Image analysis and mathematical morphology for civil engineering materials

By M. Coster, J. Chermant. 2001

133 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b15461394243a5ca061fd671a07b84eff27b81a8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

1. Milani S. Sumanasooriya
2. K. Ogiwara
3. G. H. A. J. J. Kumara
4. K. Hayano
5. N. Neithalath

In Computer Vision and Pattern Recognition

1. Milani S. Sumanasooriya
2. K. Ogiwara
3. G. H. A. J. J. Kumara
4. K. Hayano
5. N. Neithalath

Citations in the corpus

• Pore structure features of pervious concretes proportioned for desired porosities and their performance prediction (2011, methodology): “The granulometric density function is a well known morphological method that can be used to characterize the feature size distribution in two-dimensional images [22,23].”
• Image analysis techniques on evaluation of particle size distribution of gravel (2012, methodology): “In recent times, image processing techniques have also been practiced in civil engineering field [1], especially in concrete engineering [2] - [4].”