Clemens Blumer

Learn More
We propose a probabilistic occlusion-aware 3D Morphable Face Model adaptation framework for face image analysis based on the Analysis-by-Synthesis setup. In natural images, parts of the face are often occluded by a variety of objects. Such occlusions are a challenge for face model adaptation. We propose to segment the image into face and non-face regions(More)
Dendritic spines may be tiny in volume, but are of major importance for neuroscience. They are the main receivers for excitatory synaptic connections, and their constant changes in number and in shape reflect the dynamic connectivity of the brain. Two-photon microscopy allows following the fate of individual spines in brain slice preparations and in live(More)
Efficient motion tracking of faces is an important aspect for human computer interaction (HCI). In this paper we combine the condensation and the wavelet approximated reduced vector machine (W-RVM) approach. Both are joined by the core idea to spend only as much as necessary effort for easy to discriminate regions (Condensation) or vectors (W-RVM) of the(More)
This paper proposes to integrate a feature pursuit learning process into a greedy bottom-up learning scheme. The algorithm combines the benefits of bottom-up and top-down approaches for learning hierarchical models: It allows to induce the hierarchical structure of objects in an unsupervised manner, while avoiding a hard decision on the activation of parts.(More)
In this paper, we present a novel open-source pipeline for face registration based on Gaussian processes as well as an application to face image analysis. Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs). Gaussian Process Morphable Models (GPMMs) unify a(More)
  • 1