Hierarchical Skull Registration Method with a Bounded Rotation Angle

Liu, Xiaoning; Zhu, Lipin; Liu, Xiongle; Lu, Yanning; Wang, Xiaodong

doi:10.1007/978-3-319-63315-2_49

Cited by 4 publications

(2 citation statements)

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“…Therefore, an initial guess is very important for ICP algorithms to ensure that the correct transformation is going to be found. Before performing the point cloud registration process, it makes sense to overcome getting stuck in the local minimum problem by running an initial guess computation algorithm like covariance matrix-based approach by [47] or adding some constraints to the cost function such as bounding rotation angle (see [48,49] studies). However, since the aim of this study is to show the matching performance of the proposed registration algorithm under effects such as noises, deformations, and/or outliers, an initial guess algorithm was not used in experimental verification tests.…”

Section: Discussionmentioning

confidence: 99%

Robust affine registration method using line/surface normals and correntropy criterion

Yïlmaz

Temeltaş

2022

Complex Intell. Syst.

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The problem of matching point clouds is an efficient way of registration, which is significant for many research fields including computer vision, machine learning, and robotics. There may be linear or non-linear transformation between point clouds, but determining the affine relation is more challenging among linear cases. Various methods have been presented to overcome this problem in the literature and one of them is the affine variant of the iterative closest point (ICP) algorithm. However, traditional affine ICP variants are highly sensitive to effects such as noises, deformations, and outliers; the least-square metric is substituted with the correntropy criterion to increase the robustness of ICPs to such effects. Correntropy-based robust affine ICPs available in the literature use point-to-point metric to estimate transformation between point clouds. Conversely, in this study, a line/surface normal that examines point-to-curve or point-to-plane distances is employed together with the correntropy criterion for affine point cloud registration problems. First, the maximum correntropy criterion measure is built for line/surface normal conditions. Then, the closed-form solution that maximizes the similarity between point sets is achieved for 2D registration and extended for 3D registration. Finally, the application procedure of the developed robust affine ICP method is given and its registration performance is examined through extensive experiments on 2D and 3D point sets. The results achieved highlight that our method can align point clouds more robustly and precisely than the state-of-the-art methods in the literature, while the registration time of the process remains at reasonable levels.

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Section: Discussionmentioning

confidence: 99%

Robust affine registration method using line/surface normals and correntropy criterion

Yïlmaz

Temeltaş

2022

Complex Intell. Syst.

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“…All the skulls are substantially complete; that is, each skull contains all the bones from calvarias to jaw and has the full mouth of teeth. In this paper, all skull samples are unified and normalized using reference paper [16].…”

Section: Methodsmentioning

confidence: 99%

Sex Determination of Skull Based on Fuzzy Decision Tree

Yang¹,

Xiamixiding²,

Xu³

et al. 2018

Proceedings of the 4th Workshop on Advanced Research and Technology in Industry (WARTIA 2018)

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The skeleton plays an important role in sex determination in forensic anthropology. The skull bone is considered as the second best after the pelvic bone in sex determination due to its better retention of morphological features. Different populations have varying skeletal characteristics, making population specific analysis for sex determination essential. However, most previous studies have used the traditional discriminant function method, which is highly dependent on the population, and does not consider the uncertainty in the process of sex identification. In view of the above problems, this paper proposes a method of sex identification based on fuzzy decision tree. The proposed method is to improve the uncertainty, by constructing the fuzzy function of statistical information based on the cranial measurement item, as the basis of the fuzzy decision process, the classification factor is determined at the end of the training process, and a fuzzy decision binary tree is established. The experimental results show that the proposed method is effective and can provide reference for the practical application of gender discrimination.

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