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.
- Image analysis
- Area morphology