Machine Learning
Pairwise classification and support vector machines
Advances in kernel methods
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
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
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Hi-index | 12.05 |
Random surface defects occur during the hot bar rolling of steels and are identified either by manual or by automated inspection techniques. Manual inspection techniques are purely based on the process knowledge of the inspector such as the location, type and kind of defects, and the primary sources of these defects. The automated techniques, to identify and classify the defects, rely on machine vision technologies and image processing algorithms based on support vector machines, wavelets, image processing and statistical inference. Both these approaches have their own advantages and limitations. To improve the accuracy of classification of these defects a process knowledge based support vector classification scheme is proposed (called PK-MSVM) which combines feature extraction task of automated inspection with the process knowledge. The defect observation data from the imaging sensor is transformed to include this process knowledge. Three attributes of the defects - length to width ratio, longitudinal location and transverse location- are used for this transformation are they are closely related to the thermo-mechanics of the rolling process. Different formulations of the multi-class support vector machines (MSVMs) are compared for this classification with or without process knowledge based transformation: one-against-one, one-against-all and Hastie's algorithm of multi class SVM. It is found that the new approach (PK-MSVM) performs better than traditional MSVM for all the three formulations. For the best case, the performance sees a jump of more than 100%. Thus incorporating process knowledge in identification and classification does increase the reliability of inspection considerably.