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Computers and Electronics in Agriculture 75 (2011) 147158

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Computers and Electronics in Agriculture

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Original paper

Suppor inbiscuit

S. Nashata School of Elec 0 Penab Faculty of Info gor, Mc Department o kok, Th

a r t i c l

Article history:Received 10 AReceived in reAccepted 17 O

Keywords:BiscuitDiscriminant aMachine visionMulti-core processorImage segmentationSupport vector machine

ctiond onour dcurac-stepsicaproc

rrecting biscuits at 9m/min. It was discovered that touching and non-touching biscuits did not signicantlyinterfere with accurate assessment of baking. However, image processing of touching biscuits was con-siderably slower compared to non-touching biscuits, averaging at 36.3ms and 9.0ms, respectively. Thedecrease in speedwas due to the complexity of thewatershed-based algorithmused to segment touchingbiscuits. This image computing platform can potentially support the requirements of the high-volume

1. Introdu

Thebaketant sectorsin this induproducedbquality evaformed rouneed to beas colour inguide as bisis optimisedbrand of biduring a shbaking procformations(Moore, 199role in the bucts. Commout by hum

CorresponE-mail add

0168-1699/$ doi:10.1016/j.biscuit production. 2010 Elsevier B.V. All rights reserved.

ction

ry sector canbe considered tobeoneof themost impor-in food industry. Real-time inspection is very desirablestrial sector since food products like biscuits are beingymillions eachday. Likeothermanufacturingprocesses,luation and sorting are two essential operations per-tinely in biscuit production. Among the many tests thatcarried out on biscuits is the measurement of colourdicates quality and defect. Colour is also an importantcuits will appear more appetising when its appearance. Consumers expect to nd a constant colour for a samescuit. Obtaining identical biscuits are difcult evenort baking period. This is due to the complexity of theess where biochemical reactions and physical trans-give rise to biscuits with different shades of colours1). Therefore biscuit colour inspectionoccupies amajoriscuitmanufacturing, from rawdough to nished prod-only, in this industry, quality evaluation is being carriedan vision of some trained inspectors who make subjec-

ding author. Tel.: +60 4 5996001; fax: +60 4 5941023.ress: [email protected] (M.Z. Abdullah).

tive quality judgement as to the product status. This decision ishighly variable, and the process is tedious, labour-intensive, andsubjective.

In recent years, automated machine inspection systems havebeen implemented inbakery for colour inspection. Theuseof robotsin food processing has many advantages over their human coun-terpart in terms of economy and performance (Abdullah, 2008).Automated biscuit inspection would help to standardise the qual-ity assessment of biscuit during the baking process. Although thedevelopment of automatic visual inspection for bakery and agri-cultural products is steadily progressing in the last decade, mostmachine vision systems are facing the restriction of real-time con-straints and high computing volume for high-speed production.Imageprocessingofmosthigh-speedcommercial inspectors is gen-erally based on thresholding and pixel counting, so that they areeffective at distinguishing products with large contrast or colourdifferences. In the past there have been many developments onthe use of a digital imaging technique to inspect a single, isolatedand stationary agricultural products, such as the inspection of pota-toes and apples (Tao et al., 1995), oil palm fruits (Abdullah et al.,2002), rice and grains (Carter et al., 2006), citrus fruits (Blasco et al.,2007), corns (Chen et al., 2009), and beef fat (Chen et al., 2010).However, few of these developments are able to be implementedfor high-speed sorting of moving targets at an economical feasi-

see front matter 2010 Elsevier B.V. All rights reserved.compag.2010.10.010t vector machine approach to real-times on moving conveyor belta, A. Abdullahb, S. Aramvithc, M.Z. Abdullaha,

trical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 1430rmation Sciences and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi Selanf Electrical Engineering, Faculty of Engineering, Chulalongkorn University, 10330 Bang

e i n f o

pril 2010vised form 8 September 2010ctober 2010

nalysis

a b s t r a c t

An intelligent system for colour inspethe-art classication techniques baseused to classify biscuits into one of fand substantially over-baked. The acanalysis using both direct and multiafter Wilks was more precise in claswas achieved by means of multi-corealgorithms. The system resulted in co/ locate /compag

spection of

ng, Malaysiaalaysiaailand

of biscuit products is proposed. In this system, the state-of-Support Vector Machines (SVM) and Wilks analysis wereistinct groups: under-baked, moderately baked, over-baked,y of the system was compared with standard discriminantclassications. It was discovered that the radial basis SVMtion compared to other classiers. Real-time implementationessor with advanced multiple-buffering and multithreadingclassication rate of more than 96% for stationary and mov-

148 S. Nashat et al. / Computers and Electronics in Agriculture 75 (2011) 147158

Fig. 1. Examp ked, tright is substa

ble cost. Immachine vimicroprocefact that motasks like imof these tadictable daparadigm. Inition, mulyears to achinstance Zh30 frame/s fby parallelia multi-cordifferent im(2008) pres(SIMD) in thZhao et al. (lelism of im

Clearly fsolve somevarying imaof themultier for real-Both isolateproposed msamples on

2. Materia

2.1. Biscuit

Unlike iauthors kncuits existsdeterminedmost biscuion a moreever, this evariations.problemofinstance it ioverlappingtion in biscuin Fig. 1. In bgroups ree

overthatperapp

procour i

achin

har460

uippable,grabElece PCP caable

tion-mo

ex, whand

ationigh-ncy s. Thentrolor bovile of (a) non-touching and (b) touching biscuits. In both cases, top left is under-bantially over-baked.

age processing of a dynamically moving target requiression with high-data throughput and high-performancessors. This problem is further compounded due to thest practical applications require time consuming visionage transformation and extraction. Fortunately, most

sks have regular, repetitive computations with pre-ta dependencies, making them well suited for paralleln order to exploit data-level parallelism in object recog-ti-core processors are more and more used in recentieve real-time object recognition (Kim et al., 2010). Forang et al. (2008) revealed that real-time operation overor the scale invariant feature transformcan be achievedsing on amulti-core system; Ach et al. (2008) developede processors for real-time detection of trafc signs inages by means of the Viola-Jones algorithm; Kyo et al.ent massively parallel single instruction multiple datae image pre-processing stage of object recognition; and2009) exploited the thread-level and data-level paral-age segmentation technique based onOtsus algorithm.rom this review, multi-core technology is well suited topattern recognition problems and to processing time-ges. In this paper, we will present the in-depth analysis-core implementationof the SVM-basedarticial classi-time inspection of stationary aswell asmoving objects.d and partially occluded biscuits are investigated. Theethods and procedures are applied to inspect biscuita conveyor belt with adjustable speed.

ls and methods

baked,Fig. 1the temcuits toimagefor col

2.2. M

Thetion XWand eqtem, cframeMatroxinto thXC-003sustainresoludard Cvia 2-mstation45.5 cmtest stultra-hfreque85kHzsity coconveyulate mcolour grading

ndustrial and manufacturing sectors, to the best ofowledge, no single standard for colour grading of bis-. In this industry, the colour standard of biscuits isby the individual company. Colour quality control in

t producers involved human inspectorswhile some relyobjective measurement by using a colorimeter. How-quipment is not suitable when the sample has colourMoreover, there are many reasons for considering thetouching or partial recognition in biscuit processing. Fors not always possible to keep biscuits from touching oron a moving conveyor belt. Example of colour varia-its highlighting touched and untouched cases is shownoth cases, the biscuits are categorised into four distinctcting four degrees of baking: under-baked, moderately

inspectionFig. 2.

The propsteps: pre-processingimage procbrated usinUSA. The saSCS-YW-01standards racquisitionimage segmality reductand applyin

In this sMatrox Imaop right is moderately baked, bottom left is over-baked and bottom

-baked and substantially over-baked. It can be seen inthe colour is not uniformly distributed. This is due toature variation inside the oven, causing colours of bis-ear darker in some regions. Therefore, the challenge foressing software is to use this information as the basisnspection.

e vision system

dware used in this study consisted of a HP Worksta-0 with quad core CPU 2.5GHz processor, 2GB of RAMed with a colour frame grabber, an illumination sys-a charge-coupled device camera, and conveyor. Theber is of the 32-bit Meteor II type manufactured bytronic System Limited, Canada. This device is mountedI slot of the workstation. A high quality 3-CCD Sonymera was used as the image capturing device with aspeed of 25 frames per second, captured at a spatialof 640480 pixels. The camera comes with stan-unt type optical lens connected to a frame grabberternal BNC cable. The camera was mounted to a testich comprised of iron holder xed at the height ofat an angle of 90 mounted from horizontal arm. Thewas illuminated using a white, Stocker Yale (USA)

frequency uorescent ring light, model 13 plus highteady light with a maximum oscillating frequency oflight bulb was tted with a continuous light inten-

whichallowed10100% intensity adjustment. Standardelt with adjustable speed was used in this study to sim-ng object detection. The schematic diagram of biscuit

system showing all essential elements is depicted in

osed colour inspection system is divided into twomainprocessing and post-processing. In summary, the pre-step includes calibration of machine vision and theessing part. In this work the machine vision was cali-g four colour samples manufactured by Labsphere Inc.,mples are SCS-RD-010, SCS-GN-010, SCS-BL-010, and0, corresponding to red, green, blue, and yellow colourespectively. The image processing part involves imageand smoothing, RGB to HLS colour transformation, andentation. Thepost-processing step includesdimension-ion of the segmented objects by using Wilks analysis,g support vector machine to achieve classication.tudy the image processing was performed using thege Library (MIL) version MIL 9.0 with visual C++ pro-

S. Nashat et al. / Computers and Electronics in Agriculture 75 (2011) 147158 149

1. Under-baked 2. Moderately baked 3. Over-baked

trial Hkstatio

3-CCD

Iron holderBNC Cable

ision

gramming lon Matrox m

2.3. Colour

Colour isment usedtaken by theRGBcolourputer graphdevices, e.gno accuratetiveway is tvision to thespace was sperceptual uthat the huIn this spacattribute diand thus thversion proa single huefor easier cobit machineby Gonzalez

h ={

360

255360

or

h ={

cos1

Eqs. (1) andThe equatiotity, but Eqsof hardware

2.4. Image

To sepacombinatiotechniquesthe two peaand object

souras s

impoeakscon

nimuhan tost pted flcula

ng biiscuite sur,990d froas smeredhingans oproce (i)ical ethator (Sewa

age (Vers ththe oe a mnemume caorph

IndusWor

Illumination system

Camera

Biscuit samples

Industrial conveyor

Fig. 2. Schematic diagram of biscuit machine v

anguage incorporated. The parallel-processing is basedultiple-buffering and multithreading techniques.

space transformation

one of the most important features for biscuits assess-in this recognition system. The images of biscuits wereCCD camera and represented in the three-dimensional

space.Unfortunately, theRGBcolour spaceused in com-ics is device dependent, which is designed for specic. cathode-ray tube (CRT) display. Hence, RGB colour hasdenition for a human observer. Therefore, the effec-

o transformtheRGB information sensedby themachineHue-Lightness-Saturation (HLS) colour space. The HLS

elected since it denes colour not only in the sense ofniformity, butmore signicantly, itmatches to theway

man perceives colour (Camastra and Vinciarelli, 2008).e, only the hue component h was analysed, since thisrectly characterises the colour properties of an objecte degree of doneness of the biscuit. Hence, colour con-cess only involved transforming the RGB information tobuffer. Indirectly, this helped to compress informationlourdiscriminationandmanageable solution. For the8-vision, such a transformation is given mathematicallyand Wintz (1987):

cos1[

0.5[(R G) + (R B)](R G)2(R B)(G B)

]}

IfB G (1)

[ ]}

of thevalue wticallythese ptogramthemimore ttwo mseparathen catouchieach bthat thand 39touchearea wconsidsmootby meimagethey arphologimageoperatThen thent imconsidface. Ifproducwill dea minilabel ththat m0.5[(R G) + (R B)]

(R G)2(R B)(G B) 255

360IfB < G (2)

(2) yield normalised hue values in the interval [0, 255].n of hue can also be expressedusing trigonometry iden-. (1) and (2) are easier to visualise and superior in termsimplementation.

segmentation and object detection

rate an image of biscuit from the background, then of auto thresholding and watershed transformationwere proposed. The auto thresholding method locatesks in the histogram corresponding to the backgroundof the image (Otsu, 1979). In so doing, the histogram

remove artewere implenoise due topute the cewas appliedinterest. Thsequence o

2.5. Dimen

Similarhue vectorstoo large todesirable to4. Substantially over-baked

P n

Frame grabber

1

3

2

4

inspection system.

ce image was internally generated then the thresholdet to a minimum value between the two most statis-rtant peaks in the histogram, on the assumption thatrepresent the object and the background. If the his-

tains only two peaks, the threshold value was set tom value between these peaks. If the histogram containswopeaks, then the threshold valuewas set between therominent peaks. In this way the biscuit images could berom the background. The area of segmented image wasted in order to distinguish between touching and non-

scuits. This could easily be done since the surface area ofwas relatively constant. It was heuristically discoveredface area of the biscuit lies in the range between 39,870pixel square. Therefore, it was possible to distinguishm the untouched cases using direct thresholding. If thealler than the threshold value, then, the biscuits were

untouched. In this case, image processing only involvedand the removal of artefacts or noise. This was achievedf morphological erosion technique. For touched cases,essing required fourmore additional steps. Sequentiallyedge detection, (ii) watershed transformation, (iii)mor-rosion, and (iv) distance transformation. For the biscuitdiffers signicantly from the background, the Sobelobel, 1970) was applied to detect the edge of biscuit.tershed transformationwas implemented to the gradi-incent andSoille, 1991). Thewatershed transformatione gradientmagnitude of an image as a topographic sur-bjects have well-dened edges, an edge detection willaximum along the edges of each object. These maximaeach object as a catchment basin since they producein each object. A watershed transformation will then

tchment basins, effectively segmenting the image. Afterological erosion was applied to smooth the image and

facts. Then,watershed and the distance transformationmented inorder to separate touchingobjects and reduceoverlapping. The blob analysis was then used to com-

ntre of gravity of each object. Finally, image croppingto the original image in order to obtain the region of

e owchart in Fig. 3 summarises the image processingf touching and non-touching biscuits.

sionality reduction by Wilks analysis

to other image-based articial classiers the resultingfrom the above image processing algorithm are stillallow fast and accurate detection. Therefore, it is veryuse fewerhuevalues, preferably thosewithhighestdis-


Start

Grab image

A

C

Cob

Non-touchobjects

Fig. 3. Imagevalue T was se

criminantpperformingWilks selcriminantaby examininhp, iterativecontainingdecision totogether wical derivatdescribed in

2.6. Classi

Supportdeveloped b1998). It hawith excelleand Cheriethyperplaneclasses fromthe largest

The classidered as athere are lto the two(i=1, . . ., l),biscuit. The

ss (+

Support vectors Margin

VM u

s (hi,bel.

task og dathe

nedcan busint al.thedform

epresuto thresholding

alculate area for each object

Area > T

Edge detection of original image

Watershed transformation

Separate touching using distance transformation

ount number of jects and calculate centre of gravity

Image cropping

No

Yes

Morphological erosion

Morphological erosion

Touching objects

ing

Cla

Fig. 4. S

vectorclass laas thetraininclassifyis assigniquegroups(Platt electingdata tonode rEnd

processing for touching and non-touching biscuits. The thresholdt to 40,000 pixel square.

owers.Hence, a sequenceof operations is neededbeforeclassication. The most commonly used technique isection criteria, which is also known as the stepwise dis-nalysis (Rencher, 2002). In summary, thismethodworksg each hue value in the set containing all hue variablesly updating and deleting each variable until a subseta best hms is produced from the full available of hs. Theenter or to delete each variable is based on F-statisticith F-to-enter and F-to-remove values. The mathemat-ions and technical description of Wilks analysis areour earlier publication (Abdullah et al., 2001).

cation using SVM

vector machine is a supervised learning algorithmy Vapnik and others (Cortes and Vapnik, 1995; Vapnik,

s been used extensively for a wide range of applicationsnt empirical performance (Karimi et al., 2006;Adankon, 2009). The fundamental idea of SVM is to construct aas the decision line, which separates the positive (+1)the negative (1) ones in the binary classicationwith

margin. This is illustrated in Fig. 4.sication of biscuits into four quality levels can be con-collection of binary categorisation problems. Suppose

samples of biscuit in the training data correspondinggroups, and each sample is denoted by a vector hi,which represents the selected colour features of therefore the training data can be represented by the set of

theoreticallby expandidescribed in

For thefunction f h

f (hi) = sgn(

where w isshould satis

yi( wT hi + b

In operathat maximative samphyperplanestraints (4)For the nonthe groupsent strategyinto a highfunction k(kernels andthe study. R

k(hi, hj) = (

and

k(hi, hj) = e

where d is t1) Class (-1)

Hyperplane

ses hyperplane margin to separate positive from negative classes.

yi), where hi is the hue and yi {+1,1} indicates theThe classication of biscuit samples can be consideredf determining a classication function f : hi yi using

ta. Subsequently, the classication function f is used tounseen test data set. If f (hi) > 0, the input vector hito the class yi =+1, otherwise class yi =1. This tech-e extended to classication involving more than twog SVM algorithm such as the Directed Acyclic Graph

, 2000). Essentially, this algorithm works by rstly col-ata fromeachclassier, and secondly, assembling thesea graph or a tree comprising of several nodeswith eachenting result from each binary classier. Therefore, it isy plausible to perform more than 2-class classicationng and adding more nodes to the tree. The details areSection 2.7.

linearly separable training vectors, the classicationas the following form:

wT h + b) (3)

the normal to the hyperplane, and b is a bias termwhichfy the following conditions:

) 1, i = 1, . . . , l (4)

tion, the SVM attempts to nd the optimal hyperplaneises the separation margin between positive and neg-les. The margin is 2/|| w||, thus the optimal separatingis the one minimising ((1/2) wT w), subject to con-

, which is a convex quadratic programming problem.

-linear case when the hyperplane can only separatealgebraically but not strictly separate them, a differ-is needed. In this case, the input vectors hi are mappeddimensional feature space H by using suitable kernelhi, hj). Two popular kernels which are the polynomialthe Gaussian radial basis function (RBF) were used inespectively, they are dened as:

hi hj + 1)d

(5)

xp

(||

hi hj||222

)(6)

he degree of polynomial kernel.


The classication function then has the following form in termsof kernels:

f (h) = sgn[

l ]

where k is amultiplier cming problof quadraticoefcientsare used to(1999) descprogrammifurther detafunction as

W() =l

i=1

Under th

0 i C,

where C is aFor some

hyperplaneter C was umargin andsupport vecnel functioThe selectiomodelled d(SMO) algoanalyticallyThe details

2.7. Multi-c

For the pposed a noplacing two(DAG). Thea unique nwhich havein classica

This algolabelled wiperformedthe list. Thetest point isto the rstthe two clascontinues wthis stage, tthe entire sinto four grwe refer toi j node. Son the subsdecision is dThe detailsAbdullah, 2

1vs4

3

n

1 2 3

3 4

1. Under-baked 2. Moderately baked 3. Over-baked

he acyder-bed. Th

perim

his cted ae r50 sterud retors as foruracg set(2)

was iof inas uge Ws in oOncestemlue ast may using one of the kernel functions in Eqs. (5) and (6). ThenOalgorithmwas implemented to train the systemandestab-criteria from the image multiple samples by calculating alles useful in classication. Finally, the classication outputprocessed using DAG algorithm which uniquely classiedinto one of the four groups.r the training was completed, the machine vision systemitched from training to test mode. In this mode, the systemed to reclassify the training samples in order to assess itscy, repeatability and consistency. After this, the system wasclassify all samples belonging to the independent test set.

way, the variability in grading between inspectors could bered, and the accuracy between machine vision system andtors couldalsobe investigated.All biscuit sampleswereman-lassied and sorted by hand using semi-trained inspectorsing the same methods and procedures discussed previously.his study, results obtained from DAG algorithm were com-ith DA based classier using both full and reduced features.

hat, the accuracy of the system was assessed on moving tests using different speeds in real-time.i=1iyik(hi, h) + b (7)

kernel function, b is a bias term, and i is the Lagrangeoefcient obtained by solving the quadratic program-em. Based on KarushKuhnTucker (KKT) conditionsc programming, only those hi with the correspondingi >0 in Eq. (7) are called support vectors (SVs), whichmake the decision in the classication problem. Plattribes the techniques and procedures involved in KKTng. Interested readers are referred to this reference forils. Finding coefcientsi is equivalent tomaximise thefollows:

i 12

li=1

lj=1

yiyjk(hi, hj)ij (8)

e constraints:

(i = 1, . . . , l) andl

i=1yii = 0 (9)

non-negative regularisation parameter.data sets, the SVM may not be able to nd a separatingin feature space. Therefore a regularisation parame-sed to control the trade-off between maximising theminimising the training error. Entirely specifying a

tor machine requires setting two parameters: the ker-n and the magnitude C for violating the soft margin.ns of these parameters are dependent on the specicata. In this study the sequential minimum optimisationrithm was used to train the SVM algorithm and SVs areobtained by solving quadratic programming problem.

of SMO algorithm are described elsewhere (Platt, 1999).

lass SVM with directed acyclic graph

roblem of N-class SVM (N=4), Platt et al. (2000) pro-vel algorithm for multiclass classication based on-class classiers into nodes of a directed acyclic graphgraph used a rooted binary DAG, a root DAG that hasode with no arrows pointing into it, and other nodeseither 0 or 2 arcs leaving them, to be a class of functiontion tasks, as shown in Fig. 5.rithm constructs N(N1)/2 internal nodes, each one

th an element of a Boolean function. The DAG can beusing a list, where each node eliminates one class fromimplementation is initiated with a list of all classes. Aevaluated against the decision node that corresponds

and last elements of the list. If the node prefers one ofses, the other class is eliminated from the list. The DAGith testing until only one class remaining in the list, athe algorithm will terminate and the state of the list istate of the system. The classication of biscuit samplesoups was implemented by using DAG algorithm. Here,the decision node distinguishing groups i and j as theuch a DAG is obtained by training each i j node onlyet of training points labelled by i or j. The nal classerived by using theDAGarchitecture as shown in Fig. 5.of the DAG SVM are described elsewhere (Nashat and010).

4

Fig. 5. Tcates unover-bak

2.8. Ex

In tinspecsets. Thprisingand lattion aninspecsamplethe acctrainin(1) andaboveregionto 59 wthis staoptionables.the syhue vatem rspace bthe SMlishedvariablis thensample

Aftewas swwas usaccuraused toIn thiscompainspecually cfollow

In tparedwAfter tsample2vs4 1vs3

2vs3 1vs2 vs4

1 3 2

not 1 not 4

ot 2 not 3 not 4 not 1

4

1 2 3

2 3 4

1 2

2 3

4. Substantially over-baked

clic graph for 4-group classication via DAG strategy. Here 1 indi-aked, 2 moderately baked, 3 over-baked and 4 substantiallye abbreviation vs stands for versus.

ental procedures

olour inspection system 400 samples of biscuit werend categorised to either training or independent testst 200 samples were allocated to the training set, com-amples for eachgroup. These sampleswere then imagedsed to train themachinevision system for colour inspec-cognition. Another 200 samples were inspected by thend categorised as the independently set, comprising 50each group. This set was used to independently assessy of the machine vision system. Initially, the capturedwas converted from RGB to HLS colour space using Eqs.. After that, image segmentation algorithm describedmplemented to crop 80% of biscuit sample producing aterest. Then the resulted hue population ranging from 0sed as input variables to the machine vision system. Inilks algorithm was invoked with several thresholdrder to extract the maximum number of potent vari-the optimum or potent hue subset has been located,established the SVM classier by scaling each inputnd applying SMO algorithm. The colour inspection sys-pped the scaled hue vector to high dimensional feature


2.9. Parallel image processing

To achieve real-time object recognitionwith considerably lowerpower, we considered the multi-core architecture comprising of4 processing units with 6MB of level 2 cache memory. The basicidea is to partition the image into specic parts and simultane-ously processing them on different processors. Respectively, themultiple-buffering technique and the multithreading strategy areimplemented tomanage the image capturing cycle and synchronisethe execution of the program inparallel threads. Thesemethods aredescribed in the following sub-sections briey.

2.9.1. Multiple-buffering technique (MBT)The key component in reaching real-time implementation is the

Matrox dynamic simulator featuring the multiple-buffering strat-egy. This technique involves grabbing images and storing them intobuffers while processing previously grabbed images. In this wayMBT optimises wasted CPU cycles and reduces the memory costdue to managing inconsistent frame complexity. This techniqueallows imapermits forfrom image

In this stlist of buffeand to alloware being gthe processgrabbing iscessing funThe techniqtinuous acqaverage timthe camerasequence oconicts bebuffers of arequired. Inthe imagesthem. In thas speed eq

2.9.2. MultiMultithr

powerful stcations in ovision applutilising malgorithmsprocessor ttechnique h

Buffer 1

Buffer n-1

Buffer n

Processing 1

Processing n-1

Camera

Frame grabber

lock drocess

y in ts tohreaof ommi

hiswre me moicahe alachsiml. Aft coructu

r tocorr. Resultantly, the background is also ltered leaving thefeatures only. The multi-core processor used the feature

to count the number of sample, and resultantly, createdmber of compute threads in the node. One of the computes is assigned as a master, which allocates work to all otherte threads. Each thread independently performs the image

b n

Processing n

d 1 Thread 2 Thread 3 Thread 4

ask 1 Task 3 Task 4 Task 2

tem and with four SVMs threads.ges to be grabbed and processed concurrently. It alsorobustness when there is variability in processing timeto image (Hornberg, 2006).udy, multiple buffering is performed by rst allocatingrs used to hold series of sequentially grabbed images,for the processing of the buffers concurrently as they

rabbed. This grabbing is initiated by a start signal andcontinued until the stop signal is activated. In this case,implemented asynchronously which allows other pro-ctions to be executed while images are being acquired.ue goes round-robin through the list of buffers in a con-uisition. However, caremust be taken to ensure that thee to process a frame is not greater than the frame rate of. Therefore, the design in Fig. 6 is adopted showing thef image grabbing and storing. To avoid memory accesstween the video interface and the worker threads, twot least one for grabbing and one for processing arethis experiment, n (2n22) buffers are used to holdand the processing function is called n times to processis way complex image processing tasks can be realiseduals to the frame rate of the acquisition hardware.

threading techniqueeading technique and multi-core processing are tworategies to take advantage of parallelism in the appli-rder to improve the systems performance. Computerications have the potentials to run much faster byore powerful multi-core systems. On the other hand,and applications must be tuned to allow multi-coreo exploit their inherent parallelism. Multithreadingas the ability to perform multiple operations simulta-

Fig. 6. Bimage p

neouslthreadsame tcutionprograing.

In tthere ato havbe signrate. Tfrom emationparallea resulthe strstudy.

Prioimagesof viewdesiredspacethe nuthreadcompu

Grab 1 Grab 2 Gra

Processing 1 Processing 2

Thread 1 Thread 2 Thread 3 Thread 4

Task 1 Task 3 Task 4 Task 2

Threa

T

Fig. 7. Structure of queuing tasks of the inspection sysProcessing n

iagram of the multiple-buffering strategy for real-time grabbing anding.

he same process. This can be done by creating differentensure sequential execution of operations within thed while allowing simultaneous yet independent exe-perations in other threads. Here, the multithreadedng is implemented using MIL tools for multiprocess-

ork,multithreadingwas implementedbyassuming thataximum of four samples in the eld view. It is possiblere than four samples but the image resolution wouldntly reduced and the classication would be less accu-gorithm works by rstly extracting important featuressample in the image, and secondly, passing this infor-ultaneously to all distinct SVM classication threads inter the classication has completed, each thread gaveresponding to each sample in the image. Fig. 7 showsre of queuing tasks of four-threads SVM used in this

multithreading, the image is segmented into few sub-esponding to the number of biscuit sample in the eld


Start

Fig. 8. Flowchtechnique for

post-procestion. Resultthe monitocompletionall buffers aready to pr

the multi-threaded data ow structure implemented in thisstudy.

ults and discussion

lour

edgs inGrab image

Store image into buffer

3. Res

3.1. Co

ThebiscuitYes

No

Image segmentation and object detection

If no. of object 1

Master thread crops the grabbed image into sub-images

Master thread forks several threads one for each object

Each thread picks a task and processes the sub-image

Each thread writes result into memory

Each thread joins Master thread

Free buffer

Stop grading

End

Master thread displays the results

No

Yes

art of a real-time colour inspection system based on multithreadingparallel processing.

sing like dimensionality reduction and object recogni-s are assembled by the master thread and displayed onr screen or written into a text le if necessary. Afterof the image inspection task, the master thread freesnd the threads. Then, it enters a stand-by mode andocess next image. The owchart in Fig. 8 summarises

jointed regiis clearly vieach biscuiing performtransformalowing segmand then plhistogram iover 50 samshows a typlocal maximthe frequenground colbackgroundground corthe biscuits

Careful etics of biscuhue values,light or daris skewed t41; whereabaked groutwo groupstively. Thehlight and dondly it canof biscuits odistinctivelvalues for tdistributed14 to 43 forbaked grougroup. Thisvalues thatregions.

Howeveuncorrelatenot have siables need treduce thethemselvesset of varia(Smith and

3.2. Wilks

The implocate a huering to Fig.samples falvariables, wsis. These vdetect boththe analysisto default vatively tookinspection

e images corresponding to non-touching and touchingFig. 1 are shown in Fig. 9. The formation of four dis-ons corresponding uniquely to four biscuits in the scenesible in this gure. A cross-hair indicates the centre oft in the image. This result indicates that auto threshold-s well for non-touching biscuits while the watershed

tion is effective in dealing with touching biscuits. Fol-entation, the hue values of each biscuit are extracted

otted as a bar graph or histogram. Example of the hues shown in Fig. 10. This gure is plotted by averagingples of biscuits for each group category. The histogramical hue distribution of biscuits which contains manya and minima. It was also heuristically discovered thatcy distribution of hue depended strongly on the back-our, producing an image with optimal contrast whenhues ranged from green to blue. Hence, the cyan back-

responding to the hue value 180 was used to captureimages.xamination of Fig. 10 reveals some unique characteris-its. First, they are not truly brown, basedonquantitativebut rather show colours that tend toward yellow andk brown. Clearly, the colour of the under-baked groupowards the yellow and light brown region, peaking ats the colours of the over-baked and substantially over-ps are skewed towards the dark-brown region. For these, the hue reachedmaximumvalues at 21 and 16, respec-uedistributionofmoderatelybakedgroup lies betweenark brown region, peaking at approximately 20. Sec-be seen that the hue distributions for different groupsverlap each other; no single threshold exists that can

y separate one group from another. Thirdly, the huehe four types of biscuits are approximately normally, ranging from 16 to 43 for the under-baked group, fromthemoderately baked group, from12 to 30 for the over-p, and from 4 to 23 for the substantially over-bakedis in agreement with expected values, given that huerange from 0 to 60 fall between the red and yellow

r, not all these values are necessarily independent ord variables. In fact, some of them are redundant and dognicant discriminating power. Therefore, these vari-o be eliminated, since such an elimination will not onlydimensionality of independence of the variables among, but also, and more importantly, it produces a best sub-bles leading to an optimal rate of correct classicationNakai, 1990; Abdullah et al., 2004).

analysis

ortant element in this colour inspection system is tosubset that contains signicant input variables. Refer-10, the hue values of four different groups of biscuit

l in the range from0 to59.Hence, altogether there are60hich are statistically independent and useful for analy-ariables were submitted to Wilks analysis in order tothe insignicant and highly signicant hue variables. In, the F-to-remove and the F-to-enter variables were setalues of 2.71 and 3.84, respectively. The algorithm iter-eight steps to converge, producing a subset containing


Fig. 9. Segmenafter auto thre

8 principalThe 8 princ

hs = (19,20ted biscuits and their centres of gravity corresponding to image in Fig. 1: (a) non-touchinsholding; b(ii) is the edge image after watershed-based segmentation; a(ii) and b(iii) are

hues. This corresponds to a loss of 86.67% in variation.ipal components produced here were:

,23,29,39,40,42,43) (10)

Using thtrained usinefciency ostudying thg biscuits and (b) touching biscuits. Here a(i) and b(i) are edge imagesthe centres of gravity of a(i) and b(ii), respectively.

ese potent variables, the machine vision system wasg biscuit samples in the training set. The classicationf the system using 8 principal hues was examined bye Mahalanobis distances, shown canonically in Fig. 11.


Table 1Real time class

Conveyor sp

10m/min9m/min8m/min

Table 2Total processin

Classiers

SVM-RSVM-PDA

Functions 1inant functwhichwere(Morison, 2

This gragroups. It s

Table 3Total processin

Classiers

SVM-RSVM-PDAFig. 10. Hue distributions of four different groups of biscuits averaged ov

ication results of independent test samples corresponding to different conveyor speeds

eeds Classication results

Under-baked Moderately baked Over-baked

94% 98% 90%96% 98% 88%96% 98% 90%

g time of non-touching biscuits comparing sequential and parallel processing for differe

Sequential processing Parallel pr

Direct method Wilks method Direct me

No. of samples Processing time (ms) No. of samples Processing time (ms) No. of sam

4 29.44 4 28.26 44 29.51 4 28.19 44 24.14 4 24.10 4

and 2 in this gure refer to the canonical discrim-ions derived from the canonical correlation analysisused to examine the relationshipbetween thevariables005).ph clearly demonstrates the formation of four majorhows the existence of the hyperplane which strongly

separates ucontrast, thmoderatelyare disjointbraically noincreased w

g time of touching biscuits comparing sequential and parallel processing for different cl

Sequential processing Parallel pr

Direct method Wilks method Direct me

No. of samples Processing time (ms) No. of samples Processing time (ms) No. of sam

4 62.44 4 61.35 44 62.48 4 61.30 44 58.28 4 58.22 4er 50 samples for each group category.

using SVM-R classier with Wilks method.

Substantially over-baked Average accuracy

100% 95.5%100% 95.5%100% 96%

nt classiers.

ocessing

thod Wilks method

ples Processing time (ms) No. of samples Processing time (ms)

9.68 4 9.349.70 4 9.308.87 4 8.25

nder-baked and substantially over-baked groups. Ine hyperplane separation is weak for over-baked andbaked groups. It can also be seen that these groups

ed and convex, and the hyperplane separation is alge-n-linear. It was also observed that the non-linearityhen independent test samples were used.

assiers.

ocessing

thod Wilks method

ples Processing time (ms) No. of samples Processing time (ms)

36.70 4 36.5036.86 4 36.4336.20 4 35.93


Fig. 11. Discrimination separations by four biscuit groups produced by using Wilksmethod. Data are averaged from 50 samples of each group.

Fig. 12. Classiby using Wilk

3.3. Classi

The expeDA algorithto the resutest sampleapplicationtoWilks a

Fig. 13. Classiclassiers by u

0 to 59 are used for classication, whereas 8 principal hue valuesare selected with Wilks method. In this experiment, polynomialand RBF SVM classiers are trained and tested using the kernelsdened in Eqs. (5) and (6).

A rangeers are selthe SVM tha

In this(SVM-P) isods in all triwere chosedoing, the s. These sethe algorithresulted invalues selecWilks meableCwas1system andResults are

The resuaccuracy w

ers ymehe sed thier96.5

methrrecclassiWilksusing tproducR classrate ofdirectwith cocation results of training samples comparing SVM and DA classierss and direct methods.

ers analysis

rimental results on colour recognition of the SVM andms are summarised in Figs. 12 and 13, correspondinglts from classication of the training and independents, respectively. In these gures, results from the directof SVM and DA are also presented, serving as referencenalysis. In directmethod, all hue variables ranging from

cation results of independent test samples comparing SVM and DAsing Wilks and direct methods.

which yieldtion rates foDA.

These reare correladeleted varHence, theclassicatiorithms.

Results fties in machbaked samthe over-ba96.5% for otical plot inover-bakedbaked onesmisclassicage on thetherefore subetter than

Comparclassicatioclassicatiosuccess ratrect classiIn other wothe colour pThis observand quantianalysis of(Paradkar e

Based owithWilkson the convmoving atof parameters for the polynomial and RBF SVM classi-ected and tested to eliminate any biased performance oftmay be caused by inappropriate choice of parameters.experiment, the best parameter of polynomial SVMselected to be degree d=3 for direct and Wilks meth-als. In this work, the sigma () and penalty (C) variablesn experimentally through a search algorithm. In soeedvalues of 0.001and1 respectivelywasused forC anded points were heuristically decided. During searching,m solved Eq. (7) repeatedly and the C and values thatthe best accuracy were selected. In this case the sigmated for RBF SVM (SVM-R) were 58 and 2 for direct andthods, respectively. Meanwhile the best penalty vari-000. This penalty parameter improved the results of thesolved non-separable data set by using a soft margin.shown in Figs. 12 and 13.lts using training samples produced high performancehich is clearly seen in Fig. 12. Here SVM-R and SVM-Pielded best classication rate of 100% with direct andthods, whereas DA classiers yielded 99.5% and 99% byame methods, respectively. Results using test samplese same trend. It can be seen from Fig. 13, that the SVM-

with Wilks method, resulted in the best classication%, compared 95.5% SVM-P and 94% DA. The results ofod in Fig. 13, presented a best performance for SVM-Rt rate 95%, comparingwith the SVM-PandDAclassiers,ed 92% and 93%, respectively. In general the classica-r SVM-R are consistently higher compared to SVM-P or

sults also indicated that discarding of variables, whichted highly with those retained, guarantees that theiables are, in fact, redundant in the variable space.classiers after Wilks analysis is more precise inncomparedwithdirect applicationof SVMandDAalgo-

rom these experiments also demonstrated the difcul-ine vision distinguishing over-baked from moderately

ples. Referring to Fig. 13, the average success rate forked samples with Wilks method is 90% compared toher classes. These results are consistentwith the canon-Fig. 11 which showed that the hyperplanes separatingsamples to other samples particularly the moderatelyare relatively small. Considering this and the fact thatation of the vision system varied from 0 to 6% and aver-order of 3% for individual class, the above results mayggest thatmachine vision classicationmaybeequal orsemi-trained judgement in accuracy and consistency.ing Figs. 12 and 13, it can also be seen that the goodn results of the training samples are not reproduced innof the test samples. Therefore the100%or 99%averagee of training samples is articial, since the rate of cor-cation is based against the best subset in this system.rds, the machine vision system seemed to memoriseatterns in the training set instead of generalising them.ation is also in agreement with results on classicationcation of adulterants in marple syrup by multivariateFourier transforms infrared and near-infrared prolest al., 2002).n the stationary classication results, SVM-R classier analysiswas chosen to inspectmovingbiscuits placedeyor belt. The experiment was conducted on biscuitsthree different speeds: (i) 8m/min, (ii) 9m/min and


(iii) 10m/min. In this inspection, the machine vision system wastrained and tested using the same samples and illumination set-up as those used in the stationary classication. Here, the biscuitimages were acquired continuously from the centre of the eld ofview by det

Table 1,independenspeeds. Expslight decreThe averageing sampleschange in tfrom 8m/mmore, the sis used to itotal procesTables 2 andIt can be seis more efin the speedbiscuits is lsystem is apis attributedstill performis measuredparallelismResults commarised inTSVM even tperformanccompared tof the formalso be seening biscuitsaddition tocuits involvis wellknowSoille, 1991more intensR classicatshould havecuits/s forresults indican be realihardware. Ition at 10mThis imagegeneral req

4. Conclus

Quality eindustry. Inproposed asamples usicate that SVand Wilksafter Wilksthe highestimplementemultithreadan efcientimage procable changemoving bisc

was recordedby SVM-Rat a speedof 10m/min. This articial classi-er has apotential for use in routine inspectionof biscuits andotherbakery products. This articial classier has a potential to increaseinspection speeds beyond human operatorswhen implemented on

i-core bis

wled

s woch Uledggram

nces

, M.Zfor comE 79, M.Z

em foessing, M.Zclassi-basedContr, M.Zputer522.

Luth, Nprocehoven, M.Mwriti., Aleis by mneerina, F., Vlysis T.M., Yentica), 235

, Xun,orks71 (1, Sun, Xn and2732C., Va297.

z, R.C.ing, Mg, A. (GaA,

Y., Prahine tElectr., Oh,rolledessingkazakre-craleme, pp. 2C.A. (E., D.F.ia.S., Abduppo(4), 371979.ns onr,M.Mterantnal of., 199optimel Meecting the centre of each moving sample.shows the results of the dynamic classication usingt test samples corresponding to the different conveyorectedly, it can be seen from this table that there is aase in the accuracy compared to stationary inspection.correct classication attained by the system for mov-is consistentlyhigher than95.5%. There is no signicanthe accuracy when the speed of the conveyor is variedin to maximum adjustable speed of 10m/min. Further-ame correct classication is observed when the systemnspect touching biscuits. In addition to accuracy, thesing times were also considered. These are tabulated in3 for non-touching and touching biscuits, respectively.

en from these tables that the multi-threaded programcient when processing non-touching biscuits, resultingup of more than 3.0. The average speedup of touching

ess than 1.7. The maximum speedup attainable by thisproximately 4. The slightly less than the ideal speedupto the fact that the image segmentation procedure ised in sequential.Onaverage theutilisationofCPUcoresas 65%. This result indicates multithreading improvesand the multiple processing cores are highly utilised.paring single and multi-core processing are also sum-ables 2 and3. In both cases, theDA is slightly faster thanhough SVM is more superior in terms of accuracy ande. On average the accuracy of SVM-R stands at 96.5%o 94% for DA. Therefore, the slight reduction in speeder is compromised with its increase in accuracy. It canfrom these tables that the image processing of touch-

is at least 4 times longer than non-touching biscuits. Inthe auto thresholding, imageprocessingof touchingbis-ed thewatershed-based segmentation techniquewhichn for its extreme computational demand (Vincent and). Hence, the image processing for touching biscuits isive compared to non-touching biscuits. Based on SVM-ion results running on four samples, the vision systema theoretical throughput of 428 biscuits/s or 110 bis-

non-touching and touching cases, respectively. Thesecate that the total inspection process of several samplessed as speed less than the frame rate of the acquisitionn summary the algorithm is able to perform inspec-/min with correct classication of more than 95.5%.

processing capacity has the potential to fully satisfy theuirements of a fast biscuit manufacturing.

ion

valuation of bakery products has a major role in foodthis study a colour-based inspection system has been

nd shown to have a good potential to classify biscuitng SVM and DA classiers. Results from this study indi-M generally performs better than DA for both direct classications. It was discovered that the SVM-R resulted in correct classication of 96.5% which iscompared to other classiers. Parallel processing wasd based on Matroxs multiple-buffering technique anding programming. These features combined providecommunication scheme, resulting in the increased inessing speedups by a factor of 3. There was no notice-in the accuracy when the system was used to inspectuits. Like the stationary case, the highest classication

a multtion lik

Ackno

ThiResearacknowNet Pro

Refere

AbdullahysisIChe

AbdullahsystProc

AbdullahandDSPand

AbdullahCom481

Ach, R.,coreEind

Adankonhand

Blasco, JcitruEngi

CamastrAna

Carter, Rauth17 (2

Chen, X.netwture

Chen, K.visio(1),

Cortes,273

GonzaleRead

HornberCo. K

Karimi,macand

Kim, J.-YcontProc

Kyo, S., Ofor ping eUSA

Moore,York

Morisonforn

Nashat,ing s101

Otsu,N.,actio

ParadkaadulJour

Platt, J.CmalKern208.e processor. It is suitable for use in high volume produc-cuit processing considered in this example.

gements

rk has been supported by the Universiti Sains Malaysianiversity Grant 814012. The authors also would like toepartial support of this research through theAUN-Seed449/USM/2009.

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Support vector machine approach to real-time inspection of biscuits on moving conveyor beltIntroductionMaterials and methodsBiscuit colour gradingMachine vision systemColour space transformationImage segmentation and object detectionDimensionality reduction by Wilk's analysisClassification using SVMMulti-class SVM with directed acyclic graphExperimental proceduresParallel image processingMultiple-buffering technique (MBT)Multithreading technique

Results and discussionColour inspectionWilk's analysisClassifiers analysis

ConclusionAcknowledgementsReferences

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