Detection crack in image using Otsu method and multiple filtering in image processing techniques

By A. Talab, Zhangcan Huang, Fang Xi, Hai-ming Liu. 2016

334 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Geology, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/90398f81712bf9ff4e1b274cc9faf3c03b13d5e2

How researchers see this paper

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

In Civil and Structural Engineering

1. Armin Dadras Eslamlou
2. Mohsen Azimi
3. Gokhan Pekcan
4. R. Thomas
5. Marc Maguire
6. Sattar Dorafshan
7. Necati Catbas
8. Chuan-Zhi Dong

In Geology

1. Armin Dadras Eslamlou
2. Mohsen Azimi
3. Gokhan Pekcan

In Industrial and Manufacturing Engineering

1. Armin Dadras Eslamlou
2. Mohsen Azimi
3. R. Thomas
4. Marc Maguire
5. Sattar Dorafshan
6. Gokhan Pekcan

Citations in the corpus

• Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete (2018, methodology): “Image enhancement 46 methods includes the application of a variety of image processing techniques on visual images to detect 47 cracks including but not limited to morphological operations [19], digital image correlation [20, 21], image 48 binarization [22, 23], percolation model [24], wavelet trans”
• Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review (2020, methodology): “[81] showed that the fast Haar transform has higher accuracy (86%) as compared to the other filters, such as Canny and Sobel, with 76% and 68% of accuracies, respectively.”