas, which take into account the image contour variations to adapt their shape. Experiments on grayscale and color images demonstrate that these novel filters outperform classical morphological operations with a fixed, space-invariant structuring element for noise reduction applications. Tests on synthetic 3D images are then performed to show the high noise-reduction capacity of amoeba-based filters.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-01431825
Contributor : Etienne Decencière <>
Submitted on : Wednesday, January 25, 2017 - 9:18:05 AM
Last modification on : Wednesday, October 14, 2020 - 3:48:10 AM
Long-term archiving on: : Wednesday, April 26, 2017 - 12:36:47 PM

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Romain Lerallut,, Etienne Decencière, Fernand Meyer. Image filtering using morphological amoebas. Image and Vision Computing, Elsevier, 2007, 25 (4), pp.395-404. ⟨10.1016/j.imavis.2006.04.018⟩. ⟨hal-01431825⟩

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