Abstract
The goal of this work is the fully-automated detection of cellulose fibre cross sections in microtomy images. A lack of significant appearance information makes edges the only reliable cue for detection. We present a novel and highly discriminative edge fragment descriptor that represents angular relations between fragment points. We train a Random Forest with a plurality of these descriptors including their respective center votes. In such a way, the Random Forest exploits the knowledge about the object centroid for detection using a generalized Hough voting scheme. In the experiments we found that our method is able to robustly detect fibre cross sections in microtomy images and can therefore serve as initialization for successive fibre segmentation or tracking algorithms.
Original language | English |
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Title of host publication | Proceedings for ICPR 2010 |
Publisher | . |
Pages | 316-319 |
Number of pages | 4 |
ISBN (Print) | 9780769541099 |
DOIs | |
Publication status | Published - 2010 |
Event | 20th International Conference on Pattern Recognition: ICPR 2010 - Istanbul, Turkey Duration: 23 Aug 2010 → 26 Aug 2010 |
Conference
Conference | 20th International Conference on Pattern Recognition |
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Abbreviated title | ICPR'10 |
Country/Territory | Turkey |
City | Istanbul |
Period | 23/08/10 → 26/08/10 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition