Crack Detection Using Fast Spectral Clustering Considering Graph Connectivity
- Resource Type
- Authors
- Osamu Takahashi; Kousuke Matsushima; Daiki Shiotsuka
- Source
- 2018 IEEE 4th International Conference on Computer and Communications (ICCC).
- Subject
- Computer science
Process (computing)
02 engineering and technology
01 natural sciences
Grayscale
Spectral clustering
Matrix decomposition
010309 optics
0103 physical sciences
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Cluster analysis
Algorithm
Connectivity
Sparse matrix
- Language
Cracks on pavement roads cause various traffic problems. Therefore we should repair them properly. Nowadays a variety of crack detection methods in computer vision have been proposed. Spectral clustering is one of them and an effective method, but suffers from processing time due to the large amount of calculation. Among them, calculating of Laplacian-matrix and eigenvalues especially affect processing time. Therefore we propose two methods to improve the efficiency of algorithm. One applies sparse process considering graph connectivity for Laplacian-matrix, and the other considers amount of pixel of crack of pavement road images.