Classification of Production Ready 2D Animation using Contour and Distance Fields

Information

• Publication Type: Bachelor Thesis
• Workgroup(s)/Project(s):
  • Advanced Visual and Geometric Computing for 3D Capture, Display, and Fabrication
• Date: December 2019
• Date (Start): June 2019
• Date (End): December 2019
• Matrikelnummer: 01633066
• First Supervisor:
  • Michael Wimmer
  • Joao Afonso Cardoso

Abstract

Image classification is one of the most common use cases of Convolutional Neural Networks. In this thesis, our goal is to increase the accuracy of a neural network classifier for frames of production ready 2D animations and to create a model from a dataset with high accuracy for classification. This can be seen as groundwork for future work that applies neural networks on production ready 2D animation data, by reusing and tweaking the model for different applications.

We compare training a neural network with the color channels of images to training with grayscale images, predicted contours or distance fields generated from those contours. Furthermore, different combinations of the data will be used to evaluate the best option. This means that the comparison of the accuracy not only includes color data compared to color with contours and distance fields but every combination of the aforementioned four types of input.

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BibTeX

@bachelorsthesis{wieser-2019-ani,
  title =      "Classification of Production Ready 2D Animation using
               Contour and Distance Fields",
  author =     "Manuel Wieser",
  year =       "2019",
  abstract =   "Image classification is one of the most common use cases of
               Convolutional Neural Networks. In this thesis, our goal is
               to increase the accuracy of a neural network classifier for
               frames of production ready 2D animations and to create a
               model from a dataset with high accuracy for classification.
               This can be seen as groundwork for future work that applies
               neural networks on production ready 2D animation data, by
               reusing and tweaking the model for different applications. 
               We compare training a neural network with the color channels
               of images to training with grayscale images, predicted
               contours or distance fields generated from those contours.
               Furthermore, different combinations of the data will be used
               to evaluate the best option. This means that the comparison
               of the accuracy not only includes color data compared to
               color with contours and distance fields but every
               combination of the aforementioned four types of input.",
  month =      dec,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2019/wieser-2019-ani/",
}