Geodesic interactive segmentation in the color monogenic signal framework
G Demarcq, H Le Capitaine… - 2012 19th IEEE …, 2012 - ieeexplore.ieee.org
In this paper, we present an interactive algorithm for segmentation of color images. The user
first draws some scribbles into regions that must be discriminated, and the segmentation is
then automatically obtained. The segmentation is based on the computation of geodesic
distances within color monogenic signal (CMS) fields. An important difference with state-of-
the-art methods is that scribbles, which are often segments, are sample pixels picked up by
the user. It results in a much more user-friendly segmentation process. Experimental results …
first draws some scribbles into regions that must be discriminated, and the segmentation is
then automatically obtained. The segmentation is based on the computation of geodesic
distances within color monogenic signal (CMS) fields. An important difference with state-of-
the-art methods is that scribbles, which are often segments, are sample pixels picked up by
the user. It results in a much more user-friendly segmentation process. Experimental results …
[PDF][PDF] GEODESIC INTERACTIVE SEGMENTATION IN THE COLOR MONOGENIC SIGNAL FRAMEWORK
MIA EA3165 - Citeseer
In this paper, we present an interactive algorithm for segmentation of color images. The user
first draws some scribbles into regions that must be discriminated, and the segmentation is
then automatically obtained. The segmentation is based on the computation of geodesic
distances within color monogenic signal (CMS) fields. An important difference with stateof-
the-art methods is that scribbles, which are often segments, are sample pixels picked up by
the user. It results in a much more user-friendly segmentation process. Experimental results …
first draws some scribbles into regions that must be discriminated, and the segmentation is
then automatically obtained. The segmentation is based on the computation of geodesic
distances within color monogenic signal (CMS) fields. An important difference with stateof-
the-art methods is that scribbles, which are often segments, are sample pixels picked up by
the user. It results in a much more user-friendly segmentation process. Experimental results …
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