The Detection of straight and Slant Wood Fiber Through slop angle fiber feature

Atmaja, Ratri Dwi; Susanto, Erwin; Halomoan, Junartho; Indraloka, Gurnita Koncara; Murti, Muhammad Ary

doi:10.11591/telkomnika.v14i2.7725

Cited by 5 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…Therefore, wood will have several of mechanical properties, textures, shapes and colors. The determination of wood quality can be based on several things, including slope of the wood fiber [1], defects in the wood, especially knots [2] or the mechanical properties of wood [3], [4]. Hardness and stiffness are mechanical properties that frequently observed and considered to determine quality of wood [4], and have positive correlation to density of wood [5].…”

Section: Introductionmentioning

confidence: 99%

“…Digital image-based research has been developed for considering the characteristics of the object in a digital image that contains the object. Nondestructive methods related to wood quality tests have been done such as by using a wood image based on the slope of the wood fiber [1] or considering the number of defects and their distributions [7]. This study focuses on the classification of wood quality using a wood image, but is observed a test wood texture by using texture features of wood.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wood Classification Based on Edge Detections and Texture Features Selection

Fahrurozi

Madenda

Ernastuti

et al. 2016

IJECE

View full text Add to dashboard Cite

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.

show abstract

Section: E Xperimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wood Classification Based on Edge Detections and Texture Features Selection

Fahrurozi

Madenda

Ernastuti

et al. 2016

IJECE

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

<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>

show abstract