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

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 validated on a set of 13 classes of aggregates of size ranging from 0.125 to 16 mm and made a comparison with standard, fixed-shape granulometry. In the experiments our model consistently outperformed the standard approach and predicted the correct size class with overall accuracy over 92 %. Tests on three classes from real samples also confirmed the potential of the method for application in real scenarios.

[1]  Ron S. Kenett,et al.  Statistics for Business and Economics , 1973 .

[2]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[3]  Graham D. Finlayson,et al.  The 1.5D sieve algorithm , 2008, Pattern Recognit. Lett..

[4]  Francesco Bianconi,et al.  Rotation invariant co-occurrence features based on digital circles and discrete Fourier transform , 2014, Pattern Recognit. Lett..

[5]  Fionn Murtagh,et al.  A machine vision approach to the grading of crushed aggregate , 2005, Machine Vision and Applications.

[6]  Abdelghani Naceri,et al.  Use of waste brick as a partial replacement of cement in mortar. , 2009, Waste management.

[7]  Pedro Pina,et al.  In-situ computation of granulometries of sedimentary grains - some preliminary results , 2011 .

[8]  Richard J. Harvey,et al.  Using occlusion models to evaluate scale-space processors , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[9]  G. Matheron.  Random Sets and Integral Geometry , 1976 .

[10]  D. Rubin.  A Simple Autocorrelation Algorithm for Determining Grain Size from Digital Images of Sediment , 2004 .

[11]  V. Weitbrecht,et al.  User guide to gravelometric image analysis by BASEGRAIN , 2013 .

[12]  Akh Kwan,et al.  Particle size distribution analysis of coarse aggregate using digital image processing , 1998 .

[13]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[14]  Anne Marsden,et al.  International Organization for Standardization , 2014 .

[15]  Geert M. P. van Kempen,et al.  Improving the accuracy of isotropic granulometries , 2007, Pattern Recognit. Lett..

[16]  Scott T. Acton,et al.  Scale space classification using area morphology , 2000, IEEE Trans. Image Process..

[17]  Stephen Marshall,et al.  Morphological granulometry for classification of evolving and ordered texture images , 2011, 2011 19th European Signal Processing Conference.

[18]  Pierre Soille,et al.  Periodic lines: Definition, cascades, and application to granulometries , 1996, Pattern Recognit. Lett..

[19]  Kristel Michielsen,et al.  Morphological image analysis , 2000 .

[20]  Caterina Micale,et al.  Urban mining: quality and quantity of recyclable and recoverable material mechanically and physically extractable from residual waste. , 2013, Waste management.

[21]  Mohammad Sadeghi,et al.  Size Distribution Estimation of Stone Fragments via Digital Image Processing , 2010, ISVC.

[22]  Edward R. Dougherty,et al.  Application of image-based granulometry to siliceous and calcareous estuarine and marine sediments , 2003 .

[23]  Jacek Urbański,et al.  Automated granulometric analysis and grain-shape estimation of beach sediments using object-based image analysis , 2011 .

[24]  Yann Gousseau,et al.  The dead leaves model: a general tessellation modeling occlusion , 2006, Advances in Applied Probability.

[25]  Paul Southam,et al.  Texture Granularities , 2005, ICIAP.

[26]  E. Dougherty,et al.  Gray-scale morphological granulometric texture classification , 1994 .

[27]  Paul Southam,et al.  Texture classification via morphological scale-space: Tex-Mex features , 2009, J. Electronic Imaging.

[28]  Stephen Marshall,et al.  Classification of Ordered Texture Images Using Regression Modelling and Granulometric Features , 2011, 2011 Irish Machine Vision and Image Processing Conference.

[29]  Gerhard Masselink,et al.  Grain‐size information from the statistical properties of digital images of sediment , 2009 .

[30]  Rafal E Dunin-Borkowski,et al.  A simple algorithm for measuring particle size distributions on an uneven background from TEM images. , 2011, Ultramicroscopy.

[31]  Paul F. Whelan,et al.  Evaluation of robustness against rotation of LBP, CCR and ILBP features in granite texture classification , 2011, Machine Vision and Applications.

[32]  Adam Gauci,et al.  Comparative median grain size assessment through three different techniques for sandy beach deposits on the Maltese Islands (Central Mediterranean) , 2013 .

[33]  Daniel Buscombe,et al.  Estimation of grain-size distributions and associated parameters from digital images of sediment , 2008 .

[34]  Sudhir Misra,et al.  Use of aggregates from recycled construction and demolition waste in concrete , 2007 .

[35]  Fabio Roli,et al.  Image Analysis and Processing - ICIAP 2005, 13th International Conference, Cagliari, Italy, September 6-8, 2005, Proceedings , 2005, ICIAP.