Name
Title | ||
|---|---|---|
Assessment of the influence of adaptive components in trainable surface inspection systems |
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s00138-009-0211-1 | Mach. Vis. Appl. |
Keywords | Field | DocType |
adaptive component,feature calculation,final classification result,trainable surface inspection system,feature pre-processing,major contribution,food production,processing chain,final image classification performance,original image,metal processing,key aspect,image classification | Computer vision,Feature vector,Pattern recognition,Feature selection,Computer science,Image segmentation,Artificial intelligence,Test data,Quantitative assessment,Contextual image classification,Machine learning | Journal |
Volume | Issue | ISSN |
21 | 5 | 1432-1769 |
Citations | PageRank | References |
24 | 1.13 | 39 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Eitzinger | 1 | 164 | 15.33 |
| Wolfgang Heidl | 2 | 103 | 7.01 |
| Edwin Lughofer | 3 | 1940 | 99.72 |
| S. Raiser | 4 | 24 | 1.13 |
| J. E. Smith | 5 | 55 | 3.26 |
| M. A. Tahir | 6 | 80 | 5.20 |
| D. Sannen | 7 | 24 | 1.13 |
| H. Van Brussel | 8 | 494 | 43.25 |