New evaluation metrics for mesh segmentation

Liu, Zhenbao; Tang, Shengjian; Bu, Shuhui; Zhang, Hao

doi:10.1016/j.cag.2013.05.021

Cited by 25 publications

(10 citation statements)

References 39 publications

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“…The second test aims to evaluate the proposed method using two evaluation measures the Adaptive Entropy Increment [24] and the Weighted Levenshtein distance [25] proposed by our research team, in order to quantify the quality of our segmentation approach. Then we perform a comparison with four well-known segmentation algorithms, which are K-Means (KM) [7], Fitting Primitives (FP) [26], Normalized Cuts (NC) and Randomized Cuts (RC) [27].…”

Section: Experimental Studymentioning

confidence: 99%

“…Our approach gives a high similarity scores compared to the ground truth segmentations and located after the Boundary learning method, which is based on a learning step. Figure.2 The obtained segmentations for each category using our approach Figure. 3 The dissimilarity scores using the Adaptive entropy increment [24] for the five compared algorithms Figure. 4 The dissimilarity scores using the Weighted Levenshtein Distance [25] for the five compared algorithms Figure. 5 The average results of the compared algorithms on the whole dataset using the Weighted Levenshtein Distance [25](WLD) and the Adaptive entropy increment (AEI) [24] Figure.…”

Section: Experimental Studymentioning

confidence: 99%

“…Figure.2 The obtained segmentations for each category using our approach Figure. 3 The dissimilarity scores using the Adaptive entropy increment [24] for the five compared algorithms Figure. 4 The dissimilarity scores using the Weighted Levenshtein Distance [25] for the five compared algorithms Figure. 5 The average results of the compared algorithms on the whole dataset using the Weighted Levenshtein Distance [25](WLD) and the Adaptive entropy increment (AEI) [24] Figure. 6 A visual comparison of some extracted models from the evaluated data set using the evaluated algorithms Figure. 7 A comparative evaluation of our approach to eight algorithms using the 3D Normalized Probabilistic Rand Index (3DNPRI) [28].…”

Section: Experimental Studymentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Hierarchical 3D Mesh Segmentation Using Negative Curvature and Dihedral Angle

Arhid¹,

Zakani²,

Bouksim³

et al. 2017

IJIES

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Decomposing a 3D mesh into significant regions is considered as a fundamental process in computer graphics, since several algorithms use the segmentation results as an initial step, such as, skeleton extraction, shape retrieval, shape correspondence, and compression. In this work, we present a new segmentation algorithm using spectral clustering where the affinity matrix is constructed by combining the minimal curvature and dihedral angles to detect both concave and convex properties of each edge. Experimental results show that the proposed method outperforms some of the existing segmentation methods, which highlight the performance of our approach.

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Section: Experimental Studymentioning

confidence: 99%

Section: Experimental Studymentioning

confidence: 99%

Section: Experimental Studymentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Hierarchical 3D Mesh Segmentation Using Negative Curvature and Dihedral Angle

Arhid¹,

Zakani²,

Bouksim³

et al. 2017

IJIES

View full text Add to dashboard Cite

show abstract

“…For space constraints, we compare with only eight related PBS methods. More PBS methods exist, and quantitative metrics can be further used to measure segmentation quality [29]. Yet, even without such extra insights, we argue that our goal of showing that surface skeletons have added both theoretical and practical value for PBS, as opposed to the well-known use of curve skeletons for PBS, is well defended.…”

Section: Robustnessmentioning

confidence: 99%

Improved Part-Based Segmentation of Voxel Shapes by Skeleton Cut Spaces

Feng

Jalba

Telea

2016

Mathematical Morphology - Theory and Applications

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Abstract:We present a re ned method for part-based segmentation of voxel shapes by constructing partitioning cuts from every voxel of the shape's medial surface. Our cuts have several desirable propertiessmoothness, tightness, and orientation with respect to the shape's local symmetry axis, making it a good segmentation tool. We analyze the space of all cuts created for a given shape and detect cuts which are good segment borders. We present a detailed analysis of the parameter space of our method, which yields good preset values for all its parameters. Our method is robust to noise, pose invariant, independent on the shape geometry and genus, and is simple to implement. We demonstrate our method for both automatic and interactive segmentation on a wide selection of 3D shapes.

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“…In contrast to mesh decomposition or surface segmentation based on the surface model [11,[23][24][25][26][27][28], the approach in this study decomposes 3D models into meaningful parts of source point clouds. This study presents a novel application of the Voronoi theory to represent and quantify the spatial structure of 3D points to segment a 3D point cloud.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Point Cluster Analysis Using a 3D Voronoi Diagram with Applications in Point Cloud Segmentation

Ying

et al. 2015

IJGI

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Three-dimensional (3D) point analysis and visualization is one of the most effective methods of point cluster detection and segmentation in geospatial datasets. However, serious scattering and clotting characteristics interfere with the visual detection of 3D point clusters. To overcome this problem, this study proposes the use of 3D Voronoi diagrams to analyze and visualize 3D points instead of the original data item. The proposed algorithm computes the cluster of 3D points by applying a set of 3D Voronoi cells to describe and quantify 3D points. The decompositions of point cloud of 3D models are guided by the 3D Voronoi cell parameters. The parameter values are mapped from the Voronoi cells to 3D points to show the spatial pattern and relationships; thus, a 3D point cluster pattern can be highlighted and easily recognized. To capture different cluster patterns, continuous progressive clusters and segmentations are tested. The 3D spatial relationship is shown to facilitate cluster detection. Furthermore, the generated segmentations of real 3D data cases are exploited to demonstrate the feasibility of our approach in detecting different spatial clusters for continuous point cloud segmentation.

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New evaluation metrics for mesh segmentation

Cited by 25 publications

References 39 publications

An Efficient Hierarchical 3D Mesh Segmentation Using Negative Curvature and Dihedral Angle

An Efficient Hierarchical 3D Mesh Segmentation Using Negative Curvature and Dihedral Angle

Improved Part-Based Segmentation of Voxel Shapes by Skeleton Cut Spaces

Point Cluster Analysis Using a 3D Voronoi Diagram with Applications in Point Cloud Segmentation

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