2013 IEEE Global Conference on Signal and Information Processing 2013
DOI: 10.1109/globalsip.2013.6736914
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Abstract: Abstract-In this paper, we propose a nonlocal approach based on graphs to segment raw point clouds as a particular class of graph signals. Using the framework of Partial difference Equations (PdEs), we propose a transcription on graphs of recent continuous global active contours along with a minimization algorithm. To apply it on point clouds, we show how to represent a point cloud as a graph weighted with patches. Experiments show the benefits of the approach on raw colored point clouds obtained from real sca… Show more

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Cited by 6 publications
(9 citation statements)
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“…The proposed formulation is valid for any p. However, for p = 1, specific formulations can be derived thanks to the coarea formula on graphs [15] with a convex formulation and faster algorithms can be considered [23].…”
Section: Adaptation On Weighted Graphsmentioning
confidence: 99%
“…The proposed formulation is valid for any p. However, for p = 1, specific formulations can be derived thanks to the coarea formula on graphs [15] with a convex formulation and faster algorithms can be considered [23].…”
Section: Adaptation On Weighted Graphsmentioning
confidence: 99%
“…We have not aimed to be exhaustive. Other PDE operators on graphs have found applications in image analysis, such as the p-Laplacian [21,32,33,34,54,74,80]. Other theoretical investigations include [57] and recently these methods have been used to study artificial neural networks [69,79].…”
Section: Discrete-to-continuum Limitsmentioning
confidence: 99%
“…MRF/CRF converts the classification as a multi-labeling optimization problem. By minimizing their corresponding energy functions, the feature differences are minimized in the same class and are maximized among different classes (Lozes et al, 2013). Contextual classifier is insensitive to noise, but its segmented results depend on the initial values and are usually piecemeal.…”
Section: Related Workmentioning
confidence: 99%