Abstract
In this paper we present a framework for the classification of images in surface inspection
tasks and address several key aspects of the processing chain from the original image to the final classification
result. A major contribution of this paper is a quantitative assessment of how incorporating
adaptivity into the feature calculation, the feature pre-processing, and into the classifiers themselves,
influences the final image classification performance. Hereby, results achieved on a range of
artificial and real-world test data from applications in printing, die-casting, metal processing and
food production are presented.
| Original language | English |
|---|---|
| Pages (from-to) | 613-626 |
| Number of pages | 14 |
| Journal | Machine Vision and Applications |
| Volume | 21 |
| Issue number | 5 |
| Publication status | Published - Jul 2010 |
Fields of science
- 101 Mathematics
- 101004 Biomathematics
- 101027 Dynamical systems
- 101013 Mathematical logic
- 101028 Mathematical modelling
- 101014 Numerical mathematics
- 101020 Technical mathematics
- 101024 Probability theory
- 102001 Artificial intelligence
- 102003 Image processing
- 102009 Computer simulation
- 102019 Machine learning
- 102023 Supercomputing
- 202027 Mechatronics
- 206001 Biomedical engineering
- 206003 Medical physics
- 102035 Data science