Fitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences

We propose a fully automatic method for fitting a 3D morphable model to single face images in arbitrary pose and lighting. Our approach relies on geometric features (edges and landmarks) and, inspired by the iterated closest point algorithm, is based on computing hard correspondences between model vertices and edge pixels. We demonstrate that this is superior to previous work that uses soft correspondences to form an edge-derived cost surface that is minimised by nonlinear optimisation.Comment: To appear in ACCV 2016 Workshop on Facial Informatic

Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints

The human brain can be parcellated into diverse anatomical areas. We investigated whether rhythmic brain activity in these areas is characteristic and can be used for automatic classification. To this end, resting-state MEG data of 22 healthy adults was analysed. Power spectra of 1-s long data segments for atlas-defined brain areas were clustered into spectral profiles (“fingerprints”), using k-means and Gaussian mixture (GM) modelling. We demonstrate that individual areas can be identified from these spectral profiles with high accuracy. Our results suggest that each brain area engages in different spectral modes that are characteristic for individual areas. Clustering of brain areas according to similarity of spectral profiles reveals well-known brain networks. Furthermore, we demonstrate task-specific modulations of auditory spectral profiles during auditory processing. These findings have important implications for the classification of regional spectral activity and allow for novel approaches in neuroimaging and neurostimulation in health and disease

Virtual Reality and 3D Imaging to Support Collaborative Decision Making for Adaptation of Long-Life Assets

European companies of today are involved in many stages of the product life cycle. There is a trend towards the view of their business as a complex industrial product-service system (IPSS). This trend shifts the business focus from a traditional product oriented one to a function oriented one. With the function in focus, the seller shares the responsibility of for example maintenance of the product with the buyer. As such IPSS has been praised for supporting sustainable practices. This shift in focus also promotes longevity of products and promotes life extending work on the products such as adaptation and upgrades. Staying competitive requires continuous improvement of manufacturing and services to make them more flexible and adaptive to external changes. The adaptation itself needs to be performed efficiently without disrupting ongoing operations and needs to result in an acceptable after state. Virtual planning models are a key technology to enable planning and design of the future operations in parallel with ongoing operations. This chapter presents an approach to combine digitalization and virtual reality (VR) technologies to create the next generation of virtual planning environments. Through incorporating digitalization techniques such as 3D imaging, the models will reach a new level of fidelity and realism which in turn makes them accessible to a broader group of users and stakeholders. Increased accessibility facilitates a collaborative decision making process that invites and includes cross functional teams. Through such involvement, a broader range of experts, their skills, operational and tacit knowledge can be leveraged towards better planning of the upgrade process. This promises to shorte

Patient-specific finite element estimated femur strength as a predictor of the risk of hip fracture: the effect of methodological determinants

Summary: A finite element modelling pipeline was adopted to predict femur strength in a retrospective cohort of 100 women. The effects of the imaging protocol and the meshing technique on the ability of the femur strength to classify the fracture and the control groups were analysed. Introduction: The clinical standard to estimate the risk of osteoporotic hip fracture is based on the areal bone mineral density (aBMD). A few retrospective studies have concluded that finite element (FE)-based femoral strength is a better classifier of fracture and control groups than the aBMD, while others could not find significant differences. We investigated the effect of the imaging protocol and of the FE modelling techniques on the discriminatory power of femoral strength. Methods: A retrospective cohort of 100 post-menopausal women (50 with hip fracture, 50 controls) was examined. Each subject received a dual-energy absorptiometry (DXA) exam and a computed tomography (CT) scan of the proximal femur region. Each case was modelled a number of times, using different modelling pipelines, and the results were compared in terms of accuracy in discriminating the fracture and the control cases. The baseline pipeline involved local anatomical orientation and mesh morphing. Revised pipelines involved global anatomical orientation using a full-femur atlas registration and an optimised meshing algorithm. Minimum physiological (MPhyS) and pathological (MPatS) strengths were estimated for each subject. Area under the receiver operating characteristic (ROC) curve (AUC) was calculated to compare the ability of MPhyS, MPatS and aBMD to classify the control and the cases. Results: Differences in the modelling protocol were found to considerably affect the accuracy of the FE predictors. For the most optimised protocol, logistic regression showed aBMD Neck , MPhyS and MPatS to be significantly associated with the facture status, with AUC of 0.75, 0.75 and 0.79, respectively. Conclusion: The study emphasized the necessity of modelling the whole femur anatomy to develop a robust FE-based tool for hip fracture risk assessment. FE-strength performed only slightly better than the aBMD in discriminating the fracture and control cases. Differences between the published studies can be explained in terms of differences in the modelling protocol and cohort design

Rank-based Decision Fusion for 3D Shape-based Face Recognition

Abstract. In 3D face recognition systems, 3D facial shape information plays an important role. Various shape representations have been proposed in the literature. The most popular techniques are based on point clouds, surface normals, facial profiles, and statistical analysis of depth images. The contribution of the presented work can be divided into two parts: In the first part, we have developed face classifiers which use these popular techniques. A comprehensive comparison of these representation methods are given using 3D RMA dataset. Experimental results show that the linear discriminant analysis-based representation of depth images and point cloud representation perform best. In the second part of the paper, two different multiple-classifier architectures are developed to fuse individual shape-based face recognizers in parallel and hierarchical fashions at the decision level. It is shown that a significant performance improvement is possible when using rank-based decision fusion in ensemble methods.