Grain-size assessment of fine and coarse aggregates through bipolar area morphology

Francesco Bianconi, Francesco di Maria, Caterina Micale, Antonio Fernández, Richard Harvey

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

This paper presents a new methodology for computing grey-scale granulometries and estimating the mean size of fine and coarse aggregates. The proposed approach employs area morphology and combines the information derived from both openings and closings to determine the size distribution. The method, which we refer to as Bipolar Area Morphology (BAM), is general and can operate on particles of different size and shape. The effectiveness of the procedure was validate on a set of 13 classes of aggregates of size ranging from 0.125mm to 16mm and made a comparison with standard, fixed- shape granulometry. In the experiments our model con- sistently outperformed the standard approach and pre- dicted the correct size class with overall accuracy over 92%. Tests on three classes from real samples also con- firmed the potential of the method for application in real scenarios.
Original languageEnglish
Pages (from-to)775-789
JournalMachine Vision and Applications
Volume26
Issue number6
Early online date17 Jun 2015
DOIs
Publication statusPublished - Aug 2015

Keywords

  • Image analysis
  • Granulometry
  • Area morphology
  • Aggregates

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