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In this paper we present a novel sensing system, robust Near-infrared Structured Light Scanning (NIRSL) for three-dimensional human model scanning application. Human model scanning due to its nature of various hair and dress appearance and body motion has long been a challenging task. Previous structured light scanning methods typically emitted visible coded light patterns onto static and opaque objects to establish correspondence between a projector and a camera for triangulation. In the success of these methods rely on scanning objects with proper reflective surface for visible light, such as plaster, light colored cloth. Whereas for human model scanning application, conventional methods suffer from low signal to noise ratio caused by low contrast of visible light over the human body. The proposed robust NIRSL, as implemented with the near infrared light, is capable of recovering those dark surfaces, such as hair, dark jeans and black shoes under visible illumination. Moreover, successful structured light scan relies on the assumption that the subject is static during scanning. Due to the nature of body motion, it is very time sensitive to keep this assumption in the case of human model scan. The proposed sensing system, by utilizing the new near-infrared capable high speed LightCrafter DLP projector, is robust to motion, provides accurate and high resolution three-dimensional point cloud, making our system more efficient and robust for human model reconstruction. Experimental results demonstrate that our system is effective and efficient to scan real human models with various dark hair, jeans and shoes, robust to human body motion and produces accurate and high resolution 3D point cloud.

Twenty-three Taiwanese infants with unilateral cleft lip and palate (UCLP) were CT-scanned before lip repair at the age of 3 months, and again after lip repair at the age of 12 months. In order to evaluate the surgical result, detailed point correspondence between pre- and post-surgical images was needed. We have previously demonstrated that non-rigid registration using B-splines is able to provide automated determination of point correspondences in populations of infants without cleft lip. However, this type of registration fails when applied to the task of determining the complex deformation from before to after lip closure in infants with UCLP. The purpose of the present work was to show that use of prior information about typical deformations due to lip closure, through the construction of a knowledge-based atlas of deformations, could overcome the problem. Initially, mean volumes (atlases) for the pre- and post-surgical populations, respectively, were automatically constructed by non-rigid registration. An expert placed corresponding landmarks in the cleft area in the two atlases; this provided prior information used to build a knowledge-based deformation atlas. We model the change from pre- to post-surgery using thin-plate spline warping. The registration results are convincing and represent a first move towards an automatic registration method for dealing with difficult deformations due to this type of surgery.

We address the problem of intra-subject registration for change detection. The goal is to separate stationary and changing subsets to be able to robustly perform rigid registration on the stationary subsets and thus improve the subsequent change detection. An iterative approach using a hybrid of parametric and non-parametric statistics is presented. The method uses non-parametric clustering and large scale hypothesis testing with estimation of the empirical null hypothesis. The method is successfully applied to 3D surface scans of human ear impressions containing true changes as well as data with synthesized changes. It is shown that the method improves registration and is capable of reducing the difference between registration using different norms.

This work describes a non-rigid registration method for open 2D manifold embedded in 3D Euclidian space. The method is based on difference of distance maps and grid based warps interpolated by splines constrained in such a way that the deformation field is diffeomorphic. We then create a dense surface to surface correspondence using angle weighted normals and ray tracing. The implementation using a derivation of the inverse compositional algorithm for optimization of computational speed is described. The results are evaluated as a shape model showing the principal modes of variation.

We evaluate a novel method for fully automated rigid registration of 2D manifolds in 3D space based on distance maps, the Gibbs sampler and Iterated Conditional Modes (ICM). The method is tested against the ICP considered as the gold standard for automated rigid registration. Furthermore, the influence of different norms and sampling point densities is evaluated. The performance of the two methods has been evaluated on data consisting of 178 scanned ear impressions taken from the right ear. To quantify the difference of the two methods we calculate the registration cost and the mean point to point distance. T-test for common mean are used to determine the performance of the two methods (supported by a Wilcoxon signed rank test). The performance influence of sampling density, sampling quantity, and norms is analyzed using a similar method.

Today the design of custom completely-in-the-canal hearing aids is a manual process and therefore there is a variation in the quality of the finished hearing aids. Especially the placement of the so-called faceplate on the hearing aid strongly influences the size and shape of the hearing aid. Since the future hearing aid production will be less manual there is a need for algorithms that mimic the craftsmanship of skilled operators. In this paper it is described how a statistical shape model of the ear canal can be used to predict the placement of the faceplate on a hearing aid made for a given ear canal. The shape model is a point distribution model built using a training set of shapes with manually placed landmarks. An interpolation method is used to generate dense landmark correspondence over the training set prior to building the shape model. Faceplates have also been placed on the training shapes by a skilled operator. These faceplate planes are aligned to the average shape from the shape model and an average faceplate plane is calculated. Given a surface representation of a new ear canal, the shape model is fitted using a combination of the iterative closest point algorithm and the active shape model approach. The average faceplate from the training set can now be placed on the new ear canal using the position of the fitted shape model. A leave-one-out study shows that the algorithm is able to produce results comparable to a human operator.