Reliability of wood grain orientation measurements using laser illumination

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100 (2008) 479– 483

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Research Paper: AE—Automation and Emerging Technologies

Reliability of wood grain orientation measurements using laser illumination Rafael de Oliveira Fariaa, Roberto Alves Braga Jr.a,, Antoˆnio Elizeu da Rocha Netoa, Na´dia Trindadea, Fa´bio Akira Moria, Graham William Horganb a

Departamento de Engenharia, Universidade Federal de Lavras (UFLA), CP 3037, CEP 37200-000 Lavras, MG, Brazil BioMathematical Statistical Scotland, Roweett Research Institute, Bucksburn, Aberdeen AB21 9SB, Scotland, UK

b

art i cle info

This work evaluates an optical laser-based approach to measuring wood grain orientation, with results obtained by an automatic procedure. Wood grain orientation is of special

Article history:

importance in forest research but only manual methods have so far been available to

Received 10 July 2007

measure the orientation of a grain within a tree. Manual methods are crude and offer only

Received in revised form

one piece of information about the angle of a piece of wood under analysis. The optical

30 April 2008

laser approach presented in this paper demonstrates an alternative procedure using laser

Accepted 6 May 2008

illumination and image analysis of the inertial moment. This has major advantages in

Available online 9 July 2008

terms of reliability and robustness. The influence of laser illumination angle, the sensitivity of the analysis and the repeatability of a single sample were investigated. The results showed that the proposed method produced reliable results, and is a rapid and inexpensive alternative to the manual method. & 2008 IAgrE. Published by Elsevier Ltd. All rights reserved.

1.

Introduction

Grain orientation is an important feature of wood production and processing. The term ‘grain’ refers to the longitudinal alignment of wood cells, denoting the direction of wood elements. In a tree, the grain is aligned spirally around the long axis of the trunk. Classification of spiral grain has been an aim of researchers since the nineteenth century. Noskowiak (1963) and Lowery (1966) proposed a classification system based on manual measurements. Manual measurements have some limitations; they have poor repeatability and sensitivity. This has prompted researchers to seek new measurement techniques. Hu et al. (2004) and Simonaho et al. (2004) presented laser illumination and image processing as a new approach for the measurement of fibre orientation; both groups used the same illumination system.

Optics, machine vision and/or image analysis, has been widely adopted in the study of biological materials as described by Paliwal et al. (2003), Herna´ndez-Sa´nchez et al. (2006), Park and Chen (1996) and Blasco et al. (2003). Special optical techniques have been developed to analyse features revealed by the coherent illumination of lasers and they have increasingly been adopted for measurement purposes. The use of coherent, high intensity and highly directional illumination requires special techniques to analyse the images. Hu et al. (2004) and Simonaho et al. (2004) showed that when the high-intensity laser light reaches a surface, light scatters according to the grain orientation. Hu et al. (2004) adopted a method to analyse the elliptical images formed using the Sobel edge technique, to determine the figure contour, and the modified Hough transform to obtain the features of the best-fit ellipse.

Corresponding author.

E-mail address: [email protected] (R.A. Braga Jr.). 1537-5110/$ - see front matter & 2008 IAgrE. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.biosystemseng.2008.05.006

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This work presents an alternative approach to that of Hu et al. (2004) and Simonaho et al. (2004) to determine grain angle orientation. It uses a direct method to illuminate and capture the images, with less equipment, and uses the inertial moment calculation to analyse the images, implemented using a free software platform. The reliability of this approach was evaluated using varying angles of illumination and variable grain within the sample.

ccd

mirror

laser

wood sample

2.

Theoretical considerations

2.1.

Inertial moment

The approach proposed estimates the orientation of the grain distribution without the need to fit an ellipse. If we have an intensity distribution given by f(x, y), we can define the centroid of the distribution as (x0, y0) given by: R xf ðx; yÞ x0 ¼ R f ðx; yÞ R yf ðx; yÞ y0 ¼ R f ðx; yÞ where integration indicates summation over all pixels in some suitable region containing the distribution, and the central moments Mkl as: Z Z Mkl ¼ ðx  x0 Þk ðy  y0 Þl f ðx; yÞ= f ðx; yÞ The distribution then has the maximum extent along the direction of its major axis, which is given by:   1 2M11 ¼ tan1 2 ðM02  M20 Þ If M02oM20 a value of 12p is added, where f is measured clockwise, with the horizontal direction being zero. More details have been given by Glasbey and Horgan (1995). Some pre-processing steps may be required before the above formulae are used, such as adjustment of the image contrast details, and the choice of an intensity threshold to eliminate the background. These procedures can be implemented using standard image analysis software.

2.2.

Diffraction in wood grain

The main hypothesis regarding the formation of the ellipsoidal shape in the scattering images is related to the diffraction inside the elongated cells, which form the fibres. The propagation of light occurs by diffraction mainly in the direction of the fibres because the walls, which contain cellulose deflect the lateral propagation, thus forming the ellipsoidal scattering shape that is used to measure the angle of the grains.

Fig. 1 – Optical configuration.

the laser set was a HeNe laser with 10 mW power, and the camera was connected to a microcomputer by using a frame grabber. The wood samples were from Eucalyptus grandis W. Hill ex Maiden with the dimensions 300  95  15 mm. The samples were analysed in the axial-growing direction.

3.2.

Vertical angle illumination

Changes in the angle of illumination were produced using a rotational graduated device that alters the angle of the mirror in a controlled manner. The camera and the wood were moved so as to maintain illumination of the same portion of the sample, as shown in Fig. 2.

3.3.

Sensitivity of grain angle determination

The sensitivity of the measurement to the angle of placement of the wood sample was assessed using a rotatable table with angular graduations. The table was rotated in steps of 21 as shown in Fig. 3. The aim was to evaluate the sensitivity of the configuration to reliably measure a range of angles with respect to the axial reference direction. A step of 21 was used since smaller values did not produce reliable results.

3.4.

Variability within a sample

The variability of the angle at different positions within the same sample of wood was evaluated by illuminating four rows of the wood sample covering a range of 20 mm. Each row was illuminated at 10 points separated by 20 mm.

3.

Materials and methods

3.5.

3.1.

Optical configuration

The method was based on a software routine that can be used on a free platform (Imagej, NIH, USA; http://rsb.info.nih.gov/ ij/). The software code used to implement the automatic determination of angle is presented in Appendix.

The configuration used is shown in Fig. 1. A neutral filter was used to reduce the intensity of saturation in the camera, and

Automatic determination

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Table 1 – Grain angle measurement under a range of angle illumination Mirror angle

ccd

mirror

Grain angle

Measured angle

Error

0 0 0 0 0

2.31 0.37 0.3 0.75 1.59

2.31 0.37 0.3 0.75 1.59

50 55 60 65 70

Table 2 – Comparison of measured and known grain angle

wood sample Table angle1

vertical angle

Fig. 2 – Vertical angle illumination configuration.

laser

Measured angle1

Error1

2.002 2.766 5.395 6.577 6.425 8.653 13.100 12.611 12.750 16.768 20.537 19.807 24.148 23.784 27.479 30.000

2 0.77 1.4 0.58 1.58 1.35 1.1 1.39 3.25 1.23 0.54 2.19 0.15 2.22 0.52 0

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

mirror

5 0

sample

Angle °

−5 −10 −15 −20

table

−25

Fig. 3 – Schematic of sample rotation.

1

4.

Results and discussions

4.1.

Vertical angle illumination

The sensitivity to vertical angle illumination of the technique is presented in Table 1, with angles varying from 501 to 701. The results presented in Table 1 demonstrate that the technique is robust with respect to vertical angle illumination, since the error ranged from 2.311 to 1.591 in the 201 range of variation vertically. The values appeared to show a pattern that may be explained as resulting from the deviation of the table alignment when the sample position was altered

2

3

4

5 6 Point

7

8

9

10

Fig. 4 – Variation of angle in four rows of a sample.

in order to ensure consistent illumination after the change in angle.

4.2.

Sensitivity of grain angle determination

The estimated grain angle as a function of sample placement is presented in Table 2. It can be seen that the difference in the measurements was always less than 3.251. Over a range of

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4.3.

table angles, the estimated angle was close to the true angle (noting the equivalence of 01 and 3601). These errors can also be attributed to the devices used and human intervention when measuring and adjusting the rotation.

Variability within a sample

The variability of the angle at different positions within the same sample of wood was investigated by illuminating 4 rows over a range of 20 mm along the axial-growing direction. The results presented in Fig. 4 show means of 7.451, 12.481, 9.021 and 7.371. The variation within a single sample can be observed by visual inspection along the axial-growing direction. In the sample evaluated, the point with the highest variation can be observed in Fig. 5. It can be seen that the angle varies as calculated by the proposed methodology. This reinforces the conclusion that the optical method can follow the variation of the grain over the entire illuminated surface. The automated results obtained using the macro are shown in Fig. 6. The images produced by laser illumination in grey scale are arranged side by side showing the angle change during the scanning process in a row along the axial-growing direction in the wood sample, with the angle values indicated.

-3°

2,26° Point 5 6,77°

5. 10,26°

Conclusions

The automatic procedure we have evaluated to measure grain angle in wood appears to provide objective information at a

Fig. 5 – Image of a point in the four rows.

3.81°

3.94°

−0.21°

5.32°

−1.59°

−0.61°

−8.18°

−6.76°

8.92°

2.76°

2.37°

Fig. 6 – Angle variation in a single portion of a sample.

ARTICLE IN PRESS BIOSYSTEMS ENGINEERING

100 (2008) 479 – 483

low cost. The reliability of data was shown over a range of illumination angles and with the ability to estimate the variation of angle with low error. In contrast to other methods, the variability of the grain angle in the wood was measured reproducibly.

run(‘‘Measure’’); //Get the angle value a ¼ getResult(‘‘Angle’’, k1); //Record the angle y[k1] ¼ a; j ¼ getHeight( ); i ¼ getWidth( ); }

Acknowledgements This work was supported by the Federal University of Lavras, CNPq and by the Scottish Government.

Appendix Automated macro for a row of measurements—ImageJ //Clear memory run(‘‘Clear Results’’); //Smooth the data convolving it with a black square centred in a white pattern //Square width, in pixel 2radius+1. run(‘‘Gaussian Blury’’, ‘‘radius ¼ 6 stack’’); //Transform the data in a 8-bits images run(‘‘8-bit’’); //Adjust the limits setAutoThreshold( ); setThreshold(102, 255); run(‘‘Convert to Mask’’, ‘‘black’’); //Grain orientation calculation with fit ellipse procedure run(‘‘Set Measurementsy’’, ‘‘fit redirect ¼ None decimal ¼ 3’’); //Create a vector to record the angles y ¼ newArray(nSlices); for ( k ¼ 1; ko ¼ nSlices; k++){ //Ensure stack reference to always catch one position after the other setSlice(k); for (i ¼ 0; iogetWidth( ); i++){ for(j ¼ 0;jogetHeight( );j++){ //look the first black pixel of the grain // if(getPixel(i, j)! ¼ 0){ //Select the grain to precede the measurement doWand(i, j); //Start the measurement

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} } } //Create a vector to show the results x ¼ newArray(nSlices); for(i ¼ 0; ionSlices ; i++){ x[i] ¼ (i+1); } Plot.create(‘‘Gra’’,’’Imagem’’,’’Grau’’,x,y); // end of the program

R E F E R E N C E S

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