Section for Visual Computing

The Section for Visual Computing carries out research in image analysis and computer graphics. The goal of image analysis is to extract information from images whereas the goal of computer graphics is concerned with image synthesis. Even though the goals are converse, there is a large overlap in methodology between the two fields.

Visual Computing
Visual Computing section October 2023

The research at this section is focused on medical image analysis, geometry and appearance modelling, computer vision, multivariate statistics for images and analysis of 3D microstructures. Within these areas, our overarching research aim is to make methodological contributions of broad applicability but motivated by applications and often informed by our collaborations with partners in applied domains. Our contributions have often resulted in innovation, and we are proud of the fact that several start-ups have been founded by former members of the section.

Medical Image Analysis

Medical image analysis takes place at the intersection of image acquisition, processing, analysis and interpretation. While our focus is on the modelling and analysis of data, this can only be done through a detailed understanding of the image data and the underlying anatomical context. Our vision is to push the frontiers of integrated image acquisition, modelling and its interpretation. Our research is grounded in industrial, clinical and societal needs.

Lead researchers

Aasa Feragen Professor

Anders Nymark Christensen Associate Professor

Tim Bjørn Dyrby Professor

Marco Pizzolato Assistant Professor

Modelling Geometry and Appearance

Creating digital models of physical objects is an important concern in the field of computer graphics. A digital model of an object combines a description of the surface geometry with a description of the appearance of the object. These two descriptions can be extremely simple as, say, in the case of a sphere which reflects incident light evenly in all directions, and they can be of great complexity as is often the case when we create models of real world objects.

Our research into digital geometry and appearance is method oriented and application motivated. We develop methods for modelling geometry and appearance, creating digital twins of actual objects or processes, and analysis of geometry and materials.

Lead researchers

Jeppe Revall Frisvad Associate Professor

Computer Vision

We do research in visual data restoration, statistical image modeling, compression, visual recognition with minimal supervision, multimodal learning, camera calibration, 3D scanning and visual data restoration. Many problems in computer vision involve conditional image generation, acquisition, and display. Deep learning has greatly advanced the quality of the generated images in recent years; however challenges still remain. These problems often come with paired training data, making the image generation supervised. We develop innovative image restoration techniques using deep neural networks. These techniques can solve many problems such as image denoising, demosaicing, super-resolution, and inpainting. We also work on video frame interpolation which is able to increase the temporal resolution of a video.

Lead researchers

Dimitrios Papadopoulos Associate Professor

Siavash Arjomand Bigdeli Associate Professor

Morten Rieger Hannemose Assistant Professor

Multivariate Statistics in Image Analysis

Multivariate statistics deals with the development, computer implementation and application of methods related to the analysis of data where more than one attribute or variable is available for each observation. Typically, the data are related to physical, chemical, biological, other natural, economical or societal phenomena. Traditional multivariate statistical methods include test theory, regression analysis, discriminant analysis and classification, principal component analysis and other orthogonal transformations. We also work with iterative extensions of some of these methods and more computer intensive statistical learning based extensions such as kernel methods, binary decision tree based methods, ensemble methods, artificial neural networks, machine learning and deep learning.

Analysis of 3D Microstructure

We develop new methods for fast and accurate quantification of 3D microstructure from large 3D volumes. Our research is conducted in close collaboration with scientists developing and using 3D imaging. Together, we promote 3D imaging as a reliable tool for measuring microstructure. Thanks to the construction of the large-scale facilities MAX IV and ESS in Lund, Sweden, there is now a fantastic opportunity for 3D imaging at extremely high spatial and temporal resolutions close to DTU. An obstacle in fully utilizing this opportunity is a time-consuming image analysis, which is challenged by the size and complexity of the acquired data sets. There is therefore a need for expertise and new analysis tools for 3D imaging. Developing this expertise and associated tools is our research focus. We have established the QIM: The Center for Quantification of Imaging Data in a collaboration with University of Copenhagen and Lund University. QIM is also a part of the DTU 3D Imaging Center - 3DIM, which is our X-ray µCT laboratory that is also tied closely to the DANMAX beamline at MAX IV.

Lead researchers

Anders Nymark Christensen Associate Professor

Allan Aasbjerg Nielsen Emeritus, Associate Professor

Lead researchers

Hans Martin Kjer Researcher

Vedrana Andersen Dahl Associate Professor

Marco Pizzolato Assistant Professor

Anders Bjorholm Dahl Professor, Head of Section

3D printet rabbits for use in VR calibration