IEEE Trans Image Process - Fast and robust 2D-shape extraction using discrete-point sampling and centerline grouping in complex images.

Tópicos

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Resumo

This paper initially develops the discrete-point sampling operator's concept, model, and parameters that we have previously proposed, and makes its belt-shaped regions in a discrete-point sampling map more salient and appropriate for centerline extraction. The cross-sectional features of these belt-shaped regions are then analyzed and seven types of feature points are defined to facilitate descriptions of such features. Based on these feature points, a three-level detection system is proposed, including feature points, line segments, and centerlines, to extract centerlines from the belt-shaped regions. Eight basic types of centerlines and five types of relationships among the centerlines are defined by computational geometry algorithms, and Gestalt laws are used to cluster them into groupings. If some prior information about a desired shape is available, retrieval grouping may be carried out by a discrete-point sampling map, the purpose of which is to find centerlines by best matching with prior information. Discrete-point sampling effectually overcomes the influences of interference from noise, textures, and uneven illumination, and greatly reduces the difficulty of centerline extraction. Centerline clustered groupings and retrieval grouping can offer a strong anti-interference ability with nonlinear deformations such as articulation and occlusion. This method can extract large-scale complex shapes combined of lines and planes from complex images. The wheel location results of noise test and other shape extraction experiments show that our method has a strong capability to persist with nonlinear deformations.

Resumo Limpo

paper initi develop discretepoint sampl oper concept model paramet previous propos make beltshap region discretepoint sampl map salient appropri centerlin extract crosssect featur beltshap region analyz seven type featur point defin facilit descript featur base featur point threelevel detect system propos includ featur point line segment centerlin extract centerlin beltshap region eight basic type centerlin five type relationship among centerlin defin comput geometri algorithm gestalt law use cluster group prior inform desir shape avail retriev group may carri discretepoint sampl map purpos find centerlin best match prior inform discretepoint sampl effectu overcom influenc interfer nois textur uneven illumin great reduc difficulti centerlin extract centerlin cluster group retriev group can offer strong antiinterfer abil nonlinear deform articul occlus method can extract largescal complex shape combin line plane complex imag wheel locat result nois test shape extract experi show method strong capabl persist nonlinear deform

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