A New Morphological Measure of Histogram Bimodality

  • Miguel Angel Cataño
  • Joan Climent
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

The presence of multiple modes in a histogram gives important information about data distribution for a great amount of different applications. The dip test has been the most common statistical measure used for this purpose.

Histograms of oriented gradients (HOGs) with a high bimodality have shown to be very useful to detect highly robust keypoints. However, the dip test presents serious disadvantages when dealing with such histograms. In this paper we describe the drawbacks of the dip test for determining HOGs bimodality, and present a new bimodality test, based on mathematical morphology, that overcomes them.

Keywords

Keypoint detection Bimodality test Histograms of Oriented Gradients Mathematical Morphology Dynamics 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Miguel Angel Cataño
    • 1
    • 2
  • Joan Climent
    • 1
    • 2
  1. 1.Pontificia Universidad Católica del PerúPerú
  2. 2.Barcelona Tech (UPC)Spain

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