Abstract
In previous studies, we showed that the branch length similarity (BLS) entropy profile could be successfully used for the shape recognition such as battle tanks, facial expressions, and butterflies. In the present study, we proposed new descriptors, roundness, symmetry, and surface roughness, for the recognition, which are more accurate and fast in the computation than the previous descriptors. The roundness represents how closely a shape resembles to a circle, the symmetry characterizes how much one shape is similar with another when the shape is moved in flip, and the surface roughness quantifies the degree of vertical deviations of a shape boundary. To evaluate the performance of the descriptors, we used the database of leaf images with 12 species. Each species consisted of 10 - 20 leaf images and the total number of images were 160. The evaluation showed that the new descriptors successfully discriminated the leaf species. We believe that the descriptors can be a useful tool in the field of pattern recognition.
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A correction to this article is available at http://dx.doi.org/10.3938/jkps.71.1075
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Kwon, O., Lee, SH. Branch length similarity entropy-based descriptors for shape representation. J. Korean Phys. Soc. 71, 727–732 (2017). https://doi.org/10.3938/jkps.71.727
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DOI: https://doi.org/10.3938/jkps.71.727