Research interests

My research interests include: geometric models for surface detection, mesh-based segmentation methods, 2D and 3D texture analysis, methods for analyzing tomographic data.

I work on applications in: food quality control, medicine and diagnostics, material science and energy materials.

Some results of a mesh-based segmentation can be seen here. Optical scans of some skulls are here.

Videos demonstrating a probabilistic framework for curve evolution are here.


For a complete and updated list of publications see my Orbit page or my DTU profile.

Selected (early) papers

Surfel Based Geometry Reconstruction

Andersen2010_Surfels We propose a method for retrieving a piecewise smooth surface from noisy data. In data acquired by a scanning process sampled points are almost never on the discontinuities making reconstruction of surfaces with sharp features difficult. Our method is based on a Markov Random Field (MRF) formulation of a surface prior, with the surface represented as a collection of small planar patches, the surfels, associated with each data point. The main advantage of using surfels is that we avoid treating data points as vertices. MRF formulation of the surface prior allows us to separately model the likelihood (related to the mesh formation process) and the local surface properties. We chose to model the smoothness by considering two terms: the parallelism between neighboring surfels, and their overlap. We have demonstrated the feasibility of this approach on both synthetical and scanned data. In both cases sharp features were precisely located and planar regions smoothed.

Markov Random Fields on Triangle Meshes

Andersen2009_MRF_meshes In this paper we propose a novel anisotropic smoothing scheme based on Markov Random Fields (MRF). Our scheme is formulated as two coupled processes. A vertex process is used to smooth the mesh by displacing the vertices according to a MRF smoothness prior, while an independent edge process labels mesh edges according to a feature detecting prior. Since we should not smooth across a sharp feature, we use edge labels to control the vertex process. In a Bayesian framework, MRF priors are combined with the likelihood function related to the mesh formation method. The output of our algorithm is a piecewise smooth mesh with explicit labelling of edges belonging to the sharp features.

Height and Tilt Geometric Texture

Andersen2009_HeightAndTilt We propose a new intrinsic representation of geometric texture over triangle meshes. Our approach extends the conventional height field texture representation by incorporating displacements in the tangential plane in the form of a normal tilt. This texture representation offers a good practical compromise between functionality and simplicity: it can efficiently handle and process geometric texture too complex to be represented as a height field, without having recourse to full blown mesh editing algorithms. The height-and-tilt representation proposed here is fully intrinsic to the mesh, making texture editing and animation (such as bending or waving) intuitively controllable over arbitrary base mesh. We also provide simple methods for texture extraction and transfer using our height-and-field representation.

Academic dissertations

3D Shape Modeling Using High Level Descriptors

Smoothing 3D Meshes using Markov Random Fields