Authors:
Kauê T. N. Duarte
;
Marco A. G. de Carvalho
and
Paulo S. Martins
Affiliation:
University of Campinas (UNICAMP), Brazil
Keyword(s):
Stomata, Wavelets, Automatic Counting, Watershed, Image Processing, Image Segmentation.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
Stomata are cells mostly found in plant leaves, stems and other organs. They are responsible for controlling the
gas exchange process, i.e. the plant absorbs air and water vapor is released through transpiration. Therefore,
stomata characteristics such as size and shape are important parameters to be taken into account. In this paper,
we present a method (aiming at improved efficiency) to detect and count stomata based on the analysis of
the multi-scale properties of the Wavelet, including a spot detection task working in the CIELab colorspace.
We also segmented stomata images using the Watershed Transform, assigning each spot initially detected as a
marker. Experiments with real and high-quality images were conducted and divided in two phases. In the first,
the results were compared to both manual enumeration and another recent method existing in the literature,
considering the same dataset. In the second, the segmented results were compared to a gold standard provided
by a speciali
st using the F-Measure. The experimental results demonstrate that the proposed method results in
better effectiveness for both stomata detection and segmentation.
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