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Discontinuity-Adaptive Shape from Focus Using a Non-convex Prior

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Pattern Recognition (DAGM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5748))

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Abstract

Shape from focus (SFF) is a widely used technique for determining the 3D structure of textured microscopic objects. However, SFF output depends critically on the number of observations used and the focus measure operator adopted. In this paper, we propose a new SFF method that can provide rich structure information given limited number of observations. We observe that depth is non-linearly related to the observations and pose the shape estimation as a minimization problem within a Maximum A Posteriori (MAP) - Markov Random Field (MRF) framework. We incorporate a discontinuity-adaptive MRF prior for the underlying structure. The resulting cost function is non-convex in nature which we minimize using Graduated non-convexity algorithm. When tested on synthetic as well as real objects, the results obtained are quite impressive.

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Author information

Authors and Affiliations

  1. Image Processing and Computer Vision Laboratory Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India

    Krishnamurthy Ramnath & Ambasamudram N. Rajagopalan

Authors
  1. Krishnamurthy Ramnath
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  2. Ambasamudram N. Rajagopalan
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Editor information

Editors and Affiliations

  1. Digitale Bildverarbeitung, Universität Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany

    Joachim Denzler  & Herbert Süße  & 

  2. Fraunhofer-Institut für Angewandte Optik und Feinmechanik, Albert-Einstein-Str. 7, 07745, Jena, Germany

    Gunther Notni

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© 2009 Springer-Verlag Berlin Heidelberg

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Ramnath, K., Rajagopalan, A.N. (2009). Discontinuity-Adaptive Shape from Focus Using a Non-convex Prior. In: Denzler, J., Notni, G., Süße, H. (eds) Pattern Recognition. DAGM 2009. Lecture Notes in Computer Science, vol 5748. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03798-6_19

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  • DOI: https://doi.org/10.1007/978-3-642-03798-6_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03797-9

  • Online ISBN: 978-3-642-03798-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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