A Machine Vision based Lumber Tracing System

Riku Hietaniemi, Sami Varjo and Jari Hannuksela

Center for Machine Vision Research, University of Oulu, Oulu, Finland

Keywords:

CS-LBP, Usurf, Projection Signal, 1-D Alignment.

Abstract:

In this paper, we introduce a machine vision system for wooden board tracing in sawmills. The goal is to

match images taken from boards in the beginning and at the end of the manufacturing process in order to

track the movement of individual boards. The task is challenging due to the changing appearance of boards

during the process. These are changes in color, texture and physical form. Lighting conditions and camera

parameters are also unknown and can change between different camera systems inside a sawmill. Before

matching, image alignment is carried out using 2-D to 1-D projection signals. Signals are generated using

the statistical properties of gray scale images. Aligned images are then matched using fast and compact local

descriptors. The performance of the system was evaluated using over 1000 real life images captured with

visual quality control cameras integrated into the production line. A tracing accuracy over 95% was achieved

with a high conﬁdence of the individual match.

1 INTRODUCTION

With advances in measurement technologies, we are

approaching a point where it is possible to trace wood

all the way from the harvesting location to a ﬁnal

product. Tracing reveals important information about

how the whole wood reﬁnement process and the efﬁ-

ciency of the supply chain can be improved. For ex-

ample, by backtracking the logs and boards sawmills

will learn which harvesting areas produce the highest

quality raw material.

One part of the tracking chain is inside the

sawmill. In the beginning of the process boards pass

under a line or matrix camera system for quality and

dimensional measurement. At the end, before pack-

ing, boards pass under a second camera system for

the ﬁnal visual quality classiﬁcation. A system that

can detect when a board has passed through the pro-

cess is needed so that the board can be traced back to

the log it was sawn from.

The traditional way of object identiﬁcation in

mass production is the use markers such as bar codes,

that are added to the product. For example, commer-

cial products for lumber tracing using painted iden-

tiﬁers are available. RFID tags (Sirkka, 2008) are

also becoming an attractive option due to advances

in wireless technologies. In addition, previous re-

search also includes the use of microwave signatures

(Fuentealba et al., 2004).

In this paper, however, we present the ﬁrst ap-

proach using only features extracted from images.

Compared with the other systems, which require ad-

ditional equipment to be installed, camera based so-

lutions need only additional software, since the same

cameras that carry out the visual inspection can be

used. This makes it possible to increase the total yield

of a sawmill with only minimal investments.

Typically, the image based quality classiﬁcation

of boards is based on knot sizes and locations, color,

shakes and other visible defects (Gu et al., 2010).

However, these properties alone are not suitable for

robust matching because of the changes taking place

during the process. Although grain patterns in the

board can experience some slight changes, we found

out that these patterns are a good basis for tracing.

Figure 1 shows an example of these patterns.

Finding a matching pair of two images for the

same board is a challenging problem due to notable

changes that the manufacturing process imposes on

the appearance of the board. There are two types of

changes: actual changes in the board itself caused by

the process and camera induced changes such as lens

distortion and focus blur. It is also highly unlikely

that the lighting conditions are similar for all cameras.

Different sawmills have different camera systems and

in some cases all the cameras installed at one site can

be of different types. Figure 2 shows some of the main

anomalies including changes in color and surface tex-

Hietaniemi R., Varjo S. and Hannuksela J..

A Machine Vision based Lumber Tracing System.

DOI: 10.5220/0004279300980103

In Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP-2013), pages 98-103

ISBN: 978-989-8565-48-8

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: An example of a grain pattern image. Strongest grain are clearly visible.

Figure 2: Typical changes in the appearance of the board caused by the process. Board in the beginning up / board in the end

down.

Figure 3: An example of physical change experienced by a board.

Figure 4: Layout of the production line and tracing components.

ture. Figure 3 presents a case where the physical form

of the board is altered.

In our contribution, we present a complete sys-

tem framework for tracing wooden boards inside a

sawmill. A suitable method for fast image alignment

was implemented using 2-D to 1-D projection signals.

The required preprocessing steps for images are de-

scribed. For matching, we evaluated two different de-

scriptors that are claimed by their original authors to

be fast and compact. Tracing performance of the sys-

tem was ﬁnally tested using real life images obtained

from the production line of a sawmill.

2 BOARD TRACING METHOD

The proposed method is divided into three main

phases. Images are ﬁrst preprocessed, resulting in a

cropped gray scale image of a section of a board. For

images taken in the beginning of the process, features

are extracted from the board and stored to a database

together with a 1-D signal which is used for align-

ment later. For images taken at the end of the pro-

cess, 1-D signal is ﬁrst formed and the translation be-

tween images is calculated. After the image align-

ment, features are extracted from the corresponding

area. Matches between camera system 1. and camera

system 2. (see Figure 4) are searched for by calculat-

ing the Euclidean distance between the ﬁnal feature

matrix and feature matrices stored in the database.

Figure 4 illustrates the layout of the production line

and the tracing system.

To be able to run board tracing at full production

speed numerous things have to be taken into consid-

eration. Firstly, the amount of raw image data is mas-

sive, in this case over 16 MB per board. Secondly, the

time that is allowed for feature comparison is only a

few hundred milliseconds for high production speeds.

The database can contain thousands of boards mean-

ing that the computational complexity of the match-

AMachineVisionbasedLumberTracingSystem

Figure 5: Original and result images of preprocessing.

Figure 6: A cropped section of a board with 1-D projection signal plotted over.

ing has to be low. Also, the need to store thousands

of boards sets limits on the amount of data that can be

used in the matching stage.

2.1 Image Preprocessing

The preprocessing stage is comprised of gray color

transformation, cropping,segmentation, straightening

and background removal. The original raw data for

one board was a 6400 x 600 pixel RGB color image

containing the ﬂat and side/ﬂat view of the board. The

bottom 300 pixels containing the ﬂat/side view is im-

mediately cropped. The remaining area is converted

to a gray scale image.

From the gray scale image the corners of the right

hand side of the board are searched for using thresh-

olding in a small sliding window. After the end of the

board is found, a smaller section of N x 300 pixels

is cropped, where N is the ﬁnal width of the required

image area. From this cropped section, the left hand

side corners are searched for and the board is straight-

ened by removing background pixels above the board

and moving each row upwards equal to the removed

pixels.

Although straightening induces some geometrical

distortions because of the straightforward nature of

the algorithm, no incorrect matches were observed

due to straightening errors. Straightening also re-

moves the need for y-directional alignment since the

whole height of the board is utilized later in the fea-

ture extraction.

2.2 Feature Extraction

For an application which has strict data storage and

computational time requirements the features used

need to be compact and fast to compute. The two

descriptors chosen here, Center-Cymmetric Local Bi-

nary Pattern (CS-LBP) (Heikkil¨a et al., 2009) and

Upright Speeded-Up Robust Feature (U-SURF) (Bay

et al., 2008), are both modiﬁed, faster versions of al-

ready compact and computationally inexpensive solu-

tions. These descriptors are favorable due to the na-

ture of the problem. Boards move through the process

in a ﬁxed position, eliminating the need for rotation

invariance. Also, as stated earlier, most of the infor-

mation lies in the grain patterns which are closely re-

lated to the gradients in images. U-SURF is formed

using gradient information while CS-LBP is claimed

by the original authors to have ”gradient like proper-

ties”.

Multiple feature vectors were calculated for each

image using W subareas. The size of one subarea is h

× w, where h was chosen to be the height of the board

in pixels to utilize the maximum amount of local in-

formation. Different subarea widths w were tested to-

gether with different amounts of subareas W. When

using large subareas, the information stored inside

one descriptor is mainly from grain patterns and local

defects such as knots have a smaller effect, whereas

small subareas concentrate on the ﬁner detail.

Since the desired feature vector length in our ap-

proach is short, based on our tests, narrow subareas

offer the best matching accuracy. The effect of w ver-

sus matching accuracy is further illustrated in Exper-

imental results (Figure 8).

CS-LBP feature vectors are formed by compar-

ing opposite intensity values instead of comparing a

single intensity value with the center pixel, which is

the original local binary pattern method (Ojala et al.,

2002). Feature descriptors used in our work were

formed using the method presented in (Heikkil¨a et al.,

2009). A radius of 1 and neighborhood of 8 was used

together with 2 × 2 subgrid and 16 bin histogram pro-

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100

ducing a feature vector with the length of 64. A 4 × 4

subgrid is also common but in order to keep the fea-

ture vector length short the histogram bin count would

be reduced. In our tests, we obtained better matching

accuracy using fewer subgrids and a larger number of

histogram bins.

U-SURF features were formed in a similar man-

ner, except for the size of the subgrid which was set

to 4x4. The vector describing each subgrid contains

four values making the length of the ﬁnal feature vec-

tor for U-SURF also 64. A detailed explanation of the

method can be found in (Bay et al., 2008).

2.3 Alignment

To ensure that the subareas are correctly matched to-

gether, alignment of the input images is required. A

small offset is tolerated, as demonstrated later in the

results section, meaning that sub-pixel accuracy is not

needed. In order to keep the alignment process data

storage space and computational complexity require-

ments low, a 2-D to 1-D projection is utilized (Alliney

and Morandi, 1986).

The steps included in the alignment process are

following:

1. A 1-D signal is formed from the image by calcu-

lating the standard deviation of each image row.

2. The spatial difference between signals is calcu-

lated using phase correlation or cross correlation.

3. The image taken at the end of the process is

shifted according to the difference found.

Because we are interested in the translation in the

longitudinal direction the 1-D signals are formed in

an x-direction only. Multiple projection types were

tested, including the minimum and the maximum val-

ues for columns as well as column sums. Due to the

nature fo the material in hand, these projection strate-

gies do not offer sufﬁcient discriminative qualities for

signal generation. Using the standard deviation for

each column improved robustness considerably. An

example of how the projected signal is related to the

appearance of the board can be seen in Figure 6. As

we can see from the ﬁgure, defects and anomalities

like knots produce the highest variations to the gener-

ated signal.

The extracted 1-D vector can be used to determine

horizontal displacement with both the cross correla-

tion and the phase correlation (Stone, 2011). The sig-

nal background variations were smoothed using a low

pass ﬁltering with zero mean and unit variance nor-

malization. The ﬁlter border effects were removed

by zeroing the ends of the signal at the half length

of the ﬁlter. The cross correlation can be calculated

as a convolution of two signals where one of them

one is reversed. With the phase correlation, the sig-

nals are Fourier transformed, the phase difference

data is extracted, and the phase difference data can

be turned into cross phase correlation by using an in-

verse Fourier transformation. The offset can be deter-

mined by ﬁnding the index of the maximum value in

the correlation data. Detailed descriptions of both of

the methods can be found in (Stone, 2011).

Correlation techniques offer a fast and compu-

tationally inexpensive way to carry out the align-

ment. They have been widely utilized in different

machine vision applications for years and highly opti-

mized mathematical frameworks have been presented

for both methods (Agarwal and Cooley, 1977; Loan,

1992).

3 EXPERIMENTAL RESULTS

3.1 Test Material

The test material consisted of two sets: 1003 images

taken in the beginning and 495 images taken at the

end of the process. All boards were sawn from Nordic

pine, pinus sylvestris. Images were obtained from the

real production line of a sawmill. To verify the match-

ing results, an identiﬁcation number was added to ev-

ery board. Image sections were cropped from boards

in a way that the identiﬁcation number was not visi-

ble in the matching process. There were boards with

two different widths and multiple lengths. Board di-

mensions were not used in any way for matching. All

tests presented here are Matlab simulations.

3.2 Alignment

To study the robustness of the method against

board misalignment, without the proposed alignment

method, the offset was varied between 0 and 250 pix-

els with a sample size of 100 image pairs. The graph

in Figure 7 shows that the U-SURF is more sensi-

tive to misalignment than the CS-LBP. The U-SURF

matching accuracy is immediately affected, and de-

creases sharply after an 18 pixel offset, while CS-LBP

performance stays almost unaffected until 20 pixels.

The subarea width w used was 100 pixels, which

roughly translates to a offset tolerance of 20% for CS-

LBP. To conﬁrm the results the test was run again us-

ing 50 pixels as a value for w. CS-LBP matching ac-

curacy started to decline slightly after a 10 pixel off-

set, conﬁrming 20% tolerance.

In order to evaluate the proposed alignment

method, images were warped with a known offset for

AMachineVisionbasedLumberTracingSystem

101

0 10 20 30 40 50 60 70 80 90 100 110 120

100

Offset [pixels]

Matching Accuracy [%]

USURF No Reg

USURF Reg

CSLBP No Reg

CSLBP Reg

Figure 7: Matching performance with and without image

alignment.

ground truth. When considering alignment within 10

pixels as an acceptable result, using the standard de-

viation based projection signals, the phase correlation

yielded a 95.8% success rate and the cross correla-

tion a 95.6% success rate with only a 0.2% difference.

When the projection signals were formed using the

minimum value of each image column, the alignment

was successful in 91.8% of the cases. Using the sums

of the image columns produced a correct alignment

only for 64.0% of the boards. The results are summa-

rized in Table 1.

Table 1: Successful phase correlation alignment for differ-

ent projection signals. Alignment error less than 10 pixels

is considered to be success.

Projection type Acc. %

Standard deviation 95.8

Minimum column value 91.8

Column sum 64.0

3.3 Subarea Width

The subarea width w has a major effect on the match-

ing accuracy. If there is no alignment error, the re-

quired width is small. The matching accuracy versus

subarea width w is presented in Figure 8.

In this case, a width of 13 pixels was enough to

produce the best possible results. In reality, w has to

be set larger because of the small remaining alignment

errors and a larger comparison group. The proposed

alignment method typically leaves a small alignment

error of 0-10 pixels. To compensate for this, and fur-

ther increase the conﬁdence of the match, a subarea

width w of 50 pixels was chosen. Width of 50 pixels

provides the needed tolerance of 10 pixels.

6 8 10 12 14 16 18 20

100

Subarea width w [pixels]

Matching Accuracy [%]

Figure 8: The effect of subarea width on matching accu-

racy for CS-LBP method (100 board sample size, total of

15 subwindows).

3.4 Tracing Performance

The ﬁnal tracing performance depends on several fac-

tors: alignment, the number of subareas used W and

subarea width w. The size of w was set to 50 pixels

and the minimum required number of subareasW was

searched. Performance started to decline after 15 sub-

areas. Performance for CS-LBP features calculated

from a gray scale and a gradient image as well as U-

SURF features are presented in Table 2. The length of

the feature vector for all of the methods was 64. For

all the results presented in the table, an w of 50 and W

of 15 was used.

To evaluate the conﬁdence of the matches, a dis-

tance ratio R

C/I

was calculated by dividing the dis-

tance between the best and the second best match for

correct matches, D

12C

, with the corresponding dis-

tance for incorrect matches, D

12I

. Also, the percent-

age of correct matches, for which the Euclidean dis-

tance between the best and the second best match is

greater than the maximum distance in the case of an

incorrect match, D

C>I

, is presented.

Table 2: Tracing performance.

Method Acc. % R

C/I

C>I

CS-LBP 89.3 10 90

CS-LBP (gradient) 96.2 12 98

U-SURF 96.6 9 86

From the table, we can see that the overall match-

ing accuracy for both of the descriptors is high. Us-

ing a gradient image instead of a gray scale image

increased the accuracy of CS-LBP based matching

considerably. While the matching accuracy of the de-

scriptors is similar, the CS-LBP has a slightly better

conﬁdence of match.

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4 CONCLUSIONS

In this paper, we presented the ﬁrst machine vision

based board tracing system to be used in a sawmill

to track when a particular board has completed the

manufacturing process. This is one important part of

the chain from raw material to ﬁnal products. The

proposed system was evaluated using real life images

and was it able to ﬁnd a correct match for over 96% of

the tested 495 images from a test set of 1003 images.

We proposed a projection signal based alignment

method, which increased the robustness of the system

considerably. Instead of using column sums, standard

deviation of the column was used to create the 1-D

signal. If the alignment accuracy is improved while

keeping the computational complexity low, smaller

subarea widths can be used, thus lowering the total

number of operations required.

The use of gradient images increased the accu-

racy and conﬁdence of the CS-LBP based method.

One possible explanation for this is the material itself.

Grain patterns are unique to different boards, similar

to ﬁngerprints. When the feature vector length was

increased the beneﬁt from gradient images started to

decline, and with 256 long vectors, the matching ac-

curacy was the same. Both of the descriptors are com-

putationally inexpensive and they are well suited for

an application where real-time performance is a criti-

cal parameter.

For future work, implementation using parallel

computing needs to be considered. Accelerated meth-

ods for database queries and comparisons, as well

as strategies for limiting the number of feature ma-

trix comparisons, can offer a signiﬁcant increase in

the total system performance. Initial pruning of pos-

sible candidates for matching can be started in the

alignment state. Also, a new ORB descriptor (Rublee

et al., 2011), which is claimed by original authors to

be tens of times faster than the SURF method, should

be tested in this application. Fingerprint matching

techniques could also be well suited for this kind of

task.

The described system was targeted here to be used

inside sawmills although it is not in any way limited

to that application area.

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