Heat Walk: Robust Salient Segmentation of Non‐rigid Shapes

Benjamin, William J.; Polk, Andrew Wood; Vishwanathan, S. V. N.; Ramani, Karthik

doi:10.1111/j.1467-8659.2011.02060.x

Cited by 31 publications

(23 citation statements)

References 35 publications

(47 reference statements)

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“…The early works usually focus on finding geometrical features used to provide segmentation criteria, and a detailed survey [16] classified previous segmentation solutions into meaningful part-type segmentation and surface-type segmentation partitioning the surface mesh into patches under some geometric criteria. Recent progress in discovering geometric properties includes diffusion distance [17], heat kernel [3], intrinsic primitive decomposition [13], heat walk [10], concavity-sensitive scalar fields [12], and minimum slice perimeter [14]. These geometric features are clustered in a descriptor space using clustering techniques such as recent Gaussian mixture models [18], greedy algorithm [10], and the Mumford-Shah model [15].…”

Section: Segmentation Methodsmentioning

confidence: 99%

New evaluation metrics for mesh segmentation

Liu

Tang

et al. 2013

Computers & Graphics

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Section: Segmentation Methodsmentioning

confidence: 99%

New evaluation metrics for mesh segmentation

Liu

Tang

et al. 2013

Computers & Graphics

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“…Lovato et al [20] used ADF to achieve the feature points analysis of the 3D human point cloud model upon the method of [38]. In the research field of 3D point cloud model segmentation, Benjamin et al [39] calculate the continuous heat kernel value of 3D model. Greedy algorithm is used to analyze the area of heat kernel energy storage, which is the large difference in heat kernel value of the point and its neighbor point.…”

Section: Application Of Heat Kernelmentioning

confidence: 99%

“…Therefore, heat kernel is widely used in model retrieval [20], model block with independent attitude [10,39], skeleton extraction of the model with independent attitude [37], and other fields.…”

Section: Affine Invariantmentioning

confidence: 99%

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Semantic Segmentation of Human Model Using Heat Kernel and Geodesic Distance

Yang

Hao

2018

Mathematical Problems in Engineering

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A novel approach of 3D human model segmentation is proposed, which is based on heat kernel signature and geodesic distance. Through calculating the heat kernel signature of the point clouds of human body model, the local maxima of thermal energy distribution of the model is found, and the set of feature points of the model is obtained. Heat kernel signature has affine invariability which can be used to extract the correct feature points of the human model in different postures. We adopt the method of geodesic distance to realize the hierarchical segmentation of human model after obtaining the semantic feature points of human model. The experimental results show that the method can overcome the defect of geodesic distance feature extraction. The human body models with different postures can be obtained with the model segmentation results of human semantic characteristics.

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“…The method is provably stable under isometric transformations, but its performance rapidly decreases in presence of topological noise. Heat walk [BPVR11] is based on similar principles, but employs a diffusion geometric edge weighting akin to [LBB11]. Other methods based on ideas from diffusion geometry [Reu10, GBAL09] successfully tackle the nearly‐isometric case, but are generally sensitive to shape deformations that are far from being isometric.…”

Section: Introductionmentioning

confidence: 99%

Robust Region Detection via Consensus Segmentation of Deformable Shapes

Rodolí

Bulò

Cremers

2014

Computer Graphics Forum

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ensemble of segmentations consensus segmentationFigure 1: The proposed framework allows to obtain robust surface segmentations in a principled way. Here, an initial ensemble of 50 segmentations (we show only 5 for visualization purposes) is generated via a clustering process on the Global Point Signature embedding of the shape. Given this ensemble, the corresponding consensus is defined as the unknown segmentation that is as close as possible to all the others. Note that the number of regions in the ensemble and in the final consensus segmentation are not necessarily the same. The detected regions are stable across non-rigid deformations of the shape. AbstractWe consider the problem of stable region detection and segmentation of deformable shapes. We pursue this goal by determining a consensus segmentation from a heterogeneous ensemble of putative segmentations, which are generated by a clustering process on an intrinsic embedding of the shape. The intuition is that the consensus segmentation, which relies on aggregate statistics gathered from the segmentations in the ensemble, can reveal components in the shape that are more stable to deformations than the single baseline segmentations. Compared to the existing approaches, our solution exhibits higher robustness and repeatability throughout a wide spectrum of non-rigid transformations. It is computationally efficient, naturally extendible to point clouds, and remains semantically stable even across different object classes. A quantitative evaluation on standard datasets confirms the potentiality of our method as a valid tool for deformable shape analysis.

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Heat Walk: Robust Salient Segmentation of Non‐rigid Shapes

Cited by 31 publications

References 35 publications

New evaluation metrics for mesh segmentation

New evaluation metrics for mesh segmentation

Semantic Segmentation of Human Model Using Heat Kernel and Geodesic Distance

Robust Region Detection via Consensus Segmentation of Deformable Shapes

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