Alim, UsmanNasim Saravi, Amin2024-04-242024-04-242024-04-22Nasim Saravi, A. (2024). Transferring transfer function (TTF): a guided approach to transfer function optimization in volume visualization (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.https://hdl.handle.net/1880/118489In volume visualization, a transfer function tailored for one volume usually does not work for other similar volumes without careful tuning. This process can be tedious and time-consuming for a large set of volumes. Leveraging the differentiability of volume rendering, this work presents a novel approach to transfer function optimization based on a reference volume and its transfer function. Using two fully connected neural networks, our approach learns a continuous 2D separable scalar-gradient transfer function that visualizes the features of interest with consistent visual properties across volumes. The resulting optimized transfer function is exportable for further interactions in domain-specific applications. Through two compelling use cases—tracking the aftermath of an asteroid blast near the ocean surface and visualizing brain white matter, grey matter, and cerebral fluid in magnetic resonance (MR) images—we demonstrate the effectiveness of our approach, validated through collaboration with domain experts.enUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Scientific VisualizationVolume RenderingDeep Learning for VisualizationComputer ScienceArtificial Intelligencemaster thesis