Coarse filters for shape matching

Corney, Jonathan; Rea, H.; Clark, D. E. R.; Pritchard, John Paul; Breaks, Michael; macleod, roddy

doi:10.1109/mcg.2002.999789

Cited by 86 publications

(51 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Corney et al [21] introduce convex-hull based indices like hull crumpliness (the ratio of the object surface area and the surface area of its convex hull), hull packing (the percentage of the convex hull volume not occupied by the object), and hull compactness (the ratio of the cubed surface area of the hull and the squared volume of the convex hull).…”

Section: Global Feature Based Similaritymentioning

confidence: 99%

A survey of content based 3D shape retrieval methods

Tangelder¹,

Veltkamp

2007

Multimed Tools Appl

722

182

View full text Add to dashboard Cite

show abstract

Section: Global Feature Based Similaritymentioning

confidence: 99%

A survey of content based 3D shape retrieval methods

Tangelder¹,

Veltkamp

2007

Multimed Tools Appl

722

182

View full text Add to dashboard Cite

show abstract

“…In geometric based approaches, a higher level of shape abstraction and information reduction is necessary to support efficient characterization of shape motions. In machine vision techniques, a shape descriptor extracts local/global geometric features from the shape [41,8,42], and then, an energy functional based on the shape descriptor is minimized to classify the shapes. The modern geometry introduces shape space, where coordinates of points in the space represent some generalized properties related to various geometrical structures [4,5].…”

Section: Introductionmentioning

confidence: 99%

Visualization of Shape Motions in Shape Space

Taimouri

Hua

2013

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Abstract-Analysis of dynamic object deformations such as cardiac motion is of great importance, especially when there is a necessity to visualize and compare the deformation behavior across subjects. However, there is a lack of effective techniques for comparative visualization and assessment of a collection of motion data due to its 4-dimensional nature, i.e., timely varying threedimensional shapes. From the geometric point of view, the motion change can be considered as a function defined on the 2D manifold of the surface. This paper presents a novel classification and visualization method based on a medial surface shape space, in which two novel shape descriptors are defined, for discriminating normal and abnormal human heart deformations as well as localizing the abnormal motion regions. In our medial surface shape space, the geodesic distance connecting two points in the space measures the similarity between their corresponding medial surfaces, which can quantify the similarity and disparity of the 3D heart motions. Furthermore, the novel descriptors can effectively localize the inconsistently deforming myopathic regions on the left ventricle. An easy visualization of heart motion sequences on the projected space allows users to distinguish the deformation differences. Our experimental results on both synthetic and real imaging data show that this method can automatically classify the healthy and myopathic subjects and accurately detect myopathic regions on the left ventricle, which outperforms other conventional cardiac diagnostic methods.

show abstract

“…Surfaces in VRML are often not manifold, precluding application of many differential geometry operators, such as the computation of curvature. Some of the existing methods assumed welldefined manifolds or even solid models, often targeting 3D CAD models [Regli00, McWerter01, Hilaga01, Novotni01, Zaharia01, Corney02,Mukai02]. Others tried to cope with ill-defined shape representations such as VRML models [Paquet97, Suzuki98, Paquet00, Osada01, Osada02, Ohbuchi02, Zahaira02, Funkhouser03, Tangelder03].…”

Section: Introductionmentioning

confidence: 99%