{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:10:08Z","timestamp":1776125408232,"version":"3.50.1"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2011,3]]},"abstract":"<jats:p> In general, the sewer inspection usually employs a great number of CCTV images to discover sewer failures by human interpretation. A computer-aided program remains to be developed due to human's fatigue and subjectivity. To enhance the efficiency of sewer inspection, this paper attends to apply artificial intelligence to extract the failure features of the sewer systems that is demonstrated on the sewer system in the eastern Taichung City, Taiwan. Wavelet transform and gray-level co-occurrence matrix, which have been widely applied in many texture analyses, are adopted in this research to generate extracted features, which are the most valuable information in pattern recognition of failures on CCTV images. 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. The co-occurrence matrices of horizontal orientation, vertical orientation, and 45\u00b0 and 135\u00b0 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 matrices. However, redundant features either decrease the accuracy of texture description or increase the difficulty of pattern recognition. Thus, the correlations of the features are estimated to search the appropriate feature sets according to the correlation coefficients between the features. In addition, a discriminant analysis was used to evaluate the discriminability of the features for the pipe failure defection, and entropy, correlation, and cluster tendency were found to be the best features based on the discriminant accuracy through an error matrix analysis. <\/jats:p>","DOI":"10.1142\/s0219691311004055","type":"journal-article","created":{"date-parts":[[2011,3,24]],"date-time":"2011-03-24T11:38:15Z","timestamp":1300966695000},"page":"211-225","source":"Crossref","is-referenced-by-count":28,"title":["FEATURE EXTRACTION OF SEWER PIPE DEFECTS USING WAVELET TRANSFORM AND CO-OCCURRENCE MATRIX"],"prefix":"10.1142","volume":"09","author":[{"given":"MING-DER","family":"YANG","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, National Chung Hsing University, 250 Kuo Kuang Rd., Taichung, 402\/Taiwan, R. O. C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"TUNG-CHING","family":"SU","sequence":"additional","affiliation":[{"name":"Department of Construction Engineering, National Quemoy University, 1 University Rd., Kinmen, 892\/Taiwan, R. O. C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"NANG-FEI","family":"PAN","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, National Cheng Kung University, 1 University Rd., Tainan, 701\/Taiwan, R. O. C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"PEI","family":"LIU","sequence":"additional","affiliation":[{"name":"Department of Transportation and Traffic Engineering and Management, Feng Chia University, 100 Wenhwa Rd., Taichung, 407\/Taiwan, R. O. 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