Implementation of mathematical morphological operations for spatial data processing
Computers & Geosciences
Pattern Recognition Letters
Morphological scale space for 2D shape smoothing
Computer Vision and Image Understanding
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
Expert Systems with Applications: An International Journal
Automated diagnosis of sewer pipe defects based on machine learning approaches
Expert Systems with Applications: An International Journal
Systematic image quality assessment for sewer inspection
Expert Systems with Applications: An International Journal
Morphological segmentation based on edge detection for sewer pipe defects on CCTV images
Expert Systems with Applications: An International Journal
Achievements and challenges in recognizing and reconstructing civil infrastructure
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
| Hi-index | 12.05 |
Several literatures presented automated systems for detecting or classifying sewer pipe defects based on morphological features of pipe defects. In those automated systems, however, the morphologies of the darker center or some uncertain objects on CCTV images are also segmented and become noises while morphology-based pipe defect segmentation is implemented. In this paper, the morphology-based pipe defect segmentation is proposed and discussed to be an improved approach for automated diagnosis of pipe defects on CCTV images. The segmentation of pipe defect morphologies is first to implement an opening operation for gray-level CCTV images to distinguish pipe defects. Then, Otsu's technique is used to segment pipe defects by determining the optimal thresholds for gray-level CCTV images of opening operation. Based on the segmentation results of CCTV images, the ideal morphologies of four typical pipe defects are defined. If the segmented CCTV images match the definition of those ideal morphologies, the pipe defects on those CCTV images can be successfully identified by a radial basis network (RBN) based diagnostic system. As for the rest CCTV images failing to match the ideal morphologies, the failure causes was discussed so to suggest a regulation for imaging conditions, such as camera pose and light source, in order to obtain CCTV images for successful segmentation.