Learning a statistical 3D geometric head model

Veterinary dental cleaning prevents and treats periodontal disease, one of the most common diagnoses in small animal practice. Students learn to perform dental cleaning through deliberate practice, which can be gained through working on models. This study compared educational outcomes after students ( n = 36) were randomized to practice on one of three dental cleaning models: a low-fidelity ceramic tile, a mid-fidelity three-dimensional (3D) printed canine skull model, or a high-fidelity canine head model. Students provided survey feedback about their model and later performed a dental cleaning on a canine cadaver head while being video-recorded. Experts ( n = 10) provided feedback on each model. Experts agreed that all models were suitable for teaching dental cleaning, but the 3D skull and full head models were more suitable for assessing student skill ( p = .002). Students were also more positive about the realism and features of those two models compared to the tile model. Students practicing on each of the models were equally effective at removing calculus from the cadavers’ teeth. Students who learned on the tile model were a median of 4 minutes slower to remove calculus from their cadaver’s teeth than students who trained on the canine head model. Although students may be more accepting of the 3D skull and full head models, all three models were equally effective at teaching the skill. Experts approved all models for teaching, but recommended the 3D skull or full head model if student skills were to be assessed. Low-fidelity models remain effective training tools with comparable learning outcomes.

ABSTRACTHuman head is one of the most important parts of the body, as it houses brain and other sensory organs, which controls functioning and working of the whole body. The products used for head and face are designed for functions like protection, information transfer, healthcare or to improve the aesthetic appearance. In order for them to serve their purpose, they need a close fit and in order to make it more ergonomic, user's comfort also needs to be addressed, thereby making it necessary to acquire accurate anthropometric data for ergonomic product design. Traditional techniques involve manual measurement using tapes, callipers and scales which normally have low reliability and low accuracy. With the advancement in image processing and computer aided designing and modelling techniques it has become possible to develop highly accurate and reliable 3D head and face model. Following paper presents a systematic review of different approaches that have been proposed for developing 3D head and face model and also the techniques used in processing and analysis of 3D data and their limitations. The paper also presents application of head and face models for ergonomic product design.

Natural Human-Computer Interface requires integration of realistic audio and visual information for perception and display. An example of such an interface is an animated talking head displayed on the computer screen in the form of a human-like computer agent. This system converts text to acoustic speech with synchronized animation of mouth movements. The talking head is based on a generic 3D human head model, but to improve realism, natural looking personalized models are necessary. In this paper we report results in adapting a generic head model to 3D range data of a human head obtained from a 3D laser range scanner. This personalized model is incorporated into the talking head system. With texture mapping, the personalized model offers a more natural and realistic look than the generic model.

As important non-invasive techniques in brain stimulation, transcranial direct cur- rent stimulation (tDCS) and transcranial magnetic stimulation (TMS) have been studied and compared in this paper by employing impedance method and a 3D human head model. The quantitative analysis of distributions of current density and electric fleld by tDCS and TMS have been presented. Results are compared and potential applications are discussed. DOI: 10.2529/PIERS060906193504 In this paper we use a 3D human head model obtained from Brooks Air Force Laboratory, USA. The model which has 24 difierent tissues is based on anatomical slices from a male cadaver and it is originally obtained from the Visible Human Project. The electrical properties are modeled using the 4-Cole-Cole model (5). Typical sliced layer in the head model and tissue colour palette for part of the tissues are shown in Fig. 1. Conductivities of some important tissues used in the present paper are given in Table 1.

This paper presents a shape analysis on orthogonal feature curves of 3D bald head scans with the intention of predicting scalp shape under the hair. While there are currently a number of large scale 3D head data collections available around the world, they unfortunately all suffer from hair obstruction preventing an accurate description of true scalp shape. This study is aimed at exploring the relationship between a small set of head anthropometric measurements and the feature curves of the scalp shape based on a small set of 3D bald head scans. The feature curves include scalp profile along the sagittal plane, coronal plane and a cross-sectional curve at the level of glabella.Introduction: The ever increasing availability of 3D scans of the human head has been a valuable source of information for improving head shape related equipment design and engineering with the goal of improving overall fit, sizing and comfort. However the existence of the hair prevents the actual recording and analysis of true scalp shape, and thus the analysis of scalp shape remains an elusive work. For this reason, much of the data analysis based on 3D head scans is mostly limited on facial shape and some facial landmarks. To overcome this limitation, it is desirable to be able to reconstruct the cranium shape from a few easy to obtain anthropometric measurements. In order to achieve this goal we obtained three orthogonal feature curves from 83 bald head scans and build regression equation between principal component scores of these curves and a number of head measurements such as head length, head breadth and tragion-to-top of head distance. The prediction error of these equations are evaluated. The approach: A total of 83 bald head scans taken in the 2012 anthropometric survey of US Army personnel (ANSUR II) from male soldiers are available for the analysis. From these bald head scans we identified three feature curves that principally define the scalp shape. These curves are a profile along the sagittal plane, a profile curve along the coronal plane and a cross-sectional curve at glabella level. These curves are then sampled in an equal angular space and grouped by their respective plane. The collection of each feature curves forms a shape space. We applied Principal Component Analysis (PCA) to those shape curves and decomposed the shape variation of each curve group into its respective principal axes. After conducting the PCA each curve group has a reduced dimension of 4~5 principal components that account for 95% of total variance. It shows major shape variations within each feature curve group. Then we apply multiple linear regression to the above head measurements and PCA scores of each curve group (Principal component regression or PCR). The regression equations for each curve and each PC were evaluated for their predicting power. From these regression equations the constituent feature curves can be selected from respective shape space based on head anthropometric measurements. Then PCR was also applied to the shape space of combined three curves as compared to prediction power in individual curve spaces.The results and discussion: PCR results from all orthogonal curve spaces and combined three curve space have similar predicting power as their R-squared value falling between 0.6 ~ 0.7. With a set of feature curves it is possible to further derive a 3D shape of the scalp. Although the three head measurements used in this study are effectively correlated to the first and second principal component in each group, other principal components contribute to subtle shape variations. This is an area need further analysis. We will also investigate methods to predicting 3D scalp shape from three feature curves and accuracy of the reconstruction of 3D scalp from those curves.

Factor analysis aims to reduce the amount of information from the original variable without losing important information. A factor is a collection of reduced variables. Several methods can be used in factor analysis, the most common of which are principal component analysis and maximum likelihood. The principal component analysis technique was used in this study. This technique was chosen because it better represents the purpose of the analysis than others. A case study of beef gravel is used to supplement the explanation of factor analysis using the Principal Component Analysis (PCA) technique. The goal of this study was to identify the most important factors influencing beef gravel quality to maintain product quality. The main factor is the volume of soaking water, and the second is the volume of soaking two dishes. These two factors have a cumulative variance proportion of 55%, indicating that they can influence the quality of the gravel. In contrast, the rest can be influenced by other factors not revealed in the research model.

Urmia Lake in the northwestern of Iran is a hypersaline water body and has become an environmentally important issue especially due to the presence of an infrequent aquatic species, Artemia Urmiana. During the last three decades, several considerable man-made changes including river damming and construction of a causeway across the lake affected the lake salinity. This article aims to propose a new approach of salinity modeling using a reduced-order model based on MIKE21 simulation model, in conjunction with principal component analysis (PCA) technique. At first, spatial variation of salinity in the lake was simulated by MIKE21 to prepare the input information for the PCA. Then, the dominant modes of salinity were determined by PCA technique while MIKE21 simulated results were compared with the output of developed reduced order model. Findings indicated that MIKE21's results closely matched the experimental data collected by field study. Also, the first 10 PCs among 974 modes computed by the reduced order model conserved approximately over 93% of the system variance. Therefore, the reduced order model was sufficient to capture the variation of salinity in the lake using a few first PCs. In other words, it was generally found that improvements in the simulated salinity in the lake provided by reduced order model were comparable to MIKE21 simulations.

A distance-dependent non-linear statistical model of the active part of a very short-length HEMT-based MMIC, expressed in terms of principal components, is presented. A statistical model has been extracted for 0.1 /spl mu/m GaAs HEMT devices and MMIC's. Validation of the model is presented, based on principal component analysis and statistical hypothesis testing.

Introduction: One of the stringent assumptions about covariates in the Cox hazard and Logistic regression modelling is that they should be independent. Incorporating correlated covariates as such into the model might distort the precision of the estimates due to multicollinearity. One way to deal with multicollinearity is by using Principal Component Analysis (PCA) technique. Aim: To demonstrate the application of PCA in dealing with correlated covariates while modelling time to event and casecontrol study data. Materials and Methods: This study was conducted at Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India, from February 2021 to January 2022. Two datasets were used for the demonstration i.e., data relates to a time to event outcome and a case-control study with binary outcome in which lipids were the correlated covariates. Three sets of Cox regression models were used to demonstrate change in hazard ratios with 95% Confidence Intervals (CI) for evaluating the effect of intervention at a different time of lipid measurement. Model I has evaluated treatment/ Body Mass Index (BMI) effect on the outcome by ignoring the effect of lipid parameters. Model II has evaluated treatment/ BMI effect on the outcome by incorporating lipid variables but ignoring multicollinearity. Model III has evaluated treatment/ BMI effect on the outcome by incorporating lipid variables through principal component analysis and thus adjusting for multicollinearity. Similarly, a logistic regression model was performed by using the same three sets of models to evaluate the effect of exposure (BMI). The comparability of lipids between the two groups for both datasets was tested using Hotelling’s T-squared statistic. Results: The lipids measured at 12th, 24th and 36th months between the two groups in the first data set as well as between cases and controls in the second data set were statistically significant. In the first dataset, at baseline, the Hazard Ratio’s (HR’s) were statistically similar irrespective of the models used; while decreasing successively with narrowing 95% CI’s as moving from model I to model III for the lipid measured at 12th, 24th and 36th months. Further, at 24th and 36th months, the HR in model-III found to be significant. In the second data set, the Odds Ratio (OR) were significant for all the three models and it was almost similar for model I and II but in model III it was elevated. Conclusion: The multicollinearity issue should be properly addressed before including correlated covariates in the Cox regression hazard and Logistic regression model. The PCA technique would be a favourable method.

Nowadays, micro-machining processes are playing an important role because of the technological developments in the MEMS, electrical, medical and aerospace industries. In this research work, the principal component analysis (PCA) technique is used to examine and explains the influences of three process parameters (spindle speed, feed rate and depth of cut) on the output responses such as MRR and Ra in micro-end milling process. Also this approach is used for the optimisation of multiple responses in micro-end milling operation to achieve maximum metal removal rate (MRR) and minimum surface roughness (Ra). Further, the optimised machining parameters were determined considering the multiple response objective using multiple response performance index value. The predicted value of multiple response performance index (Z) is 0.6412 which is calculated by using additive model. The measured value of multiple response performance index is 0.707, which shows good prediction accuracy. From the experimental result, it was concluded that, the PCA technique is suitable for the optimisation of multi-response problem.

In this paper, an application of the Principal Component Analysis (PCA) technique to data-driven approach is addressed. The proposed scheme is applied to OFSP-based adaptive output feedback control systems. It is well recognized that an adaptive control system based on OFSP conditions can achieve asymptotic stability via a static output feedback. However, these conditions are very restrictive on most realistic systems. Therefore, a parallel feedforward compensator (PFC) is introduced to alleviate such restrictions, but remains a steady state error which is removed by utilizing the feedforward input. Besides, the data-driven approach is one of effective control strategies for nonlinear systems in the process control, and the control parameters can be updated rapidly at each equilibrium point. However, there is no research work that had been done in terms of applying the data-driven approach in an adaptive control system based on OFSP. Therefore, once the robust stability of the adaptive control system based on OFSP can be guaranteed, the adaptive gains can be optimized by the data-driven approach such that the output performance is able to be improved. Moreover, the PCA technique is considered to imporove the data-driven approach in terms of computional cost. The proposed scheme is verified through a numerical example, the results of which demonstrate the effectiveness.

We present HeadArtist for 3D head generation following human-language descriptions. With a landmark-guided ControlNet serving as a generative prior, we come up with an efficient pipeline that optimizes a parameterized 3D head model under the supervision of the prior distillation itself. We call such a process self score distillation (SSD). In detail, given a sampled camera pose, we first render an image and its corresponding landmarks from the head model, and add some particular level of noise onto the image. The noisy image, landmarks, and text condition are then fed into a frozen ControlNet twice for noise prediction. We conduct two predictions via the same ControlNet structure but with different classifier-free guidance (CFG) weights. The difference between these two predicted results directs how the rendered image can better match the text of interest. Experimental results show that our approach produces high-quality 3D head sculptures with rich geometry and photo-realistic appearance, which significantly outperforms state-of-the-art methods. We also show that our pipeline supports editing operations on the generated heads, including both geometry deformation and appearance change. Project page:https://kumapowerliu.github.io/HeadArtist.

An improved weighted recursive PCA algorithm for adaptive fault detection

view Abstract Citations (18) References (31) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS The Luminosity Structure and Objective Classification of Galaxies Han, Mingsheng Abstract The luminosity structure of spiral galaxies is studied using the technique of principal component analysis. It is found that ~94% of the variation in the luminosity distribution of galaxies can be accounted for by just two principal components. The principal luminosity components may contain valuable information about star formation history or whatever luminosity-regulating processes occur in galaxies. Practically, these principal components provide a new approach for the investigation of the luminosity structures of galaxies and their dependence on other properties. They also serve as an excellent objective classification system for galaxies. We introduce in this paper such a classification scheme and explore its various properties. The new system shows a number of very impressive characteristics. Most important, it can well segregate virtually all the important galactic properties we tested and does so much better than the conventional morphological classification systems. Of particular interest is that some distance-dependent parameters can also be determined to a surprisingly good accuracy; for example, absolute magnitude may be determined to an accuracy of ~0.6 mag (yet further improvement is believed highly possible). Second, the system is objective, and the classification procedure can be automated to a large degree; also the new system can apply to much smaller and fainter images than do any eye-based classification systems. These properties make the new system suitable for practical application, especially on very large (and deeper) digital image catalogs. Third, the classification is expressed in dimensionless numbers, yet the simple notation bears significant and easily understandable meaning, making it easy and convenient to use. Finally, the new system has another extremely useful feature: it provides a very powerful and convenient platform not only for classification, but also for easily recording, examining, and studying the variations and correlations of galaxy properties-all these may be carried out graphically by using the C-vectors and the C-diagrams introduced in the paper. We will also give an example to demonstrate the use of the classification system for the study of the internal extinction problem in spiral galaxies. Publication: The Astrophysical Journal Pub Date: April 1995 DOI: 10.1086/175457 Bibcode: 1995ApJ...442..504H Keywords: Astronomical Photometry; Classifications; Luminosity; Principal Components Analysis; Spiral Galaxies; Data Correlation; Data Reduction; Data Sampling; Scaling Laws; Astronomy; GALAXIES: FUNDAMENTAL PARAMETERS; GALAXIES: PHOTOMETRY full text sources ADS |

Recently, we have seen a growing trend in the reconstruction of accurate geometric models, acquired by an active method such as laser scanning or structured light. In this paper, we address the problem of creation of 3D head models from multiple RGB-Depth cameras. We propose a quick capture procedure that aims at achieving high accuracy and low-cost 3D head models with high quality. This is processed with registration of multiple RGB-Depth cameras, consolidation and matching of raw data, surface reconstruction and mesh coloring. Experiments demonstrate that it could capture the subject in one second and reconstruct the 3D head models with photo-realistic quality in 3 minutes.