- Show Keywords
- 3D Computer Vision 3D reconstruction Aerial Vision Augmented Reality Augmented Video Best Paper Award Biometrics Caleydo Computer Graphics Computer Vision Convex Optimization Coordinate transformations detection face Fingerprint Georeferencing GPU GUI HOG Human Computer Interaction Image Labelling Industrial Applications Information Visualization integral imaging Interaction Interaction Design Machine Learning Medical computer vision Medical Visualization Mixed Reality Mobile computing Mobile phone Model Multi-Display Environments Multiple Perspectives Object detection Object recognition Object reconstruction Object Tracking On-Line Learning Robotics Segmentation Shape analysis shape from focus SLAM Software Projects Structure from Motion Surveillance SVM Symmetry Tracking Fusion Tracking, Action Recognition User Interfaces Variational Methods Virtual reality and augmented reality Visual Tracking Visualization
ICAO Face Normalization and Analysis
The goal of this project is the research and development of state of the art computer vision and object recognition algorithms to analyze face portrait images according to the ICAO (International Civil Aviation Organization) standards and specifications. Therefore a close cooperation with Siemens IT Solutions and Services Biometric Center in Graz exists, where the Biometry group is developing a software solution for this purpose.
Current passports issued in the European Union contain biometric data like e.g. digital photographs and fingerprints in order to uniquely identify its owner. To be able to read passports all over the world, the ICAO has specified a number of guidelines and requirements for the structure of these biometric features. In case of face portrait images, examples for these requirements are neutral appearance, eyes opened, mouth closed, frontal pose, straight-looking eyes, properly-sitting eye-glasses, or uncovered faces. Since these analysis steps have to be performed in an automatic fashion, each of these requirements imposes certain computer vision research challenges which are tackled in this research project. Examples for the topics involved in these analysis steps are model-based segmentation using active shape and active appearance models, fast and robust AdaBoost based machine learning algorithms for face and face component detection, or classification of facial expressions using multi-classifier fusion approaches.
Nonlinear Registration for Intra-Modality CT Applications
Research interests in single-modality nonlinear registration include four different kinds of subproblems. Deformable registration of two or more CT lung data sets at different states in the breathing cycle going from Functional Residual Capacity (FRC, expiration) to Total Lung Capacity (TLC, inspiration) for modelling breathing motion and deriving lung ventilation. Deformable registration of a contrast-enhanced and a native CT lung data set for deriving lung perfusion. Deformable registration of contrast-enhanced and native CT liver data sets at one or several phases in the contrast-uptake cycle for liver perfusion. And finally, highly accurate partially rigid bone registration for head and neck CT-Angiography applications to extract bone structures from CTA images.
Deformable Lung Registration
The input for this task consists of native CT thorax scans at two or more different breathing states between Total Lung Capacity (TLC, inspiration) and Functional Residual Capacity (FRC, expiration). Deformable registration of distinct breathing states is a prerequisite for deriving ventilation information by simple subtraction of expiration from inspiration data or by fusion with special functional scans and it leads to models of breathing motion in the lung. For this purpose we have available high resolution sheep lung data at up to five distinct static breathing states and human lung data at inspiration/expiration. Another application of deformable lung registration is the fusion of native and contrast-enhanced CT lung data to show perfusion information again either by subtraction or by fusion with a special scan. A notion of vessel consistency should be included in the deformable registration, since it is important that the same amount of vessels is regarded before and after registration.
Deformable Liver Registration
Similar to the lung registration, liver registration for perfusion measurements is a topic of interest. Contrast-enhancing techniques are used to get up to 8 liver images at different phases of the contrast uptake cycle. Each of these images has to be registered to a native scan to correct motions due to breathing. Afterwards subtraction techniques are used to derive the amount of perfusion in the liver. The setup of the registration algorithm is very similar to the lung registration problem.
Partially Rigid Bone Registration
The intended application of rigid bone registration is a very accurate registration of bones from native and contrast-enhanced CT images of the head and the neck. In contrast-enhanced images vessels and bones have very similar intensities, such that simple segmentation algorithms like thresholding do not work which are frequently used for CTA image studies. The intended strategy for the removal of bone structures is to take a simple (threshold-based) bone segmentation taken from the native image and register it to the contrast-enhanced image. Registration is necessarysince small patient movements may occur (especially in the neck and shoulder area) between the acquisition of both kinds of images. Registration has to be very accurate in this area, since there are vessel structures that lie close to or inside the bone structures as well. It can be assumed that the bones themselves are rigid but the relative position of bones to each other may change. Pairs of bones should be registered rigidly but the relative bone movements are taken into account leading to a partially rigid registration scheme.