Visualization based on interactive clipping: application to confocal data

We have explored how clipping can be exploited in an interactive manner to visualize massive three-dimensional datasets. In essence, the proposed interactive clipping approach involves the dynamic adjustment of the clipping plane to expose any cross-section of the volume data and subsequent adjustment of the clipped surface to the best view position using a combination of rotation and translation. The thesis describes the design, implementation and application of our interactive-clipping-based visualization system. The implementation is done with OpenGL and C++, thus resulting in a highly portable and flexible system. For illustration, two types of scientific datasets, confocal data of a plant stem and calculated electronic charge density distributions are successfully visualized. The data are displayed using pixel- and texture-based rendering; the latter is shown to give a better performance

Multiple dataset visualization (MDV) framework for scalar volume data

Many applications require comparative analysis of multiple datasets representing different samples, conditions, time instants, or views in order to develop a better understanding of the scientific problem/system under consideration. One effective approach for such analysis is visualization of the data. In this PhD thesis, we propose an innovative multiple dataset visualization (MDV) approach in which two or more datasets of a given type are rendered concurrently in the same visualization. MDV is an important concept for the cases where it is not possible to make an inference based on one dataset, and comparisons between many datasets are required to reveal cross-correlations among them. The proposed MDV framework, which deals with some fundamental issues that arise when several datasets are visualized together, follows a multithreaded architecture consisting of three core components, data preparation/loading, visualization and rendering. The visualization module - the major focus of this study, currently deals with isosurface extraction and texture-based rendering techniques. For isosurface extraction, our all-in-memory approach keeps datasets under consideration and the corresponding geometric data in the memory. Alternatively, the only-polygons- or points-in-memory only keeps the geometric data in memory. To address the issues related to storage and computation, we develop adaptive data coherency and multiresolution schemes. The inter-dataset coherency scheme exploits the similarities among datasets to approximate the portions of isosurfaces of datasets using the isosurface of one or more reference datasets whereas the intra/inter-dataset multiresolution scheme processes the selected portions of each data volume at varying levels of resolution. The graphics hardware-accelerated approaches adopted for MDV include volume clipping, isosurface extraction and volume rendering, which use 3D textures and advanced per fragment operations. With appropriate user-defined threshold criteria, we find that various MDV techniques maintain a linear time-N relationship, improve the geometry generation and rendering time, and increase the maximum N that can be handled (N: number of datasets). Finally, we justify the effectiveness and usefulness of the proposed MDV by visualizing 3D scalar data (representing electron density distributions in magnesium oxide and magnesium silicate) from parallel quantum mechanical simulation

Multi-dimensional volume rendering for PC- based medical simulation

Ph.DDOCTOR OF PHILOSOPH

Génération et édition de textures géométriques représentées par des ensembles de points

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal