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Curvature Regularization for Resolution-Independent Images

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Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8081))

Abstract

A resolution-independent image models the true intensity function underlying a standard image of discrete pixels. Previous work on resolution-independent images demonstrated their efficacy, primarily by employing regularizers that penalize discontinuity. This paper extends the approach by permitting the curvature of resolution-independent images to be regularized. The main theoretical contribution is a generalization of the well-known elastica energy for regularizing curvature. Experiments demonstrate that (i) incorporating curvature improves the quality of resolution-independent images, and (ii) the resulting images compare favorably with another state-of-the-art curvature regularization technique.

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MacCormick, J., Fitzgibbon, A. (2013). Curvature Regularization for Resolution-Independent Images. In: Heyden, A., Kahl, F., Olsson, C., Oskarsson, M., Tai, XC. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2013. Lecture Notes in Computer Science, vol 8081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40395-8_13

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  • DOI: https://doi.org/10.1007/978-3-642-40395-8_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40394-1

  • Online ISBN: 978-3-642-40395-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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