Computers and Concrete

Volume 14, Number 3, 2014, pages 315-325

DOI: 10.12989/cac.2014...315

Automated segmentation of concrete images into microstructures: A comparative study

Mehran Yazdi and Katayoon Sarafrazi

Abstract

Concrete is an important material in most of civil constructions. Many properties of concrete can be determined through analysis of concrete images. Image segmentation is the first step for the most of these analyses. An automated system for segmentation of concrete images into microstructures using texture analysis is proposed. The performance of five different classifiers has been evaluated and the results show that using an Artificial Neural Network classifier is the best choice for an automatic image segmentation of concrete.

Key Words

microstructural analysis; image segmentation; FLD; KNN; artificial neural networks; SVM; bayesian classification; co-occurrence matrix; texture analysis

Address

Mehran Yazdi: Department of Electronics and Computer Engineering,Shiraz University, Shiraz, Iran Katayoon Sarafrazi: Department of Electronics and Computer Engineering, Shiraz University, Shiraz, Iran