Abstract
We present a new approach to detecting defects in random textures which requires
only very few defect free samples for unsupervised training.
Each product image is divided into overlapping patches of various sizes.
Then, density mixture models are applied to reduce groupings of patches to a number of
textural exemplars, referred to here as texems, characterising the means and
covariances of whole sets of image patches.
The texems can be viewed as implicit representations of textural primitives.
A multiscale approach is used to save computational costs.
Finally, we perform novelty detection by applying the
lower bound of normal samples likelihoods on the multiscale defect map of an
image to localise defects.
| Translated title of the contribution | Texture Exemplars for Defect Detection on Random Textures |
|---|---|
| Original language | English |
| Title of host publication | Unknown |
| Publisher | Springer Berlin Heidelberg |
| Pages | 404 - 413 |
| Number of pages | 9 |
| Publication status | Published - Aug 2005 |