<abstract xmlns="http://www.w3.org/1999/xhtml"><p>It is still very challenging to establish a unified and robust framework to perform accurate and complete crack extraction from images with cluttered background, various morphological differences and even with shadow influence. In this paper, an improved neuron segmentation model with two stages is proposed for crack segmentation. Firstly, a robust crack indicator function is designed based on local directional filtering; it makes up for the traditional function based on hessian matrix, which is resulting in problem of local structure discontinuities. After obtaining the indicator function, the crack detection is performed in an integrated mode; it is incorporating the automated directional region growing without manual intervention by adopting level sets; then efficient and complete crack segmentation is realized by iterative contour evolution. The performance of the proposed model is demonstrated by experiments on three kinds of grouped crack sample images and the quantitative evaluation. We also argue that the proposed model is applicable for biomedical image segmentation.</p></abstract>

It is still very challenging to establish a unified and robust framework to perform accurate and complete crack extraction from images with cluttered background, various morphological differences and even with shadow influence. In this paper, an improved neuron segmentation model with two stages is proposed for crack segmentation. Firstly, a robust crack indicator function is designed based on local directional filtering; it makes up for the traditional function based on hessian matrix, which is resulting in problem of local structure discontinuities. After obtaining the indicator function, the crack detection is performed in an integrated mode; it is incorporating the automated directional region growing without manual intervention by adopting level sets; then efficient and complete crack segmentation is realized by iterative contour evolution. The performance of the proposed model is demonstrated by experiments on three kinds of grouped crack sample images and the quantitative evaluation. We also argue that the proposed model is applicable for biomedical image segmentation.

An Improved Neuron Segmentation Model for Crack Detection – Image Segmentation Model

State Key Laboratory for Geomechanics and Deep Underground Engineering

China University of Mining and Technology

School of Information and Control Engineering

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

It is still very challenging to establish a unified and robust framework to perform accurate and complete crack extraction from images with cluttered...

An Improved Neuron Segmentation Model for Crack Detection – Image Segmentation Model

Publié en ligne: 26 juin 2017

Pages: 119 - 133

DOI: https://doi.org/10.1515/cait-2017-0021

Mots clés
Crack detection, crack indicator function, local directional filtering, directional region growing, level set

© 2017 Ming Hao et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

An Improved Neuron Segmentation Model for Crack Detection – Image Segmentation Model

Publié en ligne: 26 juin 2017

Pages: 119 - 133

DOI: https://doi.org/10.1515/cait-2017-0021

Mots clésCrack detection, crack indicator function, local directional filtering, directional region growing, level set

© 2017 Ming Hao et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Crack detection, crack indicator function, local directional filtering, directional region growing, level set