Publication details

Bark recognition using novel rotationally invariant multispectral textural features

Journal Article

Remeš Václav, Haindl Michal


serial: Pattern Recognition Letters vol.125 (2019), p. 612-617

project(s): GA19-12340S, GA ČR

keywords: Bark recognition, Tree taxonomy clasification, Spiral Markov random field model, textural feature

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abstract (eng):

We present novel rotationally invariant fully multispectral Markovian textural features applied for the efficient tree bark recognition. These textural features are derived from the novel descriptive multispectral spiral wide-sense Markov model. Unlike the alternative bark recognition methods based on various gray-scale discriminative textural descriptions, we benefit from fully descriptive color, rotationally invariant bark texture representation. The proposed methods significantly outperform the state-of-the-art bark recognition approaches regarding classification accuracy. Both our classifiers outperform convolutional neural network ResNet even on the largest public bark database BarkNet which contains 23 000 high-resolution images from 23 different tree species.

RIV: BD