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Evaluation of image partitioning strategies for preserving spatial information of cross-sectional micrographs in automated wood recognition of Fagaceae
DC Field | Value | Language |
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dc.contributor.author | Hwang, Sung-Wook | - |
dc.contributor.author | Sugiyama, Junji | - |
dc.date.accessioned | 2021-05-13T05:21:53Z | - |
dc.date.available | 2021-05-13T14:40:08Z | - |
dc.date.issued | 2021-02-28 | - |
dc.identifier.citation | Journal of Wood Science. 2021 Feb 28;67(1):18 | ko_KR |
dc.identifier.issn | 1611-4663 | - |
dc.identifier.uri | https://hdl.handle.net/10371/174355 | - |
dc.description.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. | ko_KR |
dc.description.sponsorship | This study was supported by a Grant-in-Aid for Scientifc Research (Grant Number H1805485) from the Japan Society for the Promotion of Science | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | Springer Open | ko_KR |
dc.subject | Computer vision | - |
dc.subject | Image recognition | - |
dc.subject | Spatial pyramid matching | - |
dc.subject | Wood identification | - |
dc.title | Evaluation of image partitioning strategies for preserving spatial information of cross-sectional micrographs in automated wood recognition of Fagaceae | ko_KR |
dc.type | Article | ko_KR |
dc.contributor.AlternativeAuthor | 황성욱 | - |
dc.identifier.doi | doi.org/10.1186/s10086-021-01953-z | - |
dc.citation.journaltitle | Journal of Wood Science. | ko_KR |
dc.language.rfc3066 | en | - |
dc.rights.holder | The Author(s) | - |
dc.date.updated | 2021-02-28T04:21:03Z | - |
dc.citation.number | 1 | ko_KR |
dc.citation.startpage | 18 | ko_KR |
dc.citation.volume | 67 | ko_KR |
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