• Journal of KIISE:Software and Applications
• /
• v.35 no.2
• /
• pp.118-127
• /
• 2008

In this paper, we propose a method for construction of robust 3D statistical shape model in the mandible CT datasets. Our method consists of following four steps. First, we decompose a 3D input shape Into patches. Second, to generate a corresponding shape of a floating shape, all shapes in the training set are parameterized onto a disk similar to the patch topology. Third, we generate the corresponding shape by one-to-one mapping between the reference and the floating shapes. We solve the problem failed to generate the corresponding points near the patch boundary Finally, the corresponding shapes are aligned with the reference shape. Then statistical shape model is generated by principle component analysis. To evaluate the accuracy of our 3D statistical shape model of the mandible, we perform visual inspection and similarity measure using average distance difference between the floating and the corresponding shapes. In addition, we measure the compactness of statistical shape model using the modes of variation. Experimental results show that our 3D statistical shape model generated by the mandible CT datasets with various characteristics has a high similarity between the floating and corresponding shapes and is represented by the small number of modes.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.38 no.10
• /
• pp.1139-1145
• /
• 2014

Creation of a human skeleton model and characterization of the variation in the bone shape are fundamentally important in many applications of biomechanics. In this paper, we present a parametric shape modeling method for femurs that is based on extracting the main parameter of variations of the femur shape from a 3D model database by using statistical shape analysis. For this shape analysis, principal component analysis (PCA) is used. Application of the PCA to 3D data requires bringing all the models in correspondence to each other. For this reason, anatomical landmarks are used for guiding the deformation of the template model to fit the 3D model data. After subsequent application of PCA to a set of femur models, we calculate the correlation between the dominant components of shape variability for a target population and the anatomical parameters of the femur shape. Finally, we provide tools for visualizing and creating the femur shape using the main parameter of femur shape variation.

• Geomechanics and Engineering
• /
• v.21 no.4
• /
• pp.349-356
• /
• 2020

Rock brittleness, which is closely related to the failure modes, plays a significant role in the design and construction of many rock engineering applications. However, the brittle-ductile failure transition is mostly ignored by the current statistical damage constitutive model, which may misestimate the failure strength and failure behaviours of intact rock. In this study, a new statistical damage model considering rock brittleness is proposed for brittle to ductile behaviour of rocks using brittleness index (BI). Firstly, the statistical constitutive damage model is reviewed and a new statistical damage model considering failure mode transition is developed by introducing rock brittleness parameter-BI. Then the corresponding damage distribution parameters, shape parameter m and scale parameter F₀, are expressed in terms of BI. The shape parameter m has a positive relationship with BI while the scale parameter F₀ depends on both BI and ε_e. Finally, the robustness and correctness of the proposed damage model is validated using a set of experimental data with various confining pressure.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.161-164
• /
• 2003

This paper proposes a hierarchical approach to active shape model using wavelet transform. The proposed algorithm allows us to use both global shape characteristics and finer details for model deformation. The statistical properties of the wavelet transform of a deformable model are analyzed by principal component analysis and used as priors in the contour's deformation.

• Communications for Statistical Applications and Methods
• /
• v.29 no.6
• /
• pp.695-708
• /
• 2022

Recently, several studies have been conducted using state space model. In this study, a dynamic linear model with state space model form is applied to stock data. The monthly returns for 135 Korean stocks are fitted to a dynamic linear model, to obtain an estimate of the time-varying 𝛽-coefficient time-series. The model formula used for the return is a capital asset pricing model formula explained in economics. In particular, the transition equation of the state space model form is appropriately modified to satisfy the assumptions of the error term. k-shape clustering is performed to classify the 135 estimated 𝛽 time-series into several groups. As a result of the clustering, four clusters are obtained, each consisting of approximately 30 stocks. It is found that the distribution is different for each group, so that it is well grouped to have its own characteristics. In addition, a common pattern is observed for each group, which could be interpreted appropriately.

• Journal of International Society for Simulation Surgery
• /
• v.2 no.1
• /
• pp.7-9
• /
• 2015

Purpose In the many face-related application such as head pose estimation, 3D face modeling, facial appearance manipulation, the robust and fast facial feature extraction is necessary. We present the facial feature extraction method based on shape regression and feature selection for real-time facial feature extraction. Materials and Methods The facial features are initialized by statistical shape model and then the shape of facial features are deformed iteratively according to the texture pattern which is selected on the feature pool. Results We obtain fast and robust facial feature extraction result with error less than 4% and processing time less than 12 ms. The alignment error is measured by average of ratio of pixel difference to inter-ocular distance. Conclusion The accuracy and processing time of the method is enough to apply facial feature based application and can be used on the face beautification or 3D face modeling.

• Journal of the Korea Society for Simulation
• /
• v.4 no.1
• /
• pp.113-119
• /
• 1995

In 3D computer graphics, fractal techniques have been applied to terrain models. Even though fractal models have become popular for recreating a wide variety of the shapes found in nature, a specific 3D terrain model such as Uleung Island could not be formulated by statistical fractals easily owing to the random effects. However, by locating the midpoints on the edges and the surface of a specific terrain such as Uleung Island, a similar shape of the terrain model can be simulated. This paper shows the way of simulating 3D Uleung Island terrain model by the statistical fractals wherein the subdivision algorithm is used.

• Communications for Statistical Applications and Methods
• /
• v.25 no.6
• /
• pp.673-685
• /
• 2018

Procrustes analysis is a useful technique useful to measure, compare shape differences and estimate a mean shape for objects; however it is based on a least squares criterion and is affected by some outliers. Therefore, we propose two generalized Procrustes analysis methods based on M-estimation and least median of squares estimation that are resistant to object outliers. In addition, two algorithms are given for practical implementation. A simulation study and some examples are used to examine and compared the performances of the algorithms with the least square method. Moreover since these resistant GPA methods are available for higher dimensions, we need some methods to visualize the objects and mean shape effectively. Also since we have concentrated on resistant fitting methods without considering shape distributions, we wish to shape analysis not be sensitive to particular model.

• Communications for Statistical Applications and Methods
• /
• v.31 no.3
• /
• pp.309-322
• /
• 2024

Identifying algae in water is important for managing algal blooms which have great impact on drinking water supply systems. There have been various microscopic approaches developed for algae classification. Many of them are based on the morphological features of algae. However, there have seldom been mathematical frameworks for comparing the shape of algae, represented as a planar continuous curve obtained from an image. In this work, we describe a recent framework for computing shape distance between two different algae based on the elastic metric and a novel functional representation called the square root velocity function (SRVF). We further introduce statistical procedures for multiple shapes of algae including computing the sample mean, the sample covariance, and performing the principal component analysis (PCA). Based on the shape distance, we classify six algal species in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis, Oscillatoria, and Anabaena), two diatoms (Fragilaria and Synedra), and one green algae (Pediastrum). We provide and compare the classification performance of various distance-based and model-based methods. We additionally compare elastic shape distance to non-elastic distance using the nearest neighbor classifiers.

• Journal of Welding and Joining
• /
• v.5 no.4
• /
• pp.47-53
• /
• 1987

The purpose of this study is to reexamine our test method in the light ofstatistical methods for data interpretation. Our trial to apply Plackett-Burman statistical model in multifactorial welding experiments shows that is saves much time and cost and extracts very accurate results. In this study, the parametric effects of bead shape on pulse MIG process in Cu-Ni alloy are investigated for verifying our trial.