The Forest Species Database (FSD) is composed of 2,240 microscopic images from 112 different species from two groups (Hardwood and Softwood), 85 genera and 30 families, . The images were acquired from the sheets of wood using a Olympus Cx40 microscope with 100 times zoom. The resulting color images were saved in PNG (Portable Network Graphics) format with no compression and a resolution of 1024 × 768 pixels. The figure below provides some samples of the database.
The FSD 1.0 is structured as follows:
- 112 species – 20 images per species = 2,240 images.
How to obtain access to the images
The FS Database may be used for non-commercial research provided you acknowledge the source of the image by citing the following paper in publications about your research:
- J. Martins, L. S. Oliveira, S. Nigkoski, R. Sabourin, A Database for Automatic Classification of Forest Species, Machine Vision and Applications, 24(3): 567-578, 2013. ( )
Compressed file [3.4GB]
Our last results on this database can be found in these references
- J. Martins, L. S. Oliveira, S., Sabourin, R., Combining Textural Descriptors for Forest Species Recognition, 38th Annual Conference of the IEEE Industrial Electronics Society, Montreal, Canada, 2012 ( ).
- P. Cavalin, J. Martins, L. S. Oliveira, M. Kapp, A Multiple Feature Vector Framework for Forest Species Recognition, 28th ACM Symposium on Applied Computing (ACM SAC 2013), pages 16-20, Coimbra, Portugal, 2013 (pdf)
- Haffemann, L. G. ; Cavalin, P. ; Oliveira, L.S. . Forest Species Recognition using Deep Convolutional Neural Networks. International Conference on Pattern Recognition (ICPR2014), pages 1103-1107, Stockholm, Sweden, 2014
- Kapp, M., Blot, R., Cavalin, P., Oliveira, L.S., Automatic Forest Species Recognition based on Multiple Feature Sets. International Joint Conference on Neural Networks (IJCNN2014), pages 1296-1303, Beijing, China, 2014
- Cavalin, P., Kapp, M., Oliveira, L. S., An Adaptive Multi-Level Framework for Forest Species Recognition, 2015 Brazilian Conference on Intelligent Systems (BRACIS 2015), Natal, 2015