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Traceability of Wood Products: A Structural Approach for Pattern Recognition
Copyright e IF AC Infonnation Control Problems in Manufacturing. Vienna. Austria. 200 I
IFAC
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0
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Publications www.elsevier.comilocate/ifac
TRACEABILITY OF WOOD PRODl"CTS: A STRlCTl'RAL APPROACH FOR PA TTER~
RECOG~ITIO~
Denise Choffel and Patrick Charpentier
CRAN - Centre of Research ofAutomatic Control of Nancy Faculty of Sciences B.P. 239 - 54506 Vandoeuvre-Ies-Nanc), Abstract: This paper takes places in the global problematic of traceability which is a large preoccupation in these recent years . The need of tracking and tracing products over their life-cycle generates the development of methods and techniques to perform the automatic identification. For usual manufacturing systems, electronic tags and bar-codes are successfully employed. However, in the wood industry, the nature of the material itself, makes difficult the identification of products. The idea presented is to use the property of heterogeneity of wood as an identification key. After remaining the main characteristics of wood and wood transformation, the equipment used for this application is presented. Each product is associated with a signal given by a microwaves sensor. This signal is reduced to a codesignature by the mean of a structural method for pattern recognition. Therefore identification results then in the matching of the signature of a new piece of wood and a signature contained in a database. The Wagner and Fisher chain distance algorithm has been implemented to perform the codes matching. Among a test set of 50 pieces, a rate of recognition of94% has been obtained. Copyright ©2001 IFAC. Keywords: Traceability, wood products, microwaves, pattern recognition, chain distance algorithm I.
INTRODUCTION
Log
The new trend in our consumer society is an understanding of a product's life from its origin to end-use via certain manufacturing steps.
I
From this end-user pre-occupation was born the concept of traceability and as a consequence. total control of manufacturing processes. Traceability is defined in the standard as "the ability to trace the history. the use or location of an entity by use of recorded identification" . In practice, it is organized around the interaction between process and product. so as to be able to convey production information about the product and to answer questions, at any time of the process, such as: • • • •
Cutting
i f £1L7 t j
i
Rip sawing
J
, #
!
f
I
• I
1
. . l
Grading, Cross-cutting
§
Finished or semi~finihed products Fig. 1. Live-sawing example.
Which product is it ? What are its characteristics and history ? Where is it ? What is its future in the process?
Firstly. the flow is divergent. the amount of products extracted from the raw input may be large according to the cutting pattern. The operator performs this primary breakdown according to criteria (size. quality. equipment) aimed at a precise objective in the global production objective. Secondly. input and output order is not respected. That means that further transformations are not following this first decision . Finally without any product tracking, there is no link between local objectives.
1.1 Wood industry problematic In this paper. the area of application is the transformation of wood. When focusing on the transformation from logs to semi-finished products (Fig. I). two constraints appear. 249
A lot of methods and techniques are used to perfonn automatic identification in the manufacturing systems (Grolee, 1996). Some of them involve adding tags to each piece. In many sectors of the wood industry (sawmills, furniture making, carpentry) there is no systematic answer to the problem of following up individual pieces. Indeed, the classical solutions are not so easy to implement because of surface roughness and also because of the types of operations required. Dupuy and Vlosky (2000) have retained the fact that traceability of products is encountered in the wood industry : in inter-company relations, more for a batch of products than individual ones, for finished or semi-finished products outside the production workshop.
For dried wood, dielectric properties vary mainly according to : • the presence of singularities, • the density, • and the slope of grain (macroscopic fibers arrangement). The principle of the uniqueness of the properties of a piece of wood is based upon a combination of all the fore-mentioned properties, both at a local level and when the item is considered as a whole. The working hypothesi s is to consider that these properties are sufficient to identify a piece, that is to say that they will distinguish it from any other. This can be stated in a different manner by saying thi s set of properties constitutes the "identity card" of any piece of wood. Let note {Pi." Pi.2, ... Pi.n } the measurable n properties of item i, and {Pj. " Pp, .. . , Pj .n } the measurable n properties of item j . The hypothesis is that : if items i and j are two different items, there is at least one characteristic k in [1 , n] such that Pi.k<>Pj .k.
1.2 Variability of wood Wood is an heterogeneous and anisotropic material. An illustration of the large variability may be the key constraints to be followed in the grading rules of products. They concern the presence of singularities (knots, pitch pockets) and also the wane, the density, the stiffness, etc ... . These vary from one piece to another, even if they are produced from neighbouring logs, and even if produced from the same saw cut (Walker, 1992). As a guideline, knots may be defined by four fields: • fonn (round, oval, flat ... ), • type (sound, rot, ... ), • size and position (diameter, ... ), • colour (clear, black, .. .).
In this approach the signature is borne directly and intrinsically by the piece. However, the characteristics of the pieces and hence their signature, may change over time. Moisture content, temperature or even vibrations during the measuring phase may all have an impact. Whereas some of them may be overcome, others need to be integrated into the model. Moreover, the morpho-dimensional modifications that the pieces undergo during the manufacturing phase also need to be taken into account. They will be discussed in the further work section.
Variations could be caused by the growing conditions of the tree and also by the cutting mode. One piece might posses a combination of multiple factors which differ from a neighbor's combination. The scope of this paper is to present a new approach to automatic identification of wood products by taking into account their huge variability.
2.
EXPERIMENTAL CONTEXT
The microwave sensor is composed of one source emitting at 10 GHz, with a power of 100 mW and of a line of sixteen receivers (diodes sensitive to the millimetric domain). Transducers are placed on each side of the wood piece, as shown in Fig. 2.
1.3 Concept developed Therefore, the concept is to consider that a product carries its own identification key. This kind of builtin code is encountered for the identification of natural and variable entity or set of entity, as could be human people with their genetic code.
The presence and the length of the piece are detected by two photoelectric cells and an encoder. The acquisition step has been fixed to 7 mm. This measurement step presents a compromise between the resolution and the amount of points for the treatment.
Here, the acquisition system is based on a microwave sensor, dedicated to the detection of knots and the estimation of the mechanical properties of the wood (Choffel, 1999). Microwaves interact with materials by way of dielectric properties (Torgovnikov, 1992).
250
3.
Photoelectic cells Direction of transfer
3. 1 Identification stages Fig. 5 presents the stages which are necessary to perform the identification. Identification has to give the decision if a piece has already been processed or not. In functioning, the system makes also use of a database in which a number of products has already been recorded.
/
' llb
Conveyor I
IDENTIFICATION METHODE
I
;1 '
Signal acquisition Fig. 2. Microwave test bed.
Feature extraction
The amplitude of the signal (Fig. 3) is a numerical value relevant of the voltage appearing on the microwaves diodes. These diodes are measuring the power of the wave after transmission through the wood. The disturbances at the start and end of the signal correspond to the effects created by the changes in air-wood and wood-air interfaces. The beginning and end of the piece are located by the first and final minima of the signal. The knots also create significant disturbances (hat-type) which can vary with diameter, direction and the nature of the knot (healthy, rotten, etc). Signal amplitude
begining
Signature writing No matching
Discriminant features matching ?
Matching
r--~--",
signatures matching ?
Storage of the new piece charac.
Matching
Reading of related information
Fig. 5. Stages towards the identification Hat-type variations
One signal per piece being collected, it should consists in the comparison of two signals. The amount of points and the amount of piece liable to be in the process make impossible the calculation with the whole points. Pretreatment of pattern recognition plays the role of data reduction.
Piece length end
Fig. 3. Example of a rough microwave signal.
3.2 Features extraction
Experimentation has been carried out on 50 maritime of products was pine pieces. Size 1500 * 120 * 27 mm. The moisture content has been set at 14% in this study.
The next stage is therefore the extraction of features of two kind : - Preselection features with which the matching is unnecessary. Three values have been retained : • the length of the piece, that is the number of points of the signal, • the average of the signal, relevant to the global density of the piece, • the number of knots disturbances.
Among the sixteen receivers, the receiver centred on the longitudinal axis of the piece has been retained for the analysis (Fig. 4).
+-_Q~-::3i~o:r-'-1
- The signature itself, that is the coding of the disturbances. Disturbances are detected when presenting values beyond certain thresholds (Fig. 6), a important slope and covering a certain number of points. The thresholds values are calculated from the average value of the signal minus ou plus a margin. All these factors are the parameters of the algorithm, they have been fixed
Analylis line Fig. 4. Acquisition path centred on the piece.
251
The signal (Fig. 8) represented with X is a chain called x: x = 7 1 6 7.
for the current batch of pieces with an experiment plan by Komarczuk (1999). Average value of the signal
,
!
1 1I
i
/ 1
i i
'
//Thresholds
i
L
V
i ll 11
il!
----l- V~~\ ,__ _,_:_ ~.l.!L i
Readinf! direction
Disturbances to be coded
Fig. 8. Knot area coding with 8-characters alphabet. Fig. 6. Detection of disturbances The aim of the three filters is to avoid the comparison of two pieces which are apparently different, and to evaluate only the similarity between signatures sharing the same preselection features values.
3.4 Signature matching
The chain distance algorithm proposed by Wagner and Fisher has been implemented (Bunke. 1993). It is based on the calculation of the amount of transformations necessary to make one chain look similar another one. Let denote : E. the empty string. a and b. two characters of the alphabet X. x and y, two chains of characters. Ixl. the length ofx.
3.3 Signature writing
A structural approach has been used to define the intrinsic characteristics of the signal in the knot areas. The advantage compared to statistical approach for pattern recognition is to take into account the type signal and particularly the presence of peaks and holes due to the presence of knots and slope of grain variations. The principle presented here is to consider that a knot area is a succession of segments. From a geometrical point of view, each segment may be described by the angle between its slope and the horizontal axis. The Freeman code fits to this coding. It has been adapted according to two aspects. Firstly, the scale of the signal is time, therefore only a half-plan of the Freeman code is used (Fig. 7).
Three elementary transformations are defined in order to transform x into y : insertion of 'a' in y : E->a, deletion of 'a' in x : a->E. substitution of ,a' in x by 'b' in y. a;t:b : a->b. A cost value of transformation is calculated, it is the similarity indicator D. D is given by the weighted sum of the elementary operations: D = a.nb_subst. + ~.nb_de1. + y. nb_ins, where a. ~. and y are weights. Usually, a =2 and ~=y=I. one substitution being equivalent. in most of the cases. to an insertion more a deletion. In this study, D has been adapted to take into account the length of the strings to match, and the importance of the substitutions. Indeed, it allows the distinction between the substitutions : 2-> 1 and 7-> 1, 2 and 7 are not nearby sectors. This cost is noted c(a~b) : c(a~b) = Ira-rbl. where ra and rb are respectively the ranks of a and b in X. Therefore, D= a.l:c(a~b) + ~.nb_de1. + y. nb_ins. + b.(lx l-lyl) with a=4, ~=y=2 and &=1 . Weights have been chosen to retain the balance of a. ~ and y. and to include the weight for the difference of length chains. The more the value of D is important D, the more the probability that pieces are the same is weak.
Fig. 7. Half-plan : example with 8 divisions Freeman encoding. Secondly, the number of divisions has been adjusted to 40 to get a more relevant description of the shapes, thus to help to distinguish knots among others. The structural description of the signal is based on the use of elementary items contained in an alphabet. The concatenation of elements gives a sentence or a string (chain of characters). On the example of the half-plan fore-mentioned, the alphabet is constituted by 8 characters. A character is corresponding to one of the sections of the Freemancode. Let X be this alphabet: X={O, 1,2, 3, 4, 5, 6. 7} .
252
Table 1. Results for the 1st x into 1sI passage 0 2nd passage 18 3'd passage 17 4th passage 15
When a piece presents more than one knot, the chains associated to each knots are concatenated. This choice of a global matching has been made to reduce the duration of the calculation. Chain distance is evaluated between a reference chain recorded in the database of the system and an unknown chain associated to a new piece. The automatic identification of one piece, induces the reading of the whole database. The recognition is successful when the chain distance 0 is included between zero and a sirrularity threshold.
Identification begins with the matching of the preselection features. If the results is positive, the signature will be matched to the stored signature in order to find, if it exists, the signature that is most similar to it. In the positive case, an access is given to the database in order to read the information related to the current piece. On the negative case, when useful, the new set of features will be stored into the database as well as related process information.
EXPERIMENTATION
Table 2. Results for the test set. REF 0 REF 0
LI L2 L3 L4 L5 L6 L7 L8 L9 LlO Lll Ll2 LI3 LI4 LIS LI6 Ll7
Amplitude
I" passage 3rd passage - 2 nd passage - ' 4 Ih passage
-
1000
800 -
600 400 200 •.
--- - -- - - - - - - - - - -
o
300
600
900
1200
1300
4th 15 13
14 0
The identification principle has been applied to a batch of 50 pieces (referenced L 1 to L50). A set of learning has been constituted for the writing of the database signatures. A second passage of these pieces allowed the writing of the new signature and the matching with the signatures of the first set. For the learning set, the whole pieces have been recognised successfully with a similarity indicator of zero. The result of the test set are summarised in table 2.
First experimentation has been performed to test the identification of a same product. It allows to test the repeatability of the microwave measurements face to the pattern recognition. A piece has been scanned four times, the signals are presented on Fig. 9. For this test the direction of transfer of the piece on the conveyor has been maintained.
1600 1400 1200
3'd 17 9 0 14
4.2 Set of 50 different pieces
4.1 Repeatability of the identification
1800
2nd 18 0 9 15
The values are the edition costs to convert the chains issued from the signal of the row into the chains issued from the signal of the column. A score of zero is obtained with the matching of two identical signatures. For various passages, the similarity indicator is increasing for two reasons. Firstly, some disturbances close to the thresholds may exceed them and engender a different detection. Secondly, the variation of the microwaves signal is weak. But from the recognition point of view, it may change the coding of the slopes of the segments.
3.5 Decision Making
4.
same piece.
1500
Length of the piece (mm)
Fig. 9. Four acquisitions on the same piece. The superimposition of the signal is quite perfect. The main changes in amplitude appear at the beginning and the end. They are corresponding to the change of contact between the piece of wood and the parts of the conveyor. The results of the chain distance calculation are presented in the table 1.
12 2nd 1 11 7 12 27 25 11 2 34 29 20 26 32 46 0
LI8 LI9 L20 L21 L22 L23 L24 L25 L26 L27 L28 L29 L30 L31 L32 L33 L34
17 11 32 18 9 26 6 4th 3 12 1 17 10 30 20 8 9
REF
L35 L36 L37 L38 L39 L40 L41 L42 L43 L44 L45 L46 L47 L48 L49 L50
0
2n 18 11 0 26 26 0 11
18 60 7 8 20 30 10
19
The length of these pieces were the same, thus discrimination is based on the difference of the average value of the signal and the number of disturbances. The reference of the piece is given in the first column_The second one is filled - with the similarity indicator 0 when the recognition was successful, that
253
is the piece owns the weakest indicator - or the rank of the piece when the recognition was not successful. 94 % of piece have been recognised. The average value of the similarity indicator is 15. Among the three unsuccessful pieces, two are at the second place.
5.
The objective of this paper is to show the first work about the possibility of taking benefit of the heterogeneity of wood pieces for their automatic identification in a global context of traceability. A microwaves sensor is the instrument measure because of its sensibility to the internal properties of wood. As an obliged stage to the application of this principle, a signal processing has been performed, in particular a structural pattern recognition approach has been led. The results have been given a recognition percentage of 94 % over a set of 50 pieces. At this point, two continuation ways should be followed. Firstly, the identification, whatever is the method used, it is bounded to the performance of the NDT -technique face to face of the products transformation. In particular, the variations of the signal should be tested according to the moisture content of the pieces. Secondly, the algorithm of recognition should be refined to give a more efficient recognition rate as required for identification systems. What is more, the amount of recognition parameters should be reduced to improve the portability of the identification for other wooden types and transformation processes.
4.3 Thickness variation As an introduction to the transformations that the pieces are liable to have, the test of a piece with a reduction of its thickness has been performed. On Fig. 10 are plotted the signals issued from the four acquisitions. These acquisitions have been made with the same direction of transfer of the piece. Amplitude ISOO
th. = 20 mm - t h. = 24 mm
1600 1400
-th. = 22 mm th. = 27 mm
.,.
1200 · 1000
SOO 600 400 200 . - . -
o
CONCLUSION
-.----.. --- .. - . - - - . - - --.- - - -900 300 600 1200 ISOO
References
Length of the pieces (mm)
Fig. 10. Reduction of thickness.
Bunke, H. (1993) Structural and syntactic pattern recogmtlOn. In: Handbook of pattern recognition and computer vision Ed. Chen, C. World Scientific, London. Charpentier, P. and Choffel D. (1999). Identification technique for ligneous materials : industrial processes and uses involving the identification technique. French. National patent n° 99 11040. Choffel, D. (1999) Automation of timber mechanical grading, Coupling of vision and microwaves, SPIE-Machine Vision Systems for Inspection and Metrology VIII - Boston, USA. Dupuy, c., Vlosky, R.P. (2000). Status of Electronic Data Interchange in the products forest Industry. Forest Products Journal. 50,32-38. Grolee, M. (1996). The Automatic identification: main techniques and existing normalizations In French. Travail et Methodes, 524, 31-36. Komarczuk, L. (1999). Application of a pattern recognition method for wood products tracking. In French. Wood Science Master Degree. University H. Poincare of Nancy. Torgovnikov, G.I. (1992). Dielectric properties of wood and wood-based materials, Springer Verlag, New York. Walker, J.C.F. (1992). Primary wood processing. principles and practice. Chapman & Hall, London.
Visually the signals are similar, the average values differ only. The disturbances due to the knots are changing by the amplitude but not by the pattern. The interaction with the identification process is the change in the knots detection. The pattern may be not beyond the thresholds at the position, from one signal to the other one after the planning operation. The identification has been performed of course by removing the parameter of preselection of the average values. The scores presented in table 3 are the costs of the chain distance edition. Table 3. Results for the reduction of thickness. x into 20 mm 22 mm 24 mm 27 mm Th. 20 mm 0 7 22 15 Th. 22 mm 7 0 7 22 Th. 24 mm 22 7 0 1 Th. 27 mm 17 22 0 For two consecutive thickness the cost is lower than for two or three steps. It let foresee that, for transformations which are not altering the main features of the signal, the product identification is still possible. It concerns, for instance, the modification in length.
254
IFAC
c
0
[>
Publications www.elsevier.comilocate/ifac
TRACEABILITY OF WOOD PRODl"CTS: A STRlCTl'RAL APPROACH FOR PA TTER~
RECOG~ITIO~
Denise Choffel and Patrick Charpentier
CRAN - Centre of Research ofAutomatic Control of Nancy Faculty of Sciences B.P. 239 - 54506 Vandoeuvre-Ies-Nanc), Abstract: This paper takes places in the global problematic of traceability which is a large preoccupation in these recent years . The need of tracking and tracing products over their life-cycle generates the development of methods and techniques to perform the automatic identification. For usual manufacturing systems, electronic tags and bar-codes are successfully employed. However, in the wood industry, the nature of the material itself, makes difficult the identification of products. The idea presented is to use the property of heterogeneity of wood as an identification key. After remaining the main characteristics of wood and wood transformation, the equipment used for this application is presented. Each product is associated with a signal given by a microwaves sensor. This signal is reduced to a codesignature by the mean of a structural method for pattern recognition. Therefore identification results then in the matching of the signature of a new piece of wood and a signature contained in a database. The Wagner and Fisher chain distance algorithm has been implemented to perform the codes matching. Among a test set of 50 pieces, a rate of recognition of94% has been obtained. Copyright ©2001 IFAC. Keywords: Traceability, wood products, microwaves, pattern recognition, chain distance algorithm I.
INTRODUCTION
Log
The new trend in our consumer society is an understanding of a product's life from its origin to end-use via certain manufacturing steps.
I
From this end-user pre-occupation was born the concept of traceability and as a consequence. total control of manufacturing processes. Traceability is defined in the standard as "the ability to trace the history. the use or location of an entity by use of recorded identification" . In practice, it is organized around the interaction between process and product. so as to be able to convey production information about the product and to answer questions, at any time of the process, such as: • • • •
Cutting
i f £1L7 t j
i
Rip sawing
J
, #
!
f
I
• I
1
. . l
Grading, Cross-cutting
§
Finished or semi~finihed products Fig. 1. Live-sawing example.
Which product is it ? What are its characteristics and history ? Where is it ? What is its future in the process?
Firstly. the flow is divergent. the amount of products extracted from the raw input may be large according to the cutting pattern. The operator performs this primary breakdown according to criteria (size. quality. equipment) aimed at a precise objective in the global production objective. Secondly. input and output order is not respected. That means that further transformations are not following this first decision . Finally without any product tracking, there is no link between local objectives.
1.1 Wood industry problematic In this paper. the area of application is the transformation of wood. When focusing on the transformation from logs to semi-finished products (Fig. I). two constraints appear. 249
A lot of methods and techniques are used to perfonn automatic identification in the manufacturing systems (Grolee, 1996). Some of them involve adding tags to each piece. In many sectors of the wood industry (sawmills, furniture making, carpentry) there is no systematic answer to the problem of following up individual pieces. Indeed, the classical solutions are not so easy to implement because of surface roughness and also because of the types of operations required. Dupuy and Vlosky (2000) have retained the fact that traceability of products is encountered in the wood industry : in inter-company relations, more for a batch of products than individual ones, for finished or semi-finished products outside the production workshop.
For dried wood, dielectric properties vary mainly according to : • the presence of singularities, • the density, • and the slope of grain (macroscopic fibers arrangement). The principle of the uniqueness of the properties of a piece of wood is based upon a combination of all the fore-mentioned properties, both at a local level and when the item is considered as a whole. The working hypothesi s is to consider that these properties are sufficient to identify a piece, that is to say that they will distinguish it from any other. This can be stated in a different manner by saying thi s set of properties constitutes the "identity card" of any piece of wood. Let note {Pi." Pi.2, ... Pi.n } the measurable n properties of item i, and {Pj. " Pp, .. . , Pj .n } the measurable n properties of item j . The hypothesis is that : if items i and j are two different items, there is at least one characteristic k in [1 , n] such that Pi.k<>Pj .k.
1.2 Variability of wood Wood is an heterogeneous and anisotropic material. An illustration of the large variability may be the key constraints to be followed in the grading rules of products. They concern the presence of singularities (knots, pitch pockets) and also the wane, the density, the stiffness, etc ... . These vary from one piece to another, even if they are produced from neighbouring logs, and even if produced from the same saw cut (Walker, 1992). As a guideline, knots may be defined by four fields: • fonn (round, oval, flat ... ), • type (sound, rot, ... ), • size and position (diameter, ... ), • colour (clear, black, .. .).
In this approach the signature is borne directly and intrinsically by the piece. However, the characteristics of the pieces and hence their signature, may change over time. Moisture content, temperature or even vibrations during the measuring phase may all have an impact. Whereas some of them may be overcome, others need to be integrated into the model. Moreover, the morpho-dimensional modifications that the pieces undergo during the manufacturing phase also need to be taken into account. They will be discussed in the further work section.
Variations could be caused by the growing conditions of the tree and also by the cutting mode. One piece might posses a combination of multiple factors which differ from a neighbor's combination. The scope of this paper is to present a new approach to automatic identification of wood products by taking into account their huge variability.
2.
EXPERIMENTAL CONTEXT
The microwave sensor is composed of one source emitting at 10 GHz, with a power of 100 mW and of a line of sixteen receivers (diodes sensitive to the millimetric domain). Transducers are placed on each side of the wood piece, as shown in Fig. 2.
1.3 Concept developed Therefore, the concept is to consider that a product carries its own identification key. This kind of builtin code is encountered for the identification of natural and variable entity or set of entity, as could be human people with their genetic code.
The presence and the length of the piece are detected by two photoelectric cells and an encoder. The acquisition step has been fixed to 7 mm. This measurement step presents a compromise between the resolution and the amount of points for the treatment.
Here, the acquisition system is based on a microwave sensor, dedicated to the detection of knots and the estimation of the mechanical properties of the wood (Choffel, 1999). Microwaves interact with materials by way of dielectric properties (Torgovnikov, 1992).
250
3.
Photoelectic cells Direction of transfer
3. 1 Identification stages Fig. 5 presents the stages which are necessary to perform the identification. Identification has to give the decision if a piece has already been processed or not. In functioning, the system makes also use of a database in which a number of products has already been recorded.
/
' llb
Conveyor I
IDENTIFICATION METHODE
I
;1 '
Signal acquisition Fig. 2. Microwave test bed.
Feature extraction
The amplitude of the signal (Fig. 3) is a numerical value relevant of the voltage appearing on the microwaves diodes. These diodes are measuring the power of the wave after transmission through the wood. The disturbances at the start and end of the signal correspond to the effects created by the changes in air-wood and wood-air interfaces. The beginning and end of the piece are located by the first and final minima of the signal. The knots also create significant disturbances (hat-type) which can vary with diameter, direction and the nature of the knot (healthy, rotten, etc). Signal amplitude
begining
Signature writing No matching
Discriminant features matching ?
Matching
r--~--",
signatures matching ?
Storage of the new piece charac.
Matching
Reading of related information
Fig. 5. Stages towards the identification Hat-type variations
One signal per piece being collected, it should consists in the comparison of two signals. The amount of points and the amount of piece liable to be in the process make impossible the calculation with the whole points. Pretreatment of pattern recognition plays the role of data reduction.
Piece length end
Fig. 3. Example of a rough microwave signal.
3.2 Features extraction
Experimentation has been carried out on 50 maritime of products was pine pieces. Size 1500 * 120 * 27 mm. The moisture content has been set at 14% in this study.
The next stage is therefore the extraction of features of two kind : - Preselection features with which the matching is unnecessary. Three values have been retained : • the length of the piece, that is the number of points of the signal, • the average of the signal, relevant to the global density of the piece, • the number of knots disturbances.
Among the sixteen receivers, the receiver centred on the longitudinal axis of the piece has been retained for the analysis (Fig. 4).
+-_Q~-::3i~o:r-'-1
- The signature itself, that is the coding of the disturbances. Disturbances are detected when presenting values beyond certain thresholds (Fig. 6), a important slope and covering a certain number of points. The thresholds values are calculated from the average value of the signal minus ou plus a margin. All these factors are the parameters of the algorithm, they have been fixed
Analylis line Fig. 4. Acquisition path centred on the piece.
251
The signal (Fig. 8) represented with X is a chain called x: x = 7 1 6 7.
for the current batch of pieces with an experiment plan by Komarczuk (1999). Average value of the signal
,
!
1 1I
i
/ 1
i i
'
//Thresholds
i
L
V
i ll 11
il!
----l- V~~\ ,__ _,_:_ ~.l.!L i
Readinf! direction
Disturbances to be coded
Fig. 8. Knot area coding with 8-characters alphabet. Fig. 6. Detection of disturbances The aim of the three filters is to avoid the comparison of two pieces which are apparently different, and to evaluate only the similarity between signatures sharing the same preselection features values.
3.4 Signature matching
The chain distance algorithm proposed by Wagner and Fisher has been implemented (Bunke. 1993). It is based on the calculation of the amount of transformations necessary to make one chain look similar another one. Let denote : E. the empty string. a and b. two characters of the alphabet X. x and y, two chains of characters. Ixl. the length ofx.
3.3 Signature writing
A structural approach has been used to define the intrinsic characteristics of the signal in the knot areas. The advantage compared to statistical approach for pattern recognition is to take into account the type signal and particularly the presence of peaks and holes due to the presence of knots and slope of grain variations. The principle presented here is to consider that a knot area is a succession of segments. From a geometrical point of view, each segment may be described by the angle between its slope and the horizontal axis. The Freeman code fits to this coding. It has been adapted according to two aspects. Firstly, the scale of the signal is time, therefore only a half-plan of the Freeman code is used (Fig. 7).
Three elementary transformations are defined in order to transform x into y : insertion of 'a' in y : E->a, deletion of 'a' in x : a->E. substitution of ,a' in x by 'b' in y. a;t:b : a->b. A cost value of transformation is calculated, it is the similarity indicator D. D is given by the weighted sum of the elementary operations: D = a.nb_subst. + ~.nb_de1. + y. nb_ins, where a. ~. and y are weights. Usually, a =2 and ~=y=I. one substitution being equivalent. in most of the cases. to an insertion more a deletion. In this study, D has been adapted to take into account the length of the strings to match, and the importance of the substitutions. Indeed, it allows the distinction between the substitutions : 2-> 1 and 7-> 1, 2 and 7 are not nearby sectors. This cost is noted c(a~b) : c(a~b) = Ira-rbl. where ra and rb are respectively the ranks of a and b in X. Therefore, D= a.l:c(a~b) + ~.nb_de1. + y. nb_ins. + b.(lx l-lyl) with a=4, ~=y=2 and &=1 . Weights have been chosen to retain the balance of a. ~ and y. and to include the weight for the difference of length chains. The more the value of D is important D, the more the probability that pieces are the same is weak.
Fig. 7. Half-plan : example with 8 divisions Freeman encoding. Secondly, the number of divisions has been adjusted to 40 to get a more relevant description of the shapes, thus to help to distinguish knots among others. The structural description of the signal is based on the use of elementary items contained in an alphabet. The concatenation of elements gives a sentence or a string (chain of characters). On the example of the half-plan fore-mentioned, the alphabet is constituted by 8 characters. A character is corresponding to one of the sections of the Freemancode. Let X be this alphabet: X={O, 1,2, 3, 4, 5, 6. 7} .
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Table 1. Results for the 1st x into 1sI passage 0 2nd passage 18 3'd passage 17 4th passage 15
When a piece presents more than one knot, the chains associated to each knots are concatenated. This choice of a global matching has been made to reduce the duration of the calculation. Chain distance is evaluated between a reference chain recorded in the database of the system and an unknown chain associated to a new piece. The automatic identification of one piece, induces the reading of the whole database. The recognition is successful when the chain distance 0 is included between zero and a sirrularity threshold.
Identification begins with the matching of the preselection features. If the results is positive, the signature will be matched to the stored signature in order to find, if it exists, the signature that is most similar to it. In the positive case, an access is given to the database in order to read the information related to the current piece. On the negative case, when useful, the new set of features will be stored into the database as well as related process information.
EXPERIMENTATION
Table 2. Results for the test set. REF 0 REF 0
LI L2 L3 L4 L5 L6 L7 L8 L9 LlO Lll Ll2 LI3 LI4 LIS LI6 Ll7
Amplitude
I" passage 3rd passage - 2 nd passage - ' 4 Ih passage
-
1000
800 -
600 400 200 •.
--- - -- - - - - - - - - - -
o
300
600
900
1200
1300
4th 15 13
14 0
The identification principle has been applied to a batch of 50 pieces (referenced L 1 to L50). A set of learning has been constituted for the writing of the database signatures. A second passage of these pieces allowed the writing of the new signature and the matching with the signatures of the first set. For the learning set, the whole pieces have been recognised successfully with a similarity indicator of zero. The result of the test set are summarised in table 2.
First experimentation has been performed to test the identification of a same product. It allows to test the repeatability of the microwave measurements face to the pattern recognition. A piece has been scanned four times, the signals are presented on Fig. 9. For this test the direction of transfer of the piece on the conveyor has been maintained.
1600 1400 1200
3'd 17 9 0 14
4.2 Set of 50 different pieces
4.1 Repeatability of the identification
1800
2nd 18 0 9 15
The values are the edition costs to convert the chains issued from the signal of the row into the chains issued from the signal of the column. A score of zero is obtained with the matching of two identical signatures. For various passages, the similarity indicator is increasing for two reasons. Firstly, some disturbances close to the thresholds may exceed them and engender a different detection. Secondly, the variation of the microwaves signal is weak. But from the recognition point of view, it may change the coding of the slopes of the segments.
3.5 Decision Making
4.
same piece.
1500
Length of the piece (mm)
Fig. 9. Four acquisitions on the same piece. The superimposition of the signal is quite perfect. The main changes in amplitude appear at the beginning and the end. They are corresponding to the change of contact between the piece of wood and the parts of the conveyor. The results of the chain distance calculation are presented in the table 1.
12 2nd 1 11 7 12 27 25 11 2 34 29 20 26 32 46 0
LI8 LI9 L20 L21 L22 L23 L24 L25 L26 L27 L28 L29 L30 L31 L32 L33 L34
17 11 32 18 9 26 6 4th 3 12 1 17 10 30 20 8 9
REF
L35 L36 L37 L38 L39 L40 L41 L42 L43 L44 L45 L46 L47 L48 L49 L50
0
2n 18 11 0 26 26 0 11
18 60 7 8 20 30 10
19
The length of these pieces were the same, thus discrimination is based on the difference of the average value of the signal and the number of disturbances. The reference of the piece is given in the first column_The second one is filled - with the similarity indicator 0 when the recognition was successful, that
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is the piece owns the weakest indicator - or the rank of the piece when the recognition was not successful. 94 % of piece have been recognised. The average value of the similarity indicator is 15. Among the three unsuccessful pieces, two are at the second place.
5.
The objective of this paper is to show the first work about the possibility of taking benefit of the heterogeneity of wood pieces for their automatic identification in a global context of traceability. A microwaves sensor is the instrument measure because of its sensibility to the internal properties of wood. As an obliged stage to the application of this principle, a signal processing has been performed, in particular a structural pattern recognition approach has been led. The results have been given a recognition percentage of 94 % over a set of 50 pieces. At this point, two continuation ways should be followed. Firstly, the identification, whatever is the method used, it is bounded to the performance of the NDT -technique face to face of the products transformation. In particular, the variations of the signal should be tested according to the moisture content of the pieces. Secondly, the algorithm of recognition should be refined to give a more efficient recognition rate as required for identification systems. What is more, the amount of recognition parameters should be reduced to improve the portability of the identification for other wooden types and transformation processes.
4.3 Thickness variation As an introduction to the transformations that the pieces are liable to have, the test of a piece with a reduction of its thickness has been performed. On Fig. 10 are plotted the signals issued from the four acquisitions. These acquisitions have been made with the same direction of transfer of the piece. Amplitude ISOO
th. = 20 mm - t h. = 24 mm
1600 1400
-th. = 22 mm th. = 27 mm
.,.
1200 · 1000
SOO 600 400 200 . - . -
o
CONCLUSION
-.----.. --- .. - . - - - . - - --.- - - -900 300 600 1200 ISOO
References
Length of the pieces (mm)
Fig. 10. Reduction of thickness.
Bunke, H. (1993) Structural and syntactic pattern recogmtlOn. In: Handbook of pattern recognition and computer vision Ed. Chen, C. World Scientific, London. Charpentier, P. and Choffel D. (1999). Identification technique for ligneous materials : industrial processes and uses involving the identification technique. French. National patent n° 99 11040. Choffel, D. (1999) Automation of timber mechanical grading, Coupling of vision and microwaves, SPIE-Machine Vision Systems for Inspection and Metrology VIII - Boston, USA. Dupuy, c., Vlosky, R.P. (2000). Status of Electronic Data Interchange in the products forest Industry. Forest Products Journal. 50,32-38. Grolee, M. (1996). The Automatic identification: main techniques and existing normalizations In French. Travail et Methodes, 524, 31-36. Komarczuk, L. (1999). Application of a pattern recognition method for wood products tracking. In French. Wood Science Master Degree. University H. Poincare of Nancy. Torgovnikov, G.I. (1992). Dielectric properties of wood and wood-based materials, Springer Verlag, New York. Walker, J.C.F. (1992). Primary wood processing. principles and practice. Chapman & Hall, London.
Visually the signals are similar, the average values differ only. The disturbances due to the knots are changing by the amplitude but not by the pattern. The interaction with the identification process is the change in the knots detection. The pattern may be not beyond the thresholds at the position, from one signal to the other one after the planning operation. The identification has been performed of course by removing the parameter of preselection of the average values. The scores presented in table 3 are the costs of the chain distance edition. Table 3. Results for the reduction of thickness. x into 20 mm 22 mm 24 mm 27 mm Th. 20 mm 0 7 22 15 Th. 22 mm 7 0 7 22 Th. 24 mm 22 7 0 1 Th. 27 mm 17 22 0 For two consecutive thickness the cost is lower than for two or three steps. It let foresee that, for transformations which are not altering the main features of the signal, the product identification is still possible. It concerns, for instance, the modification in length.
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