Feature Centric Volume Visualization
| dc.contributor.author | Malik, Muhammad Muddassir | en_US |
| dc.coverage.spatial | Vienna | en_US |
| dc.date.accessioned | 2015-01-21T06:50:08Z | |
| dc.date.available | 2015-01-21T06:50:08Z | |
| dc.date.issued | 11.12.2009 | en_US |
| dc.description.abstract | This thesis presents techniques and algorithms for the effective exploration of volumetric datasets. The Visualization techniques are designed to focus on user specified features of interest. The proposed techniques are grouped into four chapters namely feature peeling, computation and visualization of fabrication artifacts, locally adaptive marching cubes, and comparative visualization for parameter studies of dataset series. The presented methods enable the user to efficiently explore the volumetric dataset for features of interest.Feature peeling is a novel rendering algorithm that analyzes ray profiles along lines of sight. The profiles are subdivided according to encountered peaks and valleys at so called transition points. The sensitivity of these transition points is calibrated via two thresholds. The slope threshold is based on the magnitude of a peak following a valley, while the peeling threshold measures the depth of the transition point relative to the neighboring rays. This technique separates the dataset into a number of feature layers.Fabrication artifacts are of prime importance for quality control engineers for first part inspection of industrial components. Techniques are presented in this thesis to measure fabrication artifacts through direct comparison of a reference CAD model with the corresponding industrial 3D X-ray computed tomography volume. Information from the CAD model is used to locate corresponding points in the volume data. Then various comparison metrics are computed to measure differences (fabrication artifacts) between the CAD model and the volumetric dataset. The comparison metrics are classified as either geometry-driven comparison techniques or visual-driven comparison techniques.The locally adaptive marching cubes algorithm is a modification of the marching cubes algorithm where instead of a global iso-value, each grid point has its own iso-value. This defines an iso-value field, which modifies the case identification process in the algorithm. An iso-value field enables the algorithm to correct biases within the dataset like low frequency noise, contrast drifts, local density variations, and other artifacts introduced by the measurement process. It can also be used for blending between different iso-surfaces (e.g., skin, and bone in a medical dataset).Comparative visualization techniques are proposed to carry out parameter studies for the special application area of dimensional measurement using industrial 3D X-ray computed tomography. A dataset series is generated by scanning a specimen multiple times by varying parameters of the scanning device. A high resolution series is explored using a planar reformatting based visualization system. A multi-image view and an edge explorer are proposed for comparing and visualizing gray values and edges of several datasets simultaneously. For fast data retrieval and convenient usability the datasets are bricked and efficient data structures are used. | en_US |
| dc.format | application/pdf | en_US |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/8222 | |
| dc.language | English | en_US |
| dc.publisher | Malik | en_US |
| dc.title | Feature Centric Volume Visualization | en_US |
| dc.type | Text.PhDThesis | en_US |
Files
Original bundle
1 - 1 of 1