This paper proposes a non-self-intersecting multiscale deformable surface model with an adaptive remeshing capability. The model is specifically designed to extract the three-dimensional boundaries of topologically simple but geometrically complex anatomical structures, especially those with deep concavities such as the brain, from volumetric medical images. The model successfully addresses thr...

In this paper we address the issue of joint estimation of head pose and facial actions. We propose a method that can robustly track both subtle and extreme movements by combining two types of features: structural features observed at characteristic points of the face, and intensity features sampled from the facial texture. To handle the processing of extreme poses, we propose two innovations. T...

The aim of this work is to extract the outer skull surface from an MRI volume. Based on a 3D approach, the technique proposed takes into account the information of the skull contained in MRI volumes of the head. Our main interest in extracting the skull from MRI data is to create models of the head in order to create a database of models where the relationship between the skull and face can be ...

Deformable or active contour, and surface models are powerful image segmentation techniques. We introduce a novel fast and robust bi-directional parametric deformable model which is able to segment regions of intricate shape in multi-modal greyscale images. The power of the algorithm in terms of computation time and robustness is owing to the use of joint probabilities of the signals and region...

STATISTICAL CUE ESTIMATION FOR MODEL-BASED SHAPE AND MOTION TRACKING Siome Goldenstein Supervisor: Dimitris Metaxas Vision-based tracking of moving objects is important in many applications, ranging from sports and medicine to security and recognition of human action. In this dissertation we discuss novel methods for statistical deformable model tracking. Our main contribution is a method to es...

This work addresses the matching of a 3D deformable face model to 2D images through a 2.5D Active Appearance Models (AAM). We propose a 2.5D AAM that combines a 3D metric Point Distribution Model (PDM) and a 2D appearance model whose control points are defined by a full perspective projection of the PDM. The advantage is that, assuming a calibrated camera, 3D metric shapes can be retrieved from...

We propose a new fast facial-feature extraction technique for embedded face-recognition applications. A deformable feature model is adopted, of which the parameters are optimized to match with an input face image in two steps. First, we use a cascade of parameter predictors to directly estimate the pose (translation, scale and rotation) parameters of the facial feature. Each predictor is traine...