@inproceedings{vucini-2011, title = "Enhancing Visualization with Real-Time Frequency-based Transfer Functions", author = "Erald Vucini and Daniel Patel and Eduard Gr\"{o}ller", year = "2011", abstract = "Transfer functions have a crucial role in the understanding and visualization of 3D data. While research has scrutinized the possible uses of one and multi-dimensional transfer functions in the spatial domain, to our knowledge, no attempt has been done to explore transfer functions in the frequency domain. In this work we propose transfer functions for the purpose of frequency analysis and visualization of 3D data. Frequency-based transfer functions offer the possibility to discriminate signals, composed from different frequencies, to analyze problems related to signal processing, and to help understanding the link between the modulation of specific frequencies and their impact on the spatial domain. We demonstrate the strength of frequency-based transfer functions by applying them to medical CT, ultrasound and MRI data, physics data as well as synthetic seismic data. The interactive design of complex filters for feature enhancement can be a useful addition to conventional classification techniques.", month = jan, isbn = "978-0-8194-8405-5", series = "7868", organization = "IS&T/SPIE", location = "San Francisco, USA", booktitle = "Proceedings of IS&T/SPIE Conference on Visualization and Data Analysis", pages = "78680L-1--78680L-12", keywords = "Real Time, Data Enhancement, Frequency Analysis, Transfer Function, Volume Rendering", URL = "https://www.cg.tuwien.ac.at/research/publications/2011/vucini-2011/", } @phdthesis{vucini-2009-phd, title = "On Visualization and Reconstruction from Non-uniform Point Sets", author = "Erald Vucini", year = "2009", abstract = "Technological and research advances in both acquisition and simulation devices provide continuously increasing high-resolution volumetric data that by far exceed today's graphical and display capabilities. Non-uniform representations offer a way of balancing this deluge of data by adaptively measuring (sampling) according to the importance (variance) of the data. Also, in many real-life situations the data are known only on a non-uniform representation. Processing of non-uniform data is a non-trivial task and hence more difficult when compared to processing of regular data. Transforming from non-uniform to uniform representations is a well-accepted paradigm in the signal processing community. In this thesis we advocate such a concept. The main motivation for adopting this paradigm is that most of the techniques and methods related to signal processing, data mining and data exploration are well-defined and stable for Cartesian data, but generally are non-trivial to apply to non-uniform data. Among other things, this will allow us to better exploit the capabilities of modern GPUs. In non-uniform representations sampling rates can vary drastically even by several orders of magnitude, making the decision on a target resolution a non-trivial trade-off between accuracy and efficiency. In several cases the points are spread non-uniformly with similar density across the volume, while in other cases the points have an enormous variance in distribution. In this thesis we present solutions to both cases. For the first case we suggest computing reconstructions of the same volume in different resolutions based on the level of detail we are interested in. The second case scenario is the main motivation for proposing a multi-resolution scheme, where the scale of reconstruction is decided adaptively based on the number of points in each subregion of the whole volume. We introduce a novel framework for 3D reconstruction and visualization from non-uniform scalar and vector data. We adopt a variational reconstruction approach. In this method non-uniform point sets are transformed to a uniform representation consisting of B-spline coefficients that are attached to the grid. With these coefficients we can define a C2 continuous function across the whole volume. Several testings were performed in order to analyze and fine-tune our framework. All the testings and the results of this thesis offer a view from a new and different perspective to the visualization and reconstruction from non-uniform point sets.", month = nov, address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria", school = "Institute of Computer Graphics and Algorithms, Vienna University of Technology ", URL = "https://www.cg.tuwien.ac.at/research/publications/2009/vucini-2009-phd/", } @article{vucini_2009, title = "On Visualization and Reconstruction from Non-Uniform Point Sets using B-splines", author = "Erald Vucini and Torsten M\"{o}ller and Eduard Gr\"{o}ller", year = "2009", abstract = "In this paper we present a novel framework for the visualization and reconstruction from non-uniform point sets. We adopt a variational method for the reconstruction of 3D non-uniform data to a uniform grid of chosen resolution. We will extend this reconstruction to an efficient multi-resolution uniform representation of the underlying data. Our multi-resolution representation includes a traditional bottom-up multi-resolution approach and a novel top-down hierarchy for adaptive hierarchical reconstruction. Using a hybrid regularization functional we can improve the reconstruction results. Finally, we discuss further application scenarios and show rendering results to emphasize the effectiveness and quality of our proposed framework. By means of qualitative results and error comparisons we demonstrate superiority of our method compared to competing methods", month = jun, journal = "Computer Graphics Forum", volume = "28", number = "3", note = "2nd Best Paper Award", issn = "0167-7055", pages = "1007--1014", keywords = "B-splines, Image Processing and Computer Vision, Non-uniform data, Reconstruction", URL = "https://www.cg.tuwien.ac.at/research/publications/2009/vucini_2009/", } @article{vucini_2008_rnp, title = "Efficient Reconstruction from Non-uniform Point Sets", author = "Erald Vucini and Torsten M\"{o}ller and Eduard Gr\"{o}ller", year = "2008", abstract = "We propose a method for non-uniform reconstruction of 3D scalar data. Typically, radial basis functions, trigonometric polynomials or shift-invariant functions are used in the functional approximation of 3D data. We adopt a variational approach for the reconstruction and rendering of 3D data. The principle idea is based on data fitting via thin-plate splines. An approximation by B-splines offers more compact support for fast reconstruction. We adopt this method for large datasets by introducing a block-based reconstruction approach. This makes the method practical for large data sets. Our reconstruction will be smooth across blocks. We give reconstruction measurements as error estimations based on different parameter settings and also an insight on the computational effort. We show that the block size used in reconstruction has a negligible effect on the reconstruction error. Finally we show rendering results to emphasize the quality of this 3D reconstruction technique.", month = jul, journal = "The Visual Computer", volume = "24", number = "7-9", note = "http://www.springerlink.com/content/r8578865643x0061/", issn = "0178-2789 (Print) 1432-2315 (Online)", pages = "555--563", keywords = "3D Object Modeling, Non-uniform Reconstruction, Variational Approximation, B-splines", URL = "https://www.cg.tuwien.ac.at/research/publications/2008/vucini_2008_rnp/", } @inproceedings{vucini_erald-2007-FRI, title = "Face Recognition under Varying Illumination", author = "Erald Vucini and Muhittin G\"{o}kmen and Eduard Gr\"{o}ller", year = "2007", abstract = "This paper proposes a novel pipeline to develop a Face Recognition System robust to illumination variation. We consider the case when only one single image per person is available during the training phase. In order to utilize the superiority of Linear Discriminant Analysis (LDA) over Principal Component Analysis (PCA) in regard to variable illumination, a number of new images illuminated from different directions are synthesized from a single image by means of the Quotient Image. Furthermore, during the testing phase, an iterative algorithm is used for the restoration of frontal illumination of a face illuminated from any arbitrary angle. Experimental results on the YaleB database show that our approach can achieve a top recognition rate compared to existing methods and can be integrated into real time face recognition system.", month = jan, isbn = "978-80-86943-01-5", series = "WSCG’2007 Full Papers Proceedings", publisher = "University of West Bohemia", organization = "WSCG", note = "Full Paper", location = "Plzen, Czech Republic", address = "University of West Bohemia, Univerzitni 8, Box 314, CZ 306 14 Plzen, Czech Republic", editor = "Vaclav Skala", booktitle = "15th WSCG 2007", pages = "57--64", keywords = "Dimensionality Reduction, Face Recognition, Image Synthesis, Illumination Restoration", URL = "https://www.cg.tuwien.ac.at/research/publications/2007/vucini_erald-2007-FRI/", } @mastersthesis{vucini_2006, title = "FACE RECOGNITION UNDER VARYING ILLUMINATION", author = "Erald Vucini", year = "2006", month = jun, note = "Istanbul Technical University", address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria", school = "Institute of Computer Graphics and Algorithms, Vienna University of Technology ", URL = "https://www.cg.tuwien.ac.at/research/publications/2006/vucini_2006/", }