Automated X-Ray Inspection of Aluminum Castings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated visual inspection of rolled metal surfaces
Machine Vision and Applications
Pairwise classification and support vector machines
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
An Intelligent Real-time Vision System for Surface Defect Detection
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Copper Strip Surface Defects Inspection Based on SVM-RBF
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 02
The Copper Surface Defects Inspection System Based on Computer Vision
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 03
Computers and Industrial Engineering
A Bayesian discriminating features method for face detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Automatic classification of granite tiles through colour and texture features
Expert Systems with Applications: An International Journal
Wavelet-based defect detection in solar wafer images with inhomogeneous texture
Pattern Recognition
Hi-index | 12.05 |
This paper describes the designing and testing process of a vision system for strongly reflected metal's surface defects detection. In the authors' view, an automatic inspection system has the following stages: image acquisition, image pre-processing, feature extraction and classification. Thus, the system including four subsystems is designed, and image processing method and pattern recognition algorithm that perform specific functions are outlined. First, the study uses wavelet smoothing method to eliminate noise from the images. Then, the images are segmented by Otsu threshold. At last, five characteristics based on spectral measure of the binary images are collected and input into a support vector machine (SVM). Furthermore, kernel function selection and parameters settings which are used for SVM method are evaluated and discussed. Also, a very difficult detection case for the surface defects of strongly reflected metal is depicted in details. The classification results demonstrate that the proposed method can identify seven classes of metal surface defects effectively, and the results are summarized and interpreted.