The nature of statistical learning theory
The nature of statistical learning theory
Pattern Recognition Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Wavelet transform-based locally orderless images for texture segmentation
Pattern Recognition Letters
Texture segmentation using wavelet transform
Pattern Recognition Letters
A neural network model with bounded-weights for pattern classification
Computers and Operations Research
Intrusion detection using hierarchical neural networks
Pattern Recognition Letters
An application of one-class support vector machines in content-based image retrieval
Expert Systems with Applications: An International Journal
Segmenting ideal morphologies of sewer pipe defects on CCTV images for automated diagnosis
Expert Systems with Applications: An International Journal
Systematic image quality assessment for sewer inspection
Expert Systems with Applications: An International Journal
Using SVM based method for equipment fault detection in a thermal power plant
Computers in Industry
Morphological segmentation based on edge detection for sewer pipe defects on CCTV images
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
In sewage rehabilitation planning, closed circuit television (CCTV) systems are the widely used inspection tools in assessing sewage structural conditions for non man entry pipes. Currently, the assessment of sewage structural conditions by manually interpretation on CCTV images seems inefficient, especially for several thousands of frames in one inspection plan. Also, the assessment work significantly involves engineers' eye sight and professional experience. With a purpose of assisting general staffs in diagnosing pipe defects on CCTV inspection images, a diagnostic system by applying machine learning approaches is proposed in this paper. This research was first to use image process techniques, including wavelet transform and computation of co-occurrence matrices, for describing the textures of the pipe defects. Then, three neural network approaches, back-propagation neural network (BPN), radial basis network (RBN), and support vector machine (SVM), were adopted to classify pipe defect patterns, and their performances were compared and discussed. The diagnostic system of pipe defects was applied to a sewer system in the 9th district, Taichung City which is the largest city in middle Taiwan. The result shows that the diagnosis accuracy of 60% derived by SVM is the best and also better than the diagnosis accuracy of 57.4% derived by a Bayesian classifier.