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Evaluation of image partitioning strategies for preserving spatial information of cross-sectional micrographs in automated wood recognition of Fagaceae

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Authors
Hwang, Sung-Wook; Sugiyama, Junji
Issue Date
2021-02-28
Publisher
Springer Open
Citation
Journal of Wood Science. 2021 Feb 28;67(1):18
Keywords
Computer visionImage recognitionSpatial pyramid matchingWood identification
Abstract
Abstract
Although wood cross sections contain spatiotemporal information regarding tree growth, computer vision-based wood identification studies have traditionally favored disordered image representations that do not take such information into account. This paper describes image partitioning strategies that preserve the spatial information of wood cross-sectional images. Three partitioning strategies are designed, namely grid partitioning based on spatial pyramid matching and its variants, radial and tangential partitioning, and their recognition performance is evaluated for the Fagaceae micrograph dataset. The grid and radial partitioning strategies achieve better recognition performance than the bag-of-features model that constitutes their underlying framework. Radial partitioning, which is a strategy for preserving spatial information from pith to bark, further improves the performance, especially for radial-porous species. The Pearson correlation and autocorrelation coefficients produced from radially partitioned sub-images have the potential to be used as auxiliaries in the construction of multi-feature datasets. The contribution of image partitioning strategies is found to be limited to species recognition and is unremarkable at the genus level.
ISSN
1611-4663
Language
English
URI
https://hdl.handle.net/10371/174355
DOI
doi.org/10.1186/s10086-021-01953-z
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College of Agriculture and Life Sciences (농업생명과학대학)Dept. of Forest Sciences (산림과학부)Journal Papers (저널논문_산림과학부)
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