Abstract
This paper investigates two methods for the detection of defects on textured surfaces using neural networks and support vector machines. Every pixel from the inspection image is characterized by a feature vector, which serves as a local measure of homogeneity of texture. The feature vectors from the gray-level arrangement of neighboring pixels are transformed to eigenspace using Principal Component Analysis (PCA). The transformed features from a predetermined set of training images are used to train the classifier. The trained classifier is used to classify every pixel from inspection image into two-class, i.e. with- or without-defect. The experimental results on real fabric defects show that the proposed scheme can successfully segment the defects from the inspection images.
Original language | English |
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Title of host publication | IEEE International Conference on Image Processing |
Publication status | Published - 1 Jan 2002 |
Externally published | Yes |
Event | International Conference on Image Processing (ICIP'02) - Rochester, NY, United States Duration: 22 Sept 2002 → 25 Sept 2002 |
Conference
Conference | International Conference on Image Processing (ICIP'02) |
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Country/Territory | United States |
City | Rochester, NY |
Period | 22/09/02 → 25/09/02 |
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering