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Comparing 3D Descriptors for Local Search of Craniofacial Landmarks

  • Federico M. Sukno
  • John L. Waddington
  • Paul F. Whelan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7432)

Abstract

This paper presents a comparison of local descriptors for a set of 26 craniofacial landmarks annotated on 144 scans acquired in the context of clinical research. We focus on the accuracy of the different descriptors on a per-landmark basis when constrained to a local search. For most descriptors, we find that the curves of expected error against the search radius have a plateau that can be used to characterize their performance, both in terms of accuracy and maximum usable range for the local search. Six histograms-based descriptors were evaluated: three describing distances and three describing orientations. No descriptor dominated over the rest and the best accuracy per landmark was strongly distributed among 3 of the 6 algorithms evaluated. Ordering the descriptors by average error (over all landmarks) did not coincide with the ordering by most frequently selected, indicating that a comparison of descriptors based on their global behavior might be misleading when targeting facial landmarks.

Keywords

Local Search Local Accuracy Spin Image Search Radius Local Reference Frame 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Federico M. Sukno
    • 1
    • 2
  • John L. Waddington
    • 2
  • Paul F. Whelan
    • 1
  1. 1.Centre for Image Processing & AnalysisDublin City UniversityDublinIreland
  2. 2.Molecular & Cellular TherapeuticsRoyal College of Surgeons in IrelandDublinIreland

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