Affordable interactive virtual reality system for the Dynamic Hip Screw surgery training in vitro

Interactive virtual reality systems provide safe and cost-effective training environment to improve the technical skills and competence of surgeons. The trainees can have as many practice sessions, without need to the trainer all the time, before even start carrying out the procedure on any real patient. In this paper, we present an affordable interactive virtual reality system for the Dynamic Hip Screw (DHS) surgery training in vitro, through 3D tracking. The system facilitates a safe (in vitro / off patient) training to improve the cognitive coordination of trainees and junior surgeons, in particular the Hands, Eyes and Brain coordination. The system is based on very cheap commercial off-the-shelf (COT) components, which are very affordable, and needs minimum setup effort and knowledge. It also provides a range of visual and quantitative feedback information and measures, such as position, orientation, insertion point, and depth of drilling. It is envisaged that improving this level of coordination, through the training system, will contribute to reducing the failure rate of the DHS procedure. This means better treatment for patients and less costs for the Health services systems (e.g. UK's NHS system)

Fitting 3D Morphable Models using Local Features

In this paper, we propose a novel fitting method that uses local image features to fit a 3D Morphable Model to 2D images. To overcome the obstacle of optimising a cost function that contains a non-differentiable feature extraction operator, we use a learning-based cascaded regression method that learns the gradient direction from data. The method allows to simultaneously solve for shape and pose parameters. Our method is thoroughly evaluated on Morphable Model generated data and first results on real data are presented. Compared to traditional fitting methods, which use simple raw features like pixel colour or edge maps, local features have been shown to be much more robust against variations in imaging conditions. Our approach is unique in that we are the first to use local features to fit a Morphable Model. Because of the speed of our method, it is applicable for realtime applications. Our cascaded regression framework is available as an open source library (https://github.com/patrikhuber).Comment: Submitted to ICIP 2015; 4 pages, 4 figure

A 3D Face Modelling Approach for Pose-Invariant Face Recognition in a Human-Robot Environment

Face analysis techniques have become a crucial component of human-machine interaction in the fields of assistive and humanoid robotics. However, the variations in head-pose that arise naturally in these environments are still a great challenge. In this paper, we present a real-time capable 3D face modelling framework for 2D in-the-wild images that is applicable for robotics. The fitting of the 3D Morphable Model is based exclusively on automatically detected landmarks. After fitting, the face can be corrected in pose and transformed back to a frontal 2D representation that is more suitable for face recognition. We conduct face recognition experiments with non-frontal images from the MUCT database and uncontrolled, in the wild images from the PaSC database, the most challenging face recognition database to date, showing an improved performance. Finally, we present our SCITOS G5 robot system, which incorporates our framework as a means of image pre-processing for face analysis

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