A novel method of automated skull registration for forensic facial approximation

Turner, Wesley D.; Brown, Rebecca; Kelliher, Timothy P.; Tu, Peter; Taister, Myke; Miller, Kevin W.P.

doi:10.1016/j.forsciint.2004.10.003

Cited by 70 publications

(43 citation statements)

References 12 publications

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“…TPS is particularly effective for mesh models with highly complex surfaces such as brain sulci [4], lumbar vertebrae [10], and skulls [5,7,9,14,18]. Skull models are particularly complex because they have holes, missing teeth and bones, and interior as well as exterior surfaces.…”

Section: Introductionmentioning

confidence: 99%

“…The second approach automatically detects surface points on the reference mesh, which are mapped to the target surface. These surface points can be randomly sampled points [7] or distinctive feature points such as local curvature maximals [18], and they serve as soft constraints in TPS registration. This approach is sensitive to noise, outliers, and false correspondences.…”

Section: Introductionmentioning

confidence: 99%

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Dense Correspondence of Skull Models by Automatic Detection of Anatomical Landmarks

Zhang

Cheng

Leow

2013

Computer Analysis of Images and Patterns

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Abstract. Determining dense correspondence between 3D skull models is a very important but difficult task due to the complexity of the skulls. Non-rigid registration is at present the predominant approach for dense correspondence. It registers a reference model to a target model and then resamples the target according to the reference. Methods that use manually marked corresponding landmarks are accurate, but manual marking is tedious and potentially error prone. On the other hand, methods that automatically detect correspondence based on local geometric features are sensitive to noise and outliers, which can adversely affect their accuracy. This paper presents an automatic dense correspondence method for skull models that combines the strengths of both approaches. First, anatomical landmarks are automatically and accurately detected to serve as hard constraints for non-rigid registration. They ensure that the correspondence is anatomically consistent and accurate. Second, control points are sampled on the skull surfaces to serve as soft constraints for non-rigid registration. They provide additional local shape constraints for a closer match between the reference and the target. Test results show that, by combining both approaches, our algorithm can achieve more accurate automatic dense correspondence.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dense Correspondence of Skull Models by Automatic Detection of Anatomical Landmarks

Zhang

Cheng

Leow

2013

Computer Analysis of Images and Patterns

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“…In addition, as the complex deformation between the reference and the target skull, the warping methods should intensively be studied to get accurate reconstruction result. So many deformation methods are proposed to model the non-rigid shape deformation of http://asp.eurasipjournals.com/content/2012/1/217 skull and face, such as radial basis functions (RBF) [22,23], or more exactly, a thin plate spline (TPS)-based deformation [12,24,25] for its smoothness. Instead of using fixed template, the recently proposed statistical craniofacial reconstruction methods [12][13][14][15][16][17] construct a type of deformable model from a set of 3D heads by the principle component analysis (PCA) technique.…”

Section: Introductionmentioning

confidence: 99%

“…To represent the dense tissue depth exactly, the dense point registration of skull or face is usually demanded. Although many registration methods [12,24,25,36] have been proposed to construct correspondence between surfaces and point sets, it is still a challenging problem for further investigation because of the complex skull mesh with gross errors or outliers.…”

Section: Introductionmentioning

confidence: 99%

Craniofacial reconstruction based on a hierarchical dense deformable model

Duan

Zhou

et al. 2012

EURASIP J. Adv. Signal Process.

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Craniofacial reconstruction from skull has deeply been investigated by computer scientists in the past two decades because it is important for identification. The dominant methods construct facial surface from the soft tissue thickness measured at a set of skull landmarks. The quantity and position of the landmarks are very vital for craniofacial reconstruction, but there is no standard. In addition, it is difficult to accurately locate the landmarks on dense mesh without manual assistance. In this article, we propose an automatic craniofacial reconstruction method based on a hierarchical dense deformable model. To construct the model, we collect more than 100 head samples by computerized tomography scanner. The samples are represented as dense triangle mesh to model face and skull shape. As the deformable model demands all samples in uniform form, a non-rigid registration algorithm is presented to align the samples in point-to-point correspondence. Based on the aligned samples, a global deformable model is constructed, and three local models are constructed from the segmented patches of the eye, nose, and mouth. For a given skull, the global and local deformable models are matched with it, and the reconstructed facial surface is obtained by fusing the global and local reconstruction results. To validate our method, a face deformable model is constructed and the reconstruction results are evaluated in its coefficient domain. The experimental results indicate that the proposed method has good performance for craniofacial reconstruction.

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“…Novos protocolos propostos para a reconstrução facial, como a projeção nasal, do globo ocular, e a largura da abertura da cavidade bucal, foram sugeridos; teorias ortodoxas têm sido avaliadas criticamente com base em tecnologias digitais. Já é realidade o desenvolvimento de softwares de imagem 3D, com ferramentas que permitem ao usuário uma maior flexibilidade, rapidez e objetividade na realização das reconstruções Nelson;Michael, 1998;Turner et al, 2006;Turner et al, 2005;Vandermeulen et al, 2006;Vanezis et al, 1989 …”

Section: Introdução 1 Introduçãounclassified

Análise das reconstruções faciais forenses digitais caracterizadas utilizando padrões de medidas lineares de tecidos moles da face de brasileiros e estrangeiros

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A novel method of automated skull registration for forensic facial approximation

Cited by 70 publications

References 12 publications

Dense Correspondence of Skull Models by Automatic Detection of Anatomical Landmarks

Dense Correspondence of Skull Models by Automatic Detection of Anatomical Landmarks

Craniofacial reconstruction based on a hierarchical dense deformable model

Análise das reconstruções faciais forenses digitais caracterizadas utilizando padrões de medidas lineares de tecidos moles da face de brasileiros e estrangeiros

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