EXTENDED ABSTRACT 1

Machine Vision in Casino Game MonitoringJoao Paulo Maurıcio Pimentel

Abstract—To prevent game losses due to mistakes or irregularstrategies, or for statistical data acquisition, the casino gameindustry is interested in automatic monitoring of the games,recording their state for posterior analysis.

A method for detection and recognition of playing cards takinginto account partial occlusion and rotation is proposed. This iscarried out in two phases: a scene is analyzed to detect rectangleswith an original method, fitting the card size, and then eachdetected rectangle is classified according to the figure present onthe corner of the card. The cards are all assumed of a knownsize and scale, and the image to be analyzed has no perspectiveor optical deformations, although a calibration phase to take thisinto account has been developed and tested.

Three methods for the card recognition stage are studied andcompared, evaluating their performance in terms of robustnessto brightness and contrast changes and computation time. Atemplate matching method was shown to work successfully andtime efficiently, although showing low robustness to lightingchanges, whereas an edge based probabilistic rigid model showedrobustness to such changes at the expense of a longer computationtime. A probabilistic deformable model presented low success anda very long computation time.

Index Terms—Playing Card Recognition, Rectangle Detection,Object Recognition, Template Matching, Edge Based Probabilis-tic Model.

I. INTRODUCTION

THE current computer vision technology has brought a risein automatic video-surveillance solutions. This solutions

are normally implemented for security reasons, but are alsoused in statistical applications, such as people or vehiclecounting systems. In this sense, the casino game industry isinterested in automatic monitoring of the games, either forsecurity (making sure the rules are correctly followed) or forstatistical data acquisition.

This project aims at developing of a playing card detectionand recognition robust enough to perform well with anyrotation of the cards, as well as cards partially occluded byothers.

A. Problem Formulation

The application was developed for Anglo-American playingcards, as can be seen on Figure 1. In this project, the index ofthe card is defines as the image of the right upper corner ofthe cards, made of a rank on the top, which can take one of13 values (A,2,...,10,J,Q,K), and a suit on the bottom, takingone of 4 values (Clubs, Spades, Hearts, Diamonds).

The problem is described as follows: Any number of cardsare laid on a dark surface in any rotation. Any number of cardscan be on top of another, as long as at least some of the outeredge of every card is visible, and the index e fully visible.

Index

Rank

Suit

Fig. 1: Type of card and its components used in this project.

II. STATE OF THE ART

There has not been much scientific work on card detec-tion and recognition. The two main works reviewed [1], [2]approach the problem in a similar way, first by detecting thecard and then classifying it. This methods are based on contourextraction, in a way such as the detection cannot be robustto partial occlusions. In [1] the detection is performed onlyfor an isolated card, detecting the rectangle fitting a boundingbox and then estimating the rotation of the card. The card isclassified using a template matching method for the rank andsuit.

In [2] the detection is based on closed contours of the image.A contour with 4 vertices and a given area is considered a cardcontour. Any contour with another given area and aspect ratiois considered a suit block (the suit symbols in the interior ofthe value cards - cards with rank A, 2 to 10). The classificationis made with template matching of the contours, counting thenumber of suit block in the interior, as this number correspondto the rank of the value card. The face cards (J,Q and K)are classified by matching SIFT features[3] extracted in thecard interior. This method uses the image of the whole card,ignoring the card index, and as such cannot take into accountpartial occlusions.

III. CARD DETECTION

The first step of the proposed approach is the card detection.This is carried by a voting scheme, based on the GeneralizedHough Transform [4]. A card is modeled as a rectangle definedby the center localization c = (cx, cy) and orientation φ. Sincethe cards are rotationally symmetric for rotations of 180◦, φis defined in [0, π[. The 0 degree orientation is considered tobe the orientation with the longer edge of the card in thehorizontal. We define the pose of a card as θ(c, φ) ∈ Θ,the space of admissible poses. As the card size and scale areknown a priori and having a rectangle described using onlythese three parameters, any non-rigid transformation on theimage is not allowed.

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A. Hough Transform for Rectangles

The voting accumulator for the Hough Transform is definedin the three dimensional space Θ. The outer contours of thecards in the image are extracted, as well as the gradientdirection in such points. Knowing the gradient in a contourpoint is directed to the inside of the card and perpendicular tothe contour, each contour point can only belong to rectanglesin just two orientations: the same as the gradient orientation,assuming the contour point belongs to the shorter edge ofthe card, or the gradient orientation plus 90◦, assuming theedge point belongs to one of the longer edges of the card.For each of these orientations, there is a finite number ofpossible rectangles passing in said contour point. The numberof possible rectangles is proportional to the size of the edgewe are assuming the contour point belongs to, given that thepose space is discrete.

Let M(x) be the contour map, having M(x) = 1 whenthere is a contour in x = (x, y) and let Φ(x) be the gradientdirection at x. The card size is defined as 2R × 2r, with 2Rcorresponding to the size of the larger edge of the card, and 2rthe shorter one. We define a voting accumulator H , indexed bythe elements of the pose space Θ. Each contour point votes onthe possible respective rectangles by incrementing the valuesof H(θi), with θi ∈ Θ being the poses of said rectangles. Thevoting procedure is described as follows:

For each point (x, y) such that M(x, y) = 1, select therectangle centers of the candidate rectangles to be voted. Theseare given by

cjR = x + rv + jw, j = −R, ..., R (1)

ckr = x +Rv + kw, k = −r, ..., r (2)

with v and w being unit norm vectors with direction equal toΦ(x) and Φ(x) + π

2 , respectively. The centers cjR correspondto the hypothesis of x belonging to one of the longer edges ofthe card, and ckr correspond to the centers in the hypothesis ofx belonging to one of the shorter edges. These centers belongto a line segment parallel to the hypothesized edge, passingby the center of the card, with a length equal to the size of thenon hypothesized edge (the points cjR are at a parallel distanceof r from x, and ckr are at a parallel distance of R). A visualrepresentation of this points, for a contour point x, is presentin Figure 2.

Knowing the two only possible orientations of the candidaterectangles, as described above, the poses θi to increment theaccumulator H will be given by the set Θc ⊂ Θ:

Θc ={θj = (cjR,Φ(x) +π

2), j = −R, ..., R}∪

{θk = (ckr ,Φ(x)), k = −r, ..., r}. (3)

which corresponds to the union of the poses of the two setsof centers, each one with its orientation.

To compensate for possible sizing errors, for example, anerror of a single or little more pixels in the card dimensions,the actual incrementing of the accumulator is not done only onthe poses described above, but also in a small neighborhoodaround them. More precisely, the centers of the candidaterectangles used are the same as the above plus an additional

x 2R

2r

rvRv

rw

Rw

cR

cr

Fig. 2: Filling of the accumulator for a single contour pointx. The red lines represent the possible rectangle centers givenx and the gradient orientation. The continuous line groups thecenters in the hypothesis of x belonging to one of the longeredges. The dashed line groups the centers in the hypothesis ofx belonging to one of the shorter edges.

set of centers, located at a parallel distance of 1 pixel fromthe original centers, from both sides. The orientations of theadditional poses are the same as in the original poses. Thiscan be viewed as a rectangular blur of the accumulator in justthe localization dimensions.

B. Rectangle detection

After looping for every contour point the filling of theaccumulator is over. Every point in the accumulator representsa rectangle pose, and its value correspond to the number ofvotes of that pose. The detected rectangles are representedby the local maxima of the accumulator. These are detectedefficiently by looping only through the accumulator pointswith at least 1 vote, selecting as rectangles those poses whichhave a value above some defined threshold, and a value higherthan or equal to the values of the 26 neighbors.

Using a voting scheme to detect rectangles, any card havingjust a small part of its contour visible can be detected, success-fully solving the problem for partial occlusions. However, thisalso results in a large number of false positives. The three maincases of this are: several detected rectangles near the correctone; rectangles detected having part of the contour in theexterior of the card image; and too many detected rectangleswhen the cards are occluded but aligned with one another. Toattempt to solve this kind of problems, we loop through therectangle candidates, ordered by the number of votes (their“strength”), and each one is analyzed sequentially.

To solve for the excess of detected rectangles near thecorrect one, a non maxima suppression is performed. Eachrectangle which is in a small neighborhood (in terms of thepose space) of a stronger rectangle is rejected.

If a rectangle passed through the previous test, the imageof its interior is analyzed using the contour map as the image.If there are contour points inside the rectangle this cannotbe a real card, because only the outer contours of the cardsare present in the image, and as such it is rejected. Account

EXTENDED ABSTRACT 3

for small deviations on the perfect position of the detectedrectangles is taken.

At last, when the cards are occluded by alignment either bythe short sides or the longer ones, the image of the contoursdoes not give enough information about the number of cardspresent. To minimize the number of detected rectangles in thissituation, the image of a small neighborhood of the corners ofthe rectangles are analyzed, to determine if the number ofcontour points matches an expected number of contour pointsin a real card corner. This very simplistic corner evaluation isnot very significant, as it was not intended to spend too muchcomputation time in the rectangle detection.

The detection phase is tuned as to bring no false negatives,at the expense of a possibly high number of false positives.Since any rectangle that passed this filtering can be rejectedin the card classification phase if no index is found on its topright corner, these false positives do not pose a big problemfor the method.

IV. CARD CLASSIFICATION

The second phase of the proposed method is the cardclassification. After the detection, and having a correct posefor at least the cards present in the image, their index can beeasily extracted, rotating the image in the detected rectangleand retrieving the image of the top right corner. Thus, the indeximages are assumed well segmented. Classification was doneusing various methods for comparison. The rank of the cardis first classified, and if considered a valid card (likelihoodor other matching criteria above threshold) the suit is alsoclassified. Otherwise, the card is rejected.

A. Probabilistic Rigid Method

A method based on [5] was implemented. It considers binaryoriented edge features, in 8 orientations at increments of 45o,computed at each point x of a grid L. Let X = {Xe(x)|x ∈L, e = 1, ..., 8} be the set of features with orientation e.Having Xe(x) = 1 means a feature with orientation e ispresent at x. After the feature extraction, the maps of thedetected features are dilated with a 3× 3 kernel. This dilationseeks to bring robustness to small deformations and enhancethe classification.

Even if the objects to be classified (rank or suit images) arecorrectly segmented, this does not imply a constant position onthe grid. Given this, and that the model is rigid, the pose θ ofthe object is defined as a 2D vector r. We define a probabilitymap pe(x), defined on the grid L. Given the object is presentat a location r, we assume the edges present are conditionallyindependent with marginal probabilities given by

P (Xe(x) = 1|θ) = pe(x; θ) = pe(x− r). (4)

Being this a rigid model, the probability map is shifted byr. To model the presence of edges outside the object (thebackground), we define the object support Se(θ) = Se(r) foreach edge direction, and assume a background model outsideof Se(r), having the edges independent as well, but with ahomogeneous marginal probability equal to pe,bgd. The supportis defined as the set of points where the marginal probability is

greater than a threshold pmin. With this defined, the likelihoodof the edge data is given by

P (X|r) =∏e

∏x∈Se(r)

[pe(x; r)]Xe(x)[1− pe(x; r)]1−Xe(x)

×∏e

∏x/∈Se(r)

[pbgd]Xe(x)[1− pbgd]1−Xe(x), (5)

corresponding to a binomial distribution on the feature pres-ence at point x.

Defining Pc(X|r).= P (X|c, r) as the likelihood of the edge

data given the object belongs to the class c ∈ C, the space ofthe different classes, the class of the object being analyzed isgiven by the maximization

Y = arg maxc

maxrPc(X|r). (6)

For each class, the probability maps for that class are shiftedby every value of r. The classification is done choosingthe class with maximum likelihood, if that value is abovethreshold. If not, the detected card is rejected.

To train the model, i.e. the probability maps, the edge fea-tures are extracted on a set of training images. The probabilitymaps of the model are given by the average of the edge featuremaps of the training images, for each edge direction e.

B. Deformable Probabilistic ModelA deformable model proposed in [5] was also implemented.

Having the detection and recognition problem rigidly posed,with non rigid deformations not considered, there is not muchmotivation for this kind of model, thus it was not extensivelyexplored in this work. It is similar to the model described inthe previous section. The same binary edge features are used,and a probability map pe(x) on feature presence is defined inthe same way.

To model the deformations of the object, the pose cannotbe described just as a location vector as in the rigid case.We define n reference points (yi)i=1,...,n. The deformation isdescribed by a set of n 2D shifts v and a location r as well,defining the pose as θ = (r, v) = (r, v1, ..., vn). Each yi ismapped to zi = r + yi + vi, with r + vi representing a rigidshift of the model. This way, the deformation is described bythe shifts of the reference points. If all vi are equal, the posecan be described as a location r, hence having no deformationpresent. To consider different shifts of the reference points,different models are shifted accordingly, and combined withan averaging operation, performed by parts.

We define a part Qi associated to the reference point yi asa submap of pe in a square neighborhood W around yi:

Qi.= pe(yi + s), s ∈W, e = 1, ..., E. (7)

The map pe(x; θ) can no longer be given by a shift of pe(x)as in the rigid case. The parts are combined by a Patchworkof Parts (POP) operaion. Let N(x) = {i : x ∈ zi +W} be theset of windows W covering the point x. The POP operationis used to compute the map pe(x; θ) as follows:

pe(x; θ) =

{1

|N(x)|∑i∈N(x) pe(x− zi + yi) if N(x) 6= ∅

0 if N(x) = ∅(8)

EXTENDED ABSTRACT 4

The map pe(x; θ) is given by the combination of the differentparts, computing the average in the regions covered by morethan one part. Having the probability maps pe(x; θ) defined,defining the object support and assuming the edges are condi-tionally independent as in the rigid case, the likelihood of theedge data is given in the same way as (5):

P (X|θ) =∏e

∏x∈Se(θ)

[pe(x; θ)]Xe(x)[1− pe(x; θ)]1−Xe(x)

×∏e

∏x/∈Se(θ)

[pbgd]Xe(x)[1− pbgd]1−Xe(x). (9)

The classification is done choosing the class with maximumlikelihood as before:

Y = arg maxc

maxr

maxvPc(X|θ = (r, v)). (10)

However, the maximization over v is difficult, since there canbe a very large number of possible combinations of the vectorsv1, ..., vn. To choose the shifts vi that give the maximumlikelihood for a given class and location r, one of the methodsproposed in [5] is used, called Independent Maximization:

Initialize v(0)i = (0, 0), i = 1, ..., n. Choose a smallsquare neighborhood V around the origin. At iteration t loopthrough the different reference points yi. For each vectorv ∈ v(t−1)

i +V the part Qi is placed at yi+v and its likelihoodis computed using (9) only on the points of the part. ignoringall the others. The value of v that corresponds to the maximumlikelihood is given to v(t)

i . After all v(t)i are updated, more

iterations can be made. In other words, the shift associated toeach reference point is the one that gives maximum likelihoodof the part associated to it.

After the best shifts are vi are selected, this maximization isrepeated for every value of r and for each class, and the classchosen is the one with maximum likelihood, being rejected ifthis value is below threshold.

The model training is analogous to the rigid case, exceptthat it is made for each part. Defining the reference pointsyi in a regular grid, and after the feature extraction of thetraining images is performed, the average of the feature mapareas corresponding to each part is computed, having each partas the average of the parts on the training images.

C. Template Matching

Having a fixed scale imposed on the card images and havingthe orientation of a detected card known, a natural choice fora classification method is the template matching. Each rankor suit of the detected card is compared with a template fromeach class, using a cross-correlation criteria. As before, theclass chosen for the rank and suit is the class with maximumcross-correlation, if it is above threshold.

The templates used are constructed using the average of thetraining images.

V. DETECTION AND RECOGNITION IN PERSPECTIVE

To demonstrate that the proposed method’s efficacy is notentirely dependent on the camera position being perpendicularthe card plane, it was tested taking the images with different

camera positions and angles. An image taken this way presentssome perspective deformation, which was corrected as toproceed to the card detection phase.

To correct the perspective deformed images, the pinholecamera model was used. By this model, the image is formedby projecting 3D points in the physical world into the imageplane, according to a perspective transformation:

sq = M[R|t]Q, (11)

or

s

xy1

=

fx 0 cx0 fy cy0 0 1

r11 r12 r13 t1r21 r22 r23 t2r31 r32 r33 t3

XYZ1

(12)

where (X,Y, Z) are the coordinates of a 3D point in thephysical world and (x, y) are the coordinates of the projectionpoint in the image plane in pixels. M is the matrix of intrinsicparameters of the camera, which are the focal lengths fx, fyand the principal point (cx, cy), usually at the image center.This parameters do not depend on the camera position. Thejoint rotation-translation matrix

[R|t]

is called the matrix ofextrinsic parameters, and describes the camera position, bytranslating a point (X,Y, Z) to some coordinate system.

Given the cards to be recognized are all in the same plane,there can be a 3D coordinate system such that the card planecorresponds to Z = 0. Using this value, the third row of thematrix R can be rejected, ending with a projection describedby

s

xy1

= H

XY1

(13)

where H is a 3× 3 matrix called homography matrix. Havingthe projection described this way, we don’t need to explicitlyestimate the camera intrinsic and extrinsic parameters, but thewhole matrix H can be estimated using at least 4 correspon-dences of points in the image plane and in the card plane.

The calibration used in this work follows this. A calibrationimage is acquired, with a rectangular piece of paper of knownsize. A corner detection algorithm, based on the minimumeigen values of a covariance of derivatives matrix, is executedon the image acquired to detect the 4 corners. Knowing thesize of the paper, we can correspond its corners to 4 knownlocations on the image, respecting the aspect ratio of the paper.Calibrating this way we get an image with correct appearance,solving the perspective deformation problem.

VI. RESULTS

The results for each phase of the proposed method, thedetection phase and the recognition one, will be shown. Havingused three different methods for the recognition phase, theirresults will also be shown for comparison. The results of thecalibration procedure are also shown.

EXTENDED ABSTRACT 5

(a) (b)

Fig. 3: Card detection with occlusion. The red rectanglesrepresent the correctly detected cards. In green are representedthe false detection positives. (a) A disposition which does notallow rectangles to be present in the interior of the contours.(b) A disposition which allows several rectangles to be present.

A. Card Detection

The detection phase, when the card size is modeled care-fully, delivers no false negatives. It takes a considerableamount of time (1 second for an image with about 13 cards).Partial occlusions pose no problems, except when there areseveral cards aligned vertically or horizontally. In these lattercases, as was described above, the outer contours do not giveenough information about the number of cards present in themiddle. These cards cannot be guaranteed to be detected andrecognized. The number of false positives detected dependsheavily on the cards disposition in the plane. When they aredisposed in a way such that no cards but the real ones can fitthe interior of the contours, the number of false negatives canbe and typically is null. In the reverse situation, a large numberof false positives can appear. The Figure 3 show an example ofthis, with the correctly detected and classified cards delimitedin red, and the false detected positives in green after rejectionin the classification phase.

When at least a whole corner of a card is visible, thedetection faces no problems.

B. Classification

The classification phase is the most sensitive one. Having apossibly large number of false detection positives, the classifiershould be able to correctly classify just the real detectedrectangles, and reject any other. Both the rigid probabilisticmodel and the template matching perform this task. Givenideal illumination conditions (the same as in the trainingimages) both this methods deliver 100% correct recognitionrates.

The illumination can be simply modeled as brightness andcontrast. A set of images was analyzed with several values ofbrightness and contrast by both rigid methods (probabilisticmodel and template matching). The rigid probabilistic model,being based on edge features, is relatively invariant to illu-mination conditions. The success of the template matchingcan depend heavily on these conditions. This is shown inFigure 4, where the recognition rates with the probabilisticmethod are shown. The probabilistic rigid method behaves

−40 −20 0 20 40 600

0.2

0.4

0.6

0.8

1

Rec

ogni

tion

Rat

e

Brightness Variation

(a)

−80 −60 −40 −20 0 20 40 60 800

0.2

0.4

0.6

0.8

1

Rec

ogni

tion

Rat

e

Contrast Variation

(b)

Fig. 4: Recognition rates with brightness (a) and contrastvariation (b). In red, the results for the probabilistic rigidmodel. In blue, the results for the template matching.

well for large variations of brightness and contrast, whereasthe template matching only works in small variations onthe training images. The variation values are considered as100 being the most positive variation possible, and -100 themost negative one. The probabilistic method is, however, alot more computationally expensive when compared to thetemplate matching, resulting in a performance as much as 200times slower. The template matching can take as little as 0.02seconds in an image with 13 cards.

A great part of the classification errors with the templatematching come from the suit recognition. The suit symbols arenot very different from one another in terms of shape, whencompared to the different ranks, and the red suits appear lessdark than the black ones when the image is in gray scale.These facts make the method incorrectly classify a black suitas a red one when, p.e., the image has a brightness increaseover the ideal illumination, making a black suit symbol havethe same gray scale value as a red suit.

The deformable model works extremely slow. Even thoughit correctly classifies a card rank when its image is deformed,e.g by an affine transform, the model does not work suc-cessfully in normal conditions, failing considerably. This canbe explained by the fact that the deformation estimate candeform the object to the point of being close enough to awrong class, since no deformation is expected, resulting in amisclassification.

C. Classification with Perspective Correction

The perspective correction was tested using the originalimage in two different resolutions, to assess how the correctedimage appears as it is expected (with no deformation andcorrect reconstruction) according to the source resolution. Thequality of this calibration proved to be sensible to both thesource image resolution and the degree of perspective, i.e.,how much is the perspective deformation, which depends onthe angle of the camera with the card plane. Figure 5 shows anexample of a heavily perspective deformed image of a card,and the corner of the card in the corrected image for twodifferent source resolutions. A low source resolution results ina much worse reconstruction of the image. This can be becauseof interpolation errors, since with a lower resolution the pixelvalues have to be computed from longer interpolations, and

EXTENDED ABSTRACT 6

(a) (b) (c)

Fig. 5: Perspective correction. (a) Source image. (b) Sample ofthe corrected image from a low resolution source. (c) Sampleof the corrected image from a higher resolution source.

also because the corner detection is not so accurate, whencompared to a higher resolution.

The image correction problems reflect on the classificationperformance. The card detection works well with the correctedimages, since the correction, even when not very accurate,does not change the card edges enough to affect it. Theimages of the rank and suit symbols can, however, become toodegraded. When a high enough source resolution is used, boththe rigid methods perform with 100% recognition rate. Whenthe source resolution is low, as in figure 5(a), the probabilisticrigid model presents a lower recognition rate, as low as 79.5%.The template matching however presents perfect results in thesame images. This can happen because the degradation of thecorrected images an affect the feature extraction, failing todetect edges where they were expected.

Even if there is a small deformation of the symbols inthese cases, the deformable model fails to correctly classifythe cards. The deformable model is also dependent of a correctedge feature extraction.

VII. CONCLUSION

A method for detection and recognition of playing cards wasproposed. An original method, based on the Hough transform,for the detection of cards as rectangles was developed. Being avoting scheme, the detection performs well in cases of partialocclusion. For the classification, made independently from thedetection, three methods of object recognition were compared:template matching and two probabilistic methods, one rigidand the other deformable. With controlled illumination con-ditions, the best method for this application is the templatematching, for the efficacy and computational efficiency. Theprobabilistic rigid model works well in various illuminationconditions, but is slower and presents some problems when notenough definition in a perspective corrected image is available.The probabilistic deformable model is too slow and presentslow efficacy for this rigid application, not being a good methodto consider.

As is natural in any work with limited time, several otherpaths could have been explored or further developed. The pro-posed method simulates the human vision in card recognitiononly for a simple and controlled case. In fact the humanbeing is able to identify the cards even without observingtheir contours, searching and finding a rank symbol with a suitsymbol below. He can also identify the cards even without theindex image, since the card interior contains all the informationneeded about it. This way, some of the paths that could havebeen explored are presented.

Rectangle Detection. The detection presented assumes thecards have no deformation and a fixed scale and dimension.A more robust detection could find this cards even underperspective. The card detection itself and the consequentperspective detection could be used to calibrate the image, asto obtain the the index images with no deformation and in theright scale. However, even if the method would become morerobust, the classification would still depend on the contourpresence.

Detection of the card indexes. One way to integrate thedetection and classification in just one phase is to search theimage for card indexes (ranks and suit symbols below). Adetection algorithm based on robust features, such as SIFT[3]or SURF[6], could be used. This way, the detection wouldnot depend on the visibility of the contours, and to a certainextent would be robust to deformations such as perspective.The classification would be closely related to the detection,identifying each candidate region or blob as belonging to acard, being classified or rejected.

Classification of the inner image. The image in the interiorof every card contain all the information needed to classifyit. In the rank cards (A, 2,...,10) the rank is given by thenumber of suit symbols in their interior. These symbols haveall the same size, except for the Ace of Spades, which typicallyhas a different suit symbol referring to the card manufacturer.The classification can be made by counting the symbols, incase they are all visible, or by their disposition, which variesaccording to the rank and can be used in cases of partialocclusions (with limitations). The face cards (J, Q, K) have animage in the interior. Even though it’s not easy for a humanobserver to identify a face card with just this image, every facecard has a different one. This can be detected and classifiedwith a SIFT or SURF descriptor, even with some occlusion.

Every method integration. The human being uses everyone of this processes to identify playing cards. A robustmethod could work the same way, starting from a simplermethod and climbing to the more complex ones when needed.

REFERENCES

[1] C. Zheng and R. Green, “Playing card recognition using rotationalinvariant template matching,” in Proc. of Image and Vision ComputingNew Zealand, 2007, pp. 276–281.

[2] K. Zutis and J. Hoey, “Who’s Counting? Real-Time Blackjack Moni-toring for Card Counting Detection,” in Computer Vision Systems: 7thInternational Conference on Computer Vision Systems, ICVS 2009, Liege,Belgium, October 13-15, 2009, p. 354.

[3] D. Lowe, “Object recognition from local scale-invariant features,” in iccv.Published by the IEEE Computer Society, 1999, p. 1150.

[4] D. Ballard, “Generalizing the Hough transform to detect arbitrary shapes,”Pattern recognition, vol. 13, no. 2, pp. 111–122, 1981.

[5] Y. Amit and A. Trouve, “Pop: Patchwork of parts models for objectrecognition,” International Journal of Computer Vision, vol. 75, no. 2,pp. 267–282, 2007.

[6] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robustfeatures (SURF),” Computer Vision and Image Understanding, vol. 110,no. 3, pp. 346–359, 2008.

[7] J. S. Marques, Notes for Image Processing and Vision. Instituto SuperiorTecnico.

[8] G. Bradski and A. Kaehler, Learning OpenCV: Computer vision with theOpenCV library. O’Reilly Media, 2008.

[9] R. Szeliski, “Computer Vision: Algorithms and Applications,” MicrosoftResearch, 2010.