A Comparative Study of Feature Extraction Methods for Wood Texture Classification

Prasetiyo,; Khalid, Marzuki; Yusof, Rubiyah; Mériaudeau, Fabrice

doi:10.1109/sitis.2010.15

Cited by 20 publications

(13 citation statements)

References 13 publications

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“…Because previous research related to the mechanical properties of wood is done by using ultrasonic waves, x-rays and other laboratory equipment (not use image of wood), then the comparison against the results in this paper aren't done. Research on the image of wood is generally performed to detect wood defects [1], [7] or to classify wood based on species [9]- [13]. Therefore, we present experimental results of classification of wood database that we use in this paper were implemented in some classification method based on wood species.…”

Section: E Xperimental Resultsmentioning

confidence: 99%

Wood Classification Based on Edge Detections and Texture Features Selection

Fahrurozi

Madenda

Ernastuti

et al. 2016

IJECE

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One of the properties of wood is a mechanical property, includes: hardness, strength, cleavage resistance, etc. Among these properties there that can be measured or estimated by visual observation on cross-sectional areas of wood, which is based on inter-fiber density, fiber size, and lines that build the annual rings. In this paper, we proposed a new wood quality classification method based on edge detections. Edge detection is applied to the wood test images with the aim to improving the characteristics of wood fibers so as to make it easier to distinguish their quality. Gray Level Cooccurrence Matrix (GLCM) used to obtain wood texture features, while the wood quality classification done by Naïve Bayes classifier. Found in our experimental results that the first-order edge detection is likely to provide a good accuracy rate and precision. The second order edge detection is highly dependent on the choice of parameters and tends to give worse classification results, as filtering the original wood image, thus blurring characteristics related to wood density. Selection of features obtained from co-occurrence matrix is also quite affected the classification results.

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Section: E Xperimental Resultsmentioning

confidence: 99%

Wood Classification Based on Edge Detections and Texture Features Selection

Fahrurozi

Madenda

Ernastuti

et al. 2016

IJECE

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“…As explained in the first part, that this study was conducted to classify wood based on its quality with a focus on utilizing the image of the test wood. Because previous research related to the mechanical properties of wood is done by using ultrasonic waves, x-rays and other laboratory equipment (not use image of wood), then the comparison against the results in this paper Research on the image of wood is generally performed to detect wood defects [1], [7] or to classify wood based on species [9]- [13]. Therefore, we present experimental results of classification of wood database that we use in this paper were implemented in some classification method based on wood species.…”

Section: E Xperimental Resultsmentioning

confidence: 99%

Wood Classification Based on Edge Detections and Texture Features Selection

Fahrurozi¹,

Madenda²,

Ernastuti³

et al. 2016

IJECE

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<p>One of the properties of wood is a mechanical property, includes: hardness, strength, cleavage resistance, etc. Among these properties there that can be measured or estimated by visual observation on cross-sectional areas of wood, which is based on inter-fiber density, fiber size, and lines that build the annual rings. In this paper, we proposed a new wood quality classification method based on edge detections. Edge detection is applied to the wood test images with the aim to improving the characteristics of wood fibers so as to make it easier to distinguish their quality. Gray Level Co-occurrence Matrix (GLCM) used to obtain wood texture features, while the wood quality classification done by Naïve Bayes classifier.<em> </em>Found in our experimental results that the first-order edge detection is likely to provide a good accuracy rate and precision. The second order edge detection is highly dependent on the choice of parameters and tends to give worse classification results, as filtering the original wood image, thus blurring characteristics related to wood density. Selection of features obtained from co-occurrence matrix is also quite affected the classification results.</p>

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“…SVM requires less space than k-NN because it learns the training data and builds a classification model in advance. SVM has been shown to outperform k-NN for wood identification [68,73,128,147,148]. In most studies that compared the two classifiers in the same classification strategy, SVM outperformed k-NN (Table 5).…”

Section: Classificationmentioning

confidence: 99%

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

Hwang

Sugiyama

2021

Plant Methods

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The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.

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A Comparative Study of Feature Extraction Methods for Wood Texture Classification

Cited by 20 publications

References 13 publications

Wood Classification Based on Edge Detections and Texture Features Selection

Wood Classification Based on Edge Detections and Texture Features Selection

Wood Classification Based on Edge Detections and Texture Features Selection

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

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