A Faster Graph-Based Segmentation Algorithm with Statistical Region Merge

Fahad, Ahmed Mohammed; Morris, Tim

doi:10.1007/11919629_30

Cited by 8 publications

(7 citation statements)

References 11 publications

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“…The decision rule used in the LV algorithm was recently explained, in a probabilistic setting, as hypothesis testing from which the Probabilistic Local Variation (pLV) algorithm (Baltaxe et al, 2015) was derived. Other 2D variations of LV have been suggested (Fahad and Morris, 2006;Zhang and Alhajj, 2006), as well as video-based extensions (Grundmann et al, 2010). In this work we thoroughly analyze the 3D extension of each LV algorithm stage and propose a new over-segmentation algorithm for 3D point clouds.…”

Section: D Over-segmentation Methods (Super-pixel Generation)mentioning

confidence: 99%

Graph based over-segmentation methods for 3D point clouds

Ben-Shabat

Avraham

Lindenbaum

et al. 2018

Computer Vision and Image Understanding

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Over-segmentation, or super-pixel generation, is a common preliminary stage for many computer vision applications. New acquisition technologies enable the capturing of 3D point clouds that contain color and geometrical information. This 3D information introduces a new conceptual change that can be utilized to improve the results of over-segmentation, which uses mainly color information, and to generate clusters of points we call super-points. We consider a variety of possible 3D extensions of the Local Variation (LV) graph based over-segmentation algorithms, and compare them thoroughly. We consider different alternatives for constructing the connectivity graph, for assigning the edge weights, and for defining the merge criterion, which must now account for the geometric information and not only color. Following this evaluation, we derive a new generic algorithm for over-segmentation of 3D point clouds. We call this new algorithm Point Cloud Local Variation (PCLV). The advantages of the new oversegmentation algorithm are demonstrated on both outdoor and cluttered indoor scenes. Performance analysis of the proposed approach compared to state-of-the-art 2D and 3D over-segmentation algorithms shows significant improvement according to the common performance measures.

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Section: D Over-segmentation Methods (Super-pixel Generation)mentioning

confidence: 99%

Graph based over-segmentation methods for 3D point clouds

Ben-Shabat

Avraham

Lindenbaum

et al. 2018

Computer Vision and Image Understanding

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“…By suitably removing the lowest weighted edges, different partitions that have stronger inherent affinities could be found. Major MST based approaches of segmentation are the method based on Gestalt theory by Zahn [85], a tree partitioning algorithm by Xu et al [86,87], an efficient MST algorithm by Felzenszwalb et al [24,25] and methods [26,27] improving the performance of the efficient MST algorithm. A short discussion of these methods is as follow.…”

Section: Minimum Spanning Tree Based Methodsmentioning

confidence: 99%

“…A sample result obtained using this method is shown in Figure 11. Fahad et al [26] and Zhang et al [27] suggested some modifications to improve its performance.…”

Section: Minimum Spanning Tree Based Methodsmentioning

confidence: 99%

“…Note that, in this scenario, graph-based methods present different graph representations, where the nodes may be pixels [3,5,14,6,7,1,2,8], pixel vertices [15,4], regions [16], or even user-drawn markers [17]. They also differ in the graph algorithm used to solve the problem: graph matching [17], random walker [18], the min-cut/max-flow algorithm [19,20,21,22,8], Dijkstra's algorithm [3,15,4,23,14,6,7,2], Kruskal's or Prim's algorithm [24,25,26,27], etc. Note also that, a same graph-search algorithm (e.g., Dijkstra's algorithm) can be used for region-based and boundary-based segmentation, besides other image operators [23].…”

Section: Introductionmentioning

confidence: 99%

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A Review on Graph Based Segmentation

Camilus¹,

Govindan²

2012

IJIGS

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“…Any change in the definition of the difference between two regions to make the method more robust leads to NP hard. Fahad et al [11] and Zhang et al [29] has suggested some modification to improve its performance. Moreover, the drawback still resides.…”

Section: Introductionmentioning

confidence: 99%

A Robust Graph Theoretic Approach for Image Segmentation

Camilus

Govindan

Sathidevi

2008

2008 IEEE International Conference on Signal Image Technology and Internet Based Systems

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This paper presents a new robust graph theoretic approach for image segmentation. The proposed method which is capable of accurately locating region boundaries has the following salient features. First, it is a nonsupervised approach which reflects the non-local properties of the image. Second, it guarantees that the regions are connected. Finally, it produces robust results which is almost unaffected by the influences of outliers. In this technique, at each step, a minimum weight edge is selected and the two regions connected by the minimum weight edge are considered for merge. The merging of regions is carried out, if the mean of the edges connecting the two regions is smaller than the maximum of the mean of the intra region edges along with the threshold value.

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A Faster Graph-Based Segmentation Algorithm with Statistical Region Merge

Cited by 8 publications

References 11 publications

Graph based over-segmentation methods for 3D point clouds

Graph based over-segmentation methods for 3D point clouds

A Review on Graph Based Segmentation

A Robust Graph Theoretic Approach for Image Segmentation

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