An enhanced memetic differential evolution in filter design for defect detection in paper production
Evolutionary Computation
Traffic sign classification by image preprocessing and neural networks
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Selecting an appropriate segmentation method automatically using ANN classifier
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Image segmentation to HSI model based on improved particle swarm optimization
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
Automatic thresholding has been widely used in the machine vision industry for automated visual inspection of defects. A commonly use thresholding technique, the Otsu method, provides satisfactory results for thresholding an image with histogram of bimodal distribution. This method, however, fails if the histogram is unimodal or close to unimodal. For defect detection applications, defects range from no defect, small defect, to large defect, which means the gray-level distributions range from unimodal to bimodal. In this paper, we revised and improved the Otsu method for selecting optimal threshold values for both unimodal and bimodal distributions. We also tested the performance of the revised method on common defect detection applications.