CudaHull: Fast parallel 3D convex hull on the GPU

Stein, Ayal; Geva, Eran; El-Sana, Jihad

doi:10.1016/j.cag.2012.02.012

Cited by 42 publications

(24 citation statements)

References 11 publications

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“…To populate the DVF, the convex hull is calculated to find the boundary of the data set (Jarvis, 1973; Stein et al , 2012; Srikanth et al , 2009). Then Delaunay triangulation is used to compute a bounding triangle for the interpolated area (Rong et al , 2008; Qi et al , 2013).…”

Section: Methodsmentioning

confidence: 99%

Canny edge-based deformable image registration

et al. 2017

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This work focuses on developing a 2D Canny edge-based deformable image registration (Canny DIR) algorithm to register in vivo white light images taken at various time points. This method uses a sparse interpolation deformation algorithm to sparsely register regions of the image with strong edge information. A stability criterion is enforced which removes regions of edges that do not deform in a smooth uniform manner. Using a synthetic mouse surface ground truth model, the accuracy of the Canny DIR algorithm was evaluated under axial rotation in the presence of deformation. The accuracy was also tested using fluorescent dye injections, which were then used for gamma analysis to establish a second ground truth. The results indicate that the Canny DIR algorithm performs better than rigid registration, intensity corrected Demons, and Distinctive Features for all evaluation matrices and ground truth scenarios. In conclusion Canny DIR performs well in the presence of the unique lighting and shading variations associated with white-light-based image registration.

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Section: Methodsmentioning

confidence: 99%

Canny edge-based deformable image registration

et al. 2017

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“…In case of datasets with a high number of dimensions, the convex hull approach suffers from a relatively high computational speed; in such cases, the computational cost can be overcome by using parallel algorithms implemented on multicore processors [54] and GPU [55], which also minimize the impact of irregular data. This choice can improve the performance in the case of simple 2D [56,57] and 3D [58,59] datasets, but also in the case of generic -dimensional data [60,61]. Finally, the use of convex hull exhibits a good flexibility that combines computational performance with good spatial representation, since the convex hull is generally more compact, in terms of spatial occupation, when compared to the volume of hyperboxes.…”

Section: Mf Evaluation Of Convex Hull-shaped Clustersmentioning

confidence: 99%

Fuzzy Clustering Using the Convex Hull as Geometrical Model

Liparulo

Proietti

Panella

2015

Advances in Fuzzy Systems

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A new approach to fuzzy clustering is proposed in this paper. It aims to relax some constraints imposed by known algorithms using a generalized geometrical model for clusters that is based on the convex hull computation. A method is also proposed in order to determine suitable membership functions and hence to represent fuzzy clusters based on the adopted geometrical model. The convex hull is not only used at the end of clustering analysis for the geometric data interpretation but also used during the fuzzy data partitioning within an online sequential procedure in order to calculate the membership function. Consequently, a pure fuzzy clustering algorithm is obtained where clusters are fitted to the data distribution by means of the fuzzy membership of patterns to each cluster. The numerical results reported in the paper show the validity and the efficacy of the proposed approach with respect to other well-known clustering algorithms.

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“…Taking advantage of the substantial raw power of modern GPU's immediately comes to mind. Surprisingly, although implementing 2D algorithms in GPU is relatively straightforward, only more recently have we seen works describing GPU-assisted convex hull algorithms for 3D clouds [TO12], [SGES12], [GCTH11]. While significant performance increases have in general been reported, we have not yet evaluated these approaches in the scope of the problem at hand.…”

Section: Approximate Convex Hullmentioning

confidence: 99%

Image Space Rendering of Point Clouds Using the HPR Operator

Silva

Esperança

Marroquim

et al. 2013

Computer Graphics Forum

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The hidden point removal (HPR) operator introduced by Katz et al. [KTB07] provides an elegant solution for the problem of estimating the visibility of points in point samplings of surfaces. Since the method requires computing the three-dimensional convex hull of a set with the same cardinality as the original cloud, the method has been largely viewed as impractical for real-time rendering of medium to large clouds. In this paper we examine how the HPR operator can be used more efficiently by combining several image space techniques, including an approximate convex hull algorithm, cloud sampling, and GPU programming. Experiments show that this combination permits faster renderings without overly compromising the accuracy.

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CudaHull: Fast parallel 3D convex hull on the GPU

Cited by 42 publications

References 11 publications

Canny edge-based deformable image registration

Canny edge-based deformable image registration

Fuzzy Clustering Using the Convex Hull as Geometrical Model

Image Space Rendering of Point Clouds Using the HPR Operator

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