Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Table 3 Performances of GLCM, LBP, and LPQ texture features for wood identification

References	Database	Image type	#SP/#IMG	Classifier	Classification rate (%)
References	Database	Image type	#SP/#IMG	Classifier	GLCM	LBP	LPQ
Prasetiyo et al. [147]	CAIRO	Stereo	25/2390	ANN	89.6	93.6	–
Martins et al. [47]	UFPR	Micro	112/2240	SVM	55.3	79.3	–
Kobayashi et al. [66]	–	XCT	6/240	k-NN	98.3	99.5	–
Cavalin et al. [115]	UFPR	Micro	112/2240	SVM	80.7	88.5	91.5
Paula Filho et al. [62]	UFPR	Macro	41/2942	SVM	56.0	68.2	61.8
Martins et al. [95]	UFPR	Micro	112/2240	SVM	4.1	66.3	86.7
Yadav et al. [122]	UFPR	Micro	75/1500	SVM	–	79.9	93.5
da Silva et al. [71]	RMCA	Micro	77/1221	k-NN (k = 1)	–	85.0	87.4

Stereo stereogram, Macro macroscopic image, Micro micrograph, XCT X-ray computed tomographic image, #SP number of species, #IMG number of images, ANN artificial neural network, SVM support vector machine, k-NN k-nearest neighbors

ISSN: 1746-4811