Development of feature segmentation algorithms for quadratic surfaces

Lai, Hou-Chuan; Chang, Ya-Fen; Lai, Jiing Yih

doi:10.1016/j.advengsoft.2009.03.017

Cited by 9 publications

(4 citation statements)

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“…Different segmentation results of a complex curved surface model can be obtained by adopting different segmentation principles [10][11][12]. At present, the quick segmentation technology for 3D model is mainly used in the fields of biomedical stereoscopic image recognition [13] and reverse engineering [14,15], however, rarely used to improve the precision of the NC machining.…”

Section: Introductionmentioning

confidence: 99%

Quick segmentation for complex curved surface with local geometric feature based on IGES and Open CASCADE

Wang

et al. 2015

Advances in Engineering Software

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Section: Introductionmentioning

confidence: 99%

Quick segmentation for complex curved surface with local geometric feature based on IGES and Open CASCADE

Wang

et al. 2015

Advances in Engineering Software

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“…Here, we list a few most important related works. In [ 4 , 5 , 6 , 7 ], mesh segmentation was conducted by fitting analytic surfaces to the mesh for reconstruction of a CAD model. In [ 8 , 9 ], the objective function was constructed by creating a database, and the database was then applied to the targeted surface mesh for analysis to obtain the resulting segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…Feature regions of a surface model are always located where meaningful parts intersect; therefore, they often have complex geometry and are difficult to be expressed mathematically. Thus the approaches of fitting analytic surfaces in [ 4 , 5 , 6 , 7 ] are inappropriate for extracting feature regions. The learning approaches in [ 8 , 9 ] based on constructing an objective function are cumbersome and not mature in practice; furthermore, due to the uncertainty of the complex geometry of feature regions, it is difficult to realize the learning approaches to extract them.…”

Section: Introductionmentioning

confidence: 99%

An Improved Approach of Mesh Segmentation to Extract Feature Regions

Duan

Wang

et al. 2015

PLoS ONE

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The objective of this paper is to extract concave and convex feature regions via segmenting surface mesh of a mechanical part whose surface geometry exhibits drastic variations and concave-convex features are equally important when modeling. Referring to the original approach based on the minima rule (MR) in cognitive science, we have created a revised minima rule (RMR) and presented an improved approach based on RMR in the paper. Using the logarithmic function in terms of the minimum curvatures that are normalized by the expectation and the standard deviation on the vertices of the mesh, we determined the solution formulas for the feature vertices according to RMR. Because only a small range of the threshold parameters was selected from in the determined formulas, an iterative process was implemented to realize the automatic selection of thresholds. Finally according to the obtained feature vertices, the feature edges and facets were obtained by growing neighbors. The improved approach overcomes the inherent inadequacies of the original approach for our objective in the paper, realizes full automation without setting parameters, and obtains better results compared with the latest conventional approaches. We demonstrated the feasibility and superiority of our approach by performing certain experimental comparisons.

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“…This process is done from the definition of a range of grayscale voxel that expresses only the voxels in the region of interest, which is called the threshold value [16,17]. The segmentation of medical images is a difficult process, mainly due to overlapping intensities, anatomical complexity, and variability in shape and size, in addition to the usual limitations in the imaging equipment or input data, such as noise perturbations, intensity inhomogeneities, partial volume effect, and low contrast [18,19].…”

Section: Introductionmentioning

confidence: 99%