Abstract
This paper investigates a new approach for the detection of surface defects, in textured materials, using wavelet packets. Every inspection image is decomposed with a family of real orthonormal wavelet bases. The wavelet packet coefficients from a set of dominant frequency channels containing significant information are used for the characterization of textured images. A fixed number of shift invariant measures from the wavelet packet coefficients are computed. The magnitude and position of these shift invariant measures in a quadtree representation forms the feature set for a two-layer neural network classifier. The neural net classifier classifies these feature vectors into either of defect or defect-free classes. The experimental results suggest that this proposed scheme can successfully identify the defects, and can be used for automated visual inspection.
| Original language | English |
|---|---|
| Pages (from-to) | 247-251 |
| Number of pages | 5 |
| Journal | Conference Record - IAS Annual Meeting (IEEE Industry Applications Society) |
| Volume | 1 |
| Publication status | Published - 1 Jan 2001 |
| Externally published | Yes |
| Event | 36th IAS Annual Meeting -Conference Record of the 2001 Industry Applications - Chicago, IL, United States Duration: 30 Sept 2001 → 4 Oct 2001 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering