Nonlocal segmentation of point clouds with graphs

Lozes, François; Hidane, Moncef; Elmoataz, Abderrahim; Lézoray, Olivier

doi:10.1109/globalsip.2013.6736914

Cited by 6 publications

(9 citation statements)

References 12 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

Graph signal active contours

Lézoray

2021

2020 25th International Conference on Pattern Recognition (ICPR)

Self Cite

View full text Add to dashboard Cite

With the advent of data living on vertices of graphs, there is much interest in processing the so-called graph signals for partitioning tasks. As active contours have had much impact in the image processing community, their formulation on graphs is of importance to the field of graph signal processing. This paper proposes an adaptation on graphs of a model that combines the Geodesic Active Contour and the Active Contour Without Edges models. In addition, specific terms depending on graphs are introduced in the formulation. This adaptation is solved using a level set formulation with a gradient descent that can be expressed as a morphological front evolution process. Experimental results on different kinds of graphs signals show the benefit of the approach.

show abstract

Section: Adaptation On Weighted Graphsmentioning

confidence: 99%

Graph signal active contours

Lézoray

2021

2020 25th International Conference on Pattern Recognition (ICPR)

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

Partial differential equations and variational methods for geometric processing of images

Buet¹,

Mirebeau²,

Gennip³

et al. 2019

The SMAI Journal of Computational Mathematics

View full text Add to dashboard Cite

“…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%

Point Cloud Classification by Fusing Supervoxel Segmentation With Multi-Scale Features

Ao¹,

Wang²,

Shan³

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

<p><strong>Abstract.</strong> Point cloud classification is quite a challenging task due to the existence of noises, occlusion and various object types and sizes. Currently, the commonly used statistics-based features cannot accurately characterize the geometric information of a point cloud. This limitation often leads to feature confusion and classification mistakes (e.g., points of building corners and vegetation always share similar statistical features in a local neighbourhood, such as curvature, sphericity, etc). This study aims at solving this problem by leveraging the advantage of both the supervoxel segmentation and multi-scale features. For each point, its multi-scale features within different radii are extracted. Simultaneously, the point cloud is partitioned into simple supervoxel segments. After that, the class probability of each point is predicted by the proposed SegMSF approach that combines multi-scale features with the supervoxel segmentation results. At the end, the effect of data noises is supressed by using a global optimization that encourages spatial consistency of class labels. The proposed method is tested on both airborne laser scanning (ALS) and mobile laser scanning (MLS) point clouds. The experimental results demonstrate that the proposed method performs well in terms of classifying objects of different scales and is robust to noise.</p>

show abstract

Nonlocal segmentation of point clouds with graphs

Cited by 6 publications

References 12 publications

Graph signal active contours

Graph signal active contours

Partial differential equations and variational methods for geometric processing of images

Point Cloud Classification by Fusing Supervoxel Segmentation With Multi-Scale Features

Contact Info

Product

Resources

About