Handling Large Multidimensional Data in Visual Computing

Speaker:  Renato Pajarola – Zurich, Switzerland
Topic(s):  Graphics and Computer-Aided Design

Abstract

Tensor decomposition methods and multilinear algebra are powerful emerging tools to cope with current trends in computer graphics, image processing and data visualization, in particular with respect to compact representation and processing of increasingly large-scale, high-dimensional and high-parametric data sets and models. Flexible and scalable mathematical models that can process, manipulate as well as compress, store and retrieve such data efficiently are therefore of increasing importance, especially for higher-dimensional data. Initially proposed as an extension of the concept of matrix rank for 3 and more dimensions, tensor decomposition methods have found applications in a remarkably wide range of disciplines. However, partly due to the notable initial learning costs, this mathematical framework has not reached yet all its potential and awareness in the visual computing research community. In this talk I will introduce the most successful tensor decomposition models and review their application in graphics and visualization, as well as give insights into the benefits they offer and showcase specific applications such as visual data compression, signal processing, interactive data manipulation, texture synthesis, and data-driven rendering.

About this Lecture

Number of Slides:  45
Duration:  45 minutes
Languages Available:  English
Last Updated: 

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