Automated diagnosis of sewer pipe defects based on machine learning approaches

Expert Systems with Applications

Volumn 35, Issue 3, 2008, Pages 1327-1337

  Yang, Ming Der ^a   Su, Tung Ching ^a  

^a NATIONAL CHUNG HSING UNIVERSITY   (Taiwan)

Author keywords

CCTV images; Diagnostic system; Sewer pipe defects; Textural features

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLOSED CIRCUIT TELEVISION SYSTEMS; COBALT; COBALT COMPOUNDS; COMPUTER NETWORKS; DEFECTS; HEALTH; IMAGE CLASSIFICATION; IMAGE ENHANCEMENT; IMAGE PROCESSING; IMAGING SYSTEMS; IMAGING TECHNIQUES; INSPECTION; LEARNING SYSTEMS; MACHINE TOOLS; NETWORK PROTOCOLS; NEURAL NETWORKS; OPTICAL DATA PROCESSING; PIPE; ROBOT LEARNING; SEWAGE; SEWERS; SUPPORT VECTOR MACHINES; TELEVISION SYSTEMS; WAVELET TRANSFORMS;

APPLIED (CO); AUTOMATED DIAGNOSIS; BACK PROPAGATION NEURAL NETWORK (BPANN); BAYESIAN CLASSIFIERS; CO OCCURRENCE MATRICES; DIAGNOSTIC SYSTEMS; ELSEVIER (CO); GENERAL (CO); IMAGE PROCESSES; INSPECTION IMAGES; INSPECTION TOOLS; MACHINE-LEARNING; PIPE DEFECTS; PROFESSIONAL EXPERIENCES; RADIAL BASIS; SEWAGE REHABILITATION; SEWER PIPES; SEWER SYSTEMS; STRUCTURAL CONDITIONS; SUPPORT VECTOR MACHINE (SVM);

METROPOLITAN AREA NETWORKS;

EID: 44949223479     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.08.013     Document Type: Article

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