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Detection and visualization of encoded local features as anatomical predictors in cross-sectional images of Lauraceae

Journal of wood science, 2020, Vol.66 (1), p.1-12, Article 16 [Peer Reviewed Journal]

The Author(s) 2020 ;Journal of Wood Science is a copyright of Springer, (2020). All Rights Reserved. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1435-0211 ;EISSN: 1611-4663 ;DOI: 10.1186/s10086-020-01864-5

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  • Title:
    Detection and visualization of encoded local features as anatomical predictors in cross-sectional images of Lauraceae
  • Author: Hwang, Sung-Wook ; Kobayashi, Kayoko ; Sugiyama, Junji
  • Subjects: Algorithms ; Biomedical and Life Sciences ; Characterization and Evaluation of Materials ; Computer vision ; Histograms ; Image recognition ; Life Sciences ; Mapping ; Materials Science ; Optical micrograph ; Original Article ; SIFT ; Visual codebook ; Wood anatomy ; Wood Science & Technology
  • Is Part Of: Journal of wood science, 2020, Vol.66 (1), p.1-12, Article 16
  • Description: This paper describes computer vision-based quantitative microscopy and its application toward better understanding species specificity. An image dataset of the Lauraceae family that consists of nine species across six genera was investigated, and structural features were quantified using encoded local features implemented in a bag-of-features framework. Of the algorithms used for feature detection, the scale-invariant feature transform (SIFT) achieved the best performance in species discrimination. In the bag-of-features framework with the SIFT features, each image is represented by a histogram of codewords. The codewords were further analyzed by mapping them to each image to visualize the corresponding anatomical elements. From this analysis, we were able to classify and quantify the modes of aggregation of different combinations of cell elements based on clustered codewords. An analysis of the term frequency–inverse document frequency weights revealed that blob-based codewords are generally shared by all species, whereas corner-based codewords are more species specific.
  • Publisher: Singapore: Springer Singapore
  • Language: English
  • Identifier: ISSN: 1435-0211
    EISSN: 1611-4663
    DOI: 10.1186/s10086-020-01864-5
  • Source: SpringerOpen
    ProQuest Central
    DOAJ Directory of Open Access Journals
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