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Craniofacial Image Analysis

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Book cover Health Monitoring and Personalized Feedback using Multimedia Data

Abstract

Craniofacial researchers have used anthropometric measurements taken directly on the human face for research and medical practice for decades. With the advancements in 3D imaging technologies, computational methods have been developed for the diagnoses of craniofacial syndromes and the analysis of the human face. Using advanced computer vision and image analysis techniques, diagnosis and quantification of craniofacial syndromes can be improved and automated. This paper describes a craniofacial image analysis pipeline and introduces the computational methods developed by the Multimedia Group at the University of Washington including data acquisition and preprocessing, low- and mid-level features, quantification, classification, and content-based retrieval.

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Author information

Authors and Affiliations

  1. Computer Science and Engineering, University of Washington, Seattle, WA, USA

    Ezgi Mercan, Shu Liang & Linda G. Shapiro

  2. Singapore Institute of Technology, Singapore, Singapore

    Indriyati Atmosukarto

  3. Electrical Engineering, University of Washington, Seattle, WA, USA

    Jia Wu

Authors
  1. Ezgi Mercan
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  2. Indriyati Atmosukarto
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  3. Jia Wu
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  4. Shu Liang
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  5. Linda G. Shapiro
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Corresponding author

Correspondence to Ezgi Mercan .

Editor information

Editors and Affiliations

  1. Centre for Research and Technology, Hellas, Information Technologies Institute, Thermi, Thessaloniki, Greece

    Alexia Briassouli

  2. Laboratoire Bordelais de Recherche en Informatique/University Bordeaux, Talence, France

    Jenny Benois-Pineau

  3. Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Alexander Hauptmann

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© 2015 Springer International Publishing Switzerland

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Mercan, E., Atmosukarto, I., Wu, J., Liang, S., Shapiro, L.G. (2015). Craniofacial Image Analysis. In: Briassouli, A., Benois-Pineau, J., Hauptmann, A. (eds) Health Monitoring and Personalized Feedback using Multimedia Data. Springer, Cham. https://doi.org/10.1007/978-3-319-17963-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-17963-6_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17962-9

  • Online ISBN: 978-3-319-17963-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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