Method to Determine Grade of Timber Using Image Processing Technique.

Osaki, Hirokazu; Tokunaga, Shuichi; Yoshio, Sako; Komatsubara, F.

doi:10.1299/kikaic.60.1862

Cited by 2 publications

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“…The monochrome camera was used to perform the defect detection of the wood surface. 13 Previous studies indicated that two components of a color space were required to detect the defect in Douglas fir. 14 In the present study, the single channel signal image (R signal) was used to locate the potential defect regions, and the R, G, and B signals were all used for identification because the R, G, and B signals were all testified to be useful information to enhance the identification of sound and dead knots.…”

Section: Resultsmentioning

confidence: 99%

Locating and identifying sound knots and dead knots on sugi by the rule-based color vision system

Tanaka

Ohtani

2004

J Wood Sci

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Knowledge of the occurrence of sound and dead knots on the surface of sugi is important for the classification and application of the material. This study examined a color vision system for detecting sound and dead knots on sugi. The system can be conceptually divided into three components: a CCD-camera scanning system, an imagesegmenting module, and a rule-based defect identifying module. The results showed that the potential defect regions could be located by Otsu's threshold algorithm in conjunction with t-test analysis. The accuracies of locating sound knots and dead knots were 92.6% and 97.1%, respectively. The rule-based approach was used to identify sound and dead knots and the identifying accuracies for sound knots and dead knots were 92.0% and 94.1%, respectively. The overall detection accuracy of the system was 87.6%. The results indicated that the rule-based color vision system is an efficient means of detecting sound knots and dead knots on sugi.

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Section: Resultsmentioning

confidence: 99%

Locating and identifying sound knots and dead knots on sugi by the rule-based color vision system

Tanaka

Ohtani

2004

J Wood Sci

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“…The defects regions were modeled as boundary rectangles in this study. 13 The rectangular model eliminates a substantial amount of clear wood from the board considered during the evaluation process because a few defects regions are rectangles. The split and the hole are expressed by continuous pixels that do not contain redundant pixels; hence the defects regions/ clear wood region pixel ratio can be used to measure the area of the defects region.…”

Section: Resultsmentioning

confidence: 99%

“…[1][2][3][4][5][6][7][8][9][10] Some machine vision systems based on the color CCD camera were developed to classify all the above defects using their color properties. [11][12][13][14] Holes are difficult to distinguish from the other defects with a tradi-tional color CCD camera for wood species of sugi because the holes have color and shape features identical to those of the knots. The splits are difficult to detect because they have color and shape features identical to those of the growth ring in the captured image.…”

Section: Introductionmentioning

confidence: 99%

Locating and identifying splits and holes on sugi by the laser displacement sensor

Tanaka

Ohtani

2003

J Wood Sci

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The split and the hole are two common defects on sugi (Cryptomeria japonica D.Don). They have a common feature in that they are associated with surface irregularities. We have developed a laser scanning system to detect the splits and the holes based on their thickness, which correlates spatially with the profile information. The displacements measured by the laser sensor were converted to pixel values to generate the displacement profile image. Both the splits and the holes manifested well in the image. A dedicated image-processing program written in Visual Basic has been developed. The defects regions were accurately located by the image processing. To identify the defects, eight recognition rules based on four features have been utilized. Furthermore, a method based on the pixel model was proposed to compute the area of the defect. The results indicated that the defects could be identified correctly, and the areas could be computed accurately using the pixels model.

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Method to Determine Grade of Timber Using Image Processing Technique.

Cited by 2 publications

References 0 publications

Locating and identifying sound knots and dead knots on sugi by the rule-based color vision system

Locating and identifying sound knots and dead knots on sugi by the rule-based color vision system

Locating and identifying splits and holes on sugi by the laser displacement sensor

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