Revealing the Invisible: On the Extraction of Latent Information from Generalized Image Data

Dissertation, University of Bonn, Jan. 2020
 

Abstract

The desire to reveal the invisible in order to explain the world around us has been a source of impetus for technological and scientific progress throughout human history. Many of the phenomena that directly affect us cannot be sufficiently explained based on the observations using our primary senses alone. Often this is because their originating cause is either too small, too far away, or in other ways obstructed. To put it in other words: it is invisible to us. Without careful observation and experimentation, our models of the world remain inaccurate and research has to be conducted in order to improve our understanding of even the most basic effects. In this thesis, we1 are going to present our solutions to three challenging problems in visual computing, where a surprising amount of information is hidden in generalized image data and cannot easily be extracted by human observation or existing methods. We are able to extract the latent information using non-linear and discrete optimization methods based on physically motivated models and computer graphics methodology, such as ray tracing, real-time transient rendering, and image-based rendering.

Download: https://nbn-resolving.org/urn:nbn:de:hbz:5-57073

Bibtex

@PHDTHESIS{iseringhausen-2020-dissertation,
    author = {Iseringhausen, Julian},
     title = {Revealing the Invisible: On the Extraction of Latent Information from Generalized Image Data},
      type = {Dissertation},
      year = {2020},
     month = jan,
    school = {University of Bonn},
  abstract = {The desire to reveal the invisible in order to explain the world around us has been a source of
              impetus for technological and scientific progress throughout human history. Many of the phenomena
              that directly affect us cannot be sufficiently explained based on the observations using our primary
              senses alone. Often this is because their originating cause is either too small, too far away, or in
              other ways obstructed. To put it in other words: it is invisible to us. Without careful observation
              and experimentation, our models of the world remain inaccurate and research has to be conducted in
              order to improve our understanding of even the most basic effects. In this thesis, we1 are going to
              present our solutions to three challenging problems in visual computing, where a surprising amount
              of information is hidden in generalized image data and cannot easily be extracted by human
              observation or existing methods. We are able to extract the latent information using non-linear and
              discrete optimization methods based on physically motivated models and computer graphics
              methodology, such as ray tracing, real-time transient rendering, and image-based rendering.}
}