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.
Highlights
In the field of wood science, there is a growing interest in computer vision (CV)-based wood identification
In the classification of micrograph datasets, several studies have proven that local feature techniques for extracting morphological information about wood cells, represented by the scaleinvariant feature transform (SIFT), are superior to texture features such as the gray-level co-occurrence matrix and local binary patterns [4, 8]
This paper describes three image partition strategies based on Spatial pyramid matching (SPM), namely conventional SPM, radial-SPM, and tangential-SPM, that preserve the spatial information of Fagaceae cross-sectional micrographs, and evaluates their recognition performance through a comparison with other recognition strategies
Summary
In the field of wood science, there is a growing interest in computer vision (CV)-based wood identification. Applications of CV are expanding from automated wood identification systems to wood anatomical approaches [1,2,3,4]. Feature extraction from images is the most important process in determining the performance of CVbased wood identification systems. In the classification of micrograph datasets, several studies have proven that local feature techniques for extracting morphological information about wood cells, represented by the scaleinvariant feature transform (SIFT), are superior to texture features such as the gray-level co-occurrence matrix and local binary patterns [4, 8]. The CV-based wood identification strategies using micrograph datasets that have been reported to date have only focused on the morphological characterization of wood cells and their quantification, neglecting the spatial information among the features extracted
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