NDLI: Feature extraction of sewer pipe failures by wavelet transform and co-occurrence matrix

Content Provider	IEEE Xplore Digital Library
Author	Ming-Der Yang Tung-Ching Su Nang-Fei Pan Pei Liu
Copyright Year	2008
Description	Author affiliation: Dept. of Civil Eng., Nat. Chung Hsing Univ., Taichung (Ming-Der Yang; Tung-Ching Su)
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 humanpsilas 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 45deg and 135deg 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.
Starting Page	579
Ending Page	584
File Size	343142
Page Count	6
File Format	PDF
ISBN	9781424422388
DOI	10.1109/ICWAPR.2008.4635846
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Accuracy Correlation Wavelet transform Transforms Inspection CCTV images Feature extraction Joints Gray-level co-occurrence matrix
Content Type	Text
Resource Type	Article

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