Abstract
Traditionally, the sewer inspection usually discovers sewer failures on numerous CCTV images by human interpretation. However, it remains to be improved in both consideration of economic and efficient due to human's fatigue and subjectivity. To enhance the sewer inspection approaches, this paper attends to employ artificial intelligence into image process to extract the failure features of the sewer systems, which was also applied to the sewer system in the eastern Taichung City, Taiwan. The extracted features are valuable information in pattern recognition of failures on CCTV images. Wavelet transform and gray-level co-occurrence matrix, which have been widely applied in many texture analyses. were adopted in this research. Wavelet transform is capable of dividing an image into four sub-images including approximation sub-image, horizontal detail sub-image, vertical detail sub-image, and diagonal detail sub-image. In this paper, the co-occurrence matrixes of horizontal orientation, vertical orientation, and 45° and 135° orientations, respectively, were calculated for the horizontal, vertical, and diagonal detail sub-images. Subsequently, the features including angular second moment, entropy, contrast, homogeneity, dissimilarity, correlation, and cluster tendency, can be obtained from the co-occurrence matrixes. However, redundant features either could decrease the accuracy of texture description or could increase the difficulty of pattern recognition. Thus, the correlations of the features are estimated to find out the appropriate feature sets in which the coefficients of correlation of the features are less than 0.5. Finally, a discriminant analysis was used to evaluate their discriminabilities to the pipe defect patterns, and entropy, correlation, and cluster tendency were the best feature vector because of its better discriminant accuracy according error matrix analysis.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR |
| Pages | 579-584 |
| Number of pages | 6 |
| Volume | 2 |
| DOIs | |
| Publication status | Published - 2008 |
| Event | 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR - Hong Kong, China Duration: 2008 Aug 30 → 2008 Aug 31 |
Other
| Other | 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR |
|---|---|
| Country | China |
| City | Hong Kong |
| Period | 08-08-30 → 08-08-31 |
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All Science Journal Classification (ASJC) codes
- Computational Theory and Mathematics
- Computer Vision and Pattern Recognition
Cite this
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Feature extraction of sewer pipe failures by wavelet transform and co-occurrence matrix. / Yang, Ming Der; Su, Tung Ching; Pan, Nang-Fei; Liu, Pei.
Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR. Vol. 2 2008. p. 579-584 4635846.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Feature extraction of sewer pipe failures by wavelet transform and co-occurrence matrix
AU - Yang, Ming Der
AU - Su, Tung Ching
AU - Pan, Nang-Fei
AU - Liu, Pei
PY - 2008
Y1 - 2008
N2 - Traditionally, the sewer inspection usually discovers sewer failures on numerous CCTV images by human interpretation. However, it remains to be improved in both consideration of economic and efficient due to human's fatigue and subjectivity. To enhance the sewer inspection approaches, this paper attends to employ artificial intelligence into image process to extract the failure features of the sewer systems, which was also applied to the sewer system in the eastern Taichung City, Taiwan. The extracted features are valuable information in pattern recognition of failures on CCTV images. Wavelet transform and gray-level co-occurrence matrix, which have been widely applied in many texture analyses. were adopted in this research. Wavelet transform is capable of dividing an image into four sub-images including approximation sub-image, horizontal detail sub-image, vertical detail sub-image, and diagonal detail sub-image. In this paper, the co-occurrence matrixes of horizontal orientation, vertical orientation, and 45° and 135° orientations, respectively, were calculated for the horizontal, vertical, and diagonal detail sub-images. Subsequently, the features including angular second moment, entropy, contrast, homogeneity, dissimilarity, correlation, and cluster tendency, can be obtained from the co-occurrence matrixes. However, redundant features either could decrease the accuracy of texture description or could increase the difficulty of pattern recognition. Thus, the correlations of the features are estimated to find out the appropriate feature sets in which the coefficients of correlation of the features are less than 0.5. Finally, a discriminant analysis was used to evaluate their discriminabilities to the pipe defect patterns, and entropy, correlation, and cluster tendency were the best feature vector because of its better discriminant accuracy according error matrix analysis.
AB - Traditionally, the sewer inspection usually discovers sewer failures on numerous CCTV images by human interpretation. However, it remains to be improved in both consideration of economic and efficient due to human's fatigue and subjectivity. To enhance the sewer inspection approaches, this paper attends to employ artificial intelligence into image process to extract the failure features of the sewer systems, which was also applied to the sewer system in the eastern Taichung City, Taiwan. The extracted features are valuable information in pattern recognition of failures on CCTV images. Wavelet transform and gray-level co-occurrence matrix, which have been widely applied in many texture analyses. were adopted in this research. Wavelet transform is capable of dividing an image into four sub-images including approximation sub-image, horizontal detail sub-image, vertical detail sub-image, and diagonal detail sub-image. In this paper, the co-occurrence matrixes of horizontal orientation, vertical orientation, and 45° and 135° orientations, respectively, were calculated for the horizontal, vertical, and diagonal detail sub-images. Subsequently, the features including angular second moment, entropy, contrast, homogeneity, dissimilarity, correlation, and cluster tendency, can be obtained from the co-occurrence matrixes. However, redundant features either could decrease the accuracy of texture description or could increase the difficulty of pattern recognition. Thus, the correlations of the features are estimated to find out the appropriate feature sets in which the coefficients of correlation of the features are less than 0.5. Finally, a discriminant analysis was used to evaluate their discriminabilities to the pipe defect patterns, and entropy, correlation, and cluster tendency were the best feature vector because of its better discriminant accuracy according error matrix analysis.
UR - http://www.scopus.com/inward/record.url?scp=56749179992&partnerID=8YFLogxK
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U2 - 10.1109/ICWAPR.2008.4635846
DO - 10.1109/ICWAPR.2008.4635846
M3 - Conference contribution
SN - 9781424422395
VL - 2
SP - 579
EP - 584
BT - Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR
ER -