EP2145293A2

EP2145293A2 - Card-counting device

Info

Publication number: EP2145293A2
Application number: EP08805504A
Authority: EP
Inventors: Rachid Harba; Benoit Berthe; Dominique Perdoux; Benjamin Tourne
Original assignee: Datacard Corp
Current assignee: Entrust Corp
Priority date: 2007-04-26
Filing date: 2008-04-23
Publication date: 2010-01-20
Also published as: AU2008257340A1; WO2008145859A3; CN101816012A; KR20100040700A; FR2915601A1; FR2915601B1; US20100226576A1; WO2008145859A2

Abstract

The device makes it possible to count series of products (2) that are not very thick, stacked side by side, in a determined direction in a retention means, and comprises: a lighting means producing one or more light beams covering the whole length of the stack, a means (8) of detection with photosensitive elements and including an optical device, making it possible to focus light rays reflected by the stack (5), processing means (10) receiving signals originating from the detection circuit, extracting light levels from these signals in correlation with a dimension of stack thickness expressed in pixels, and computing the number of products by determining the repetition of a pattern representative of a product (2) in a noise-free signal resulting from a conversion of the signals received. A Fourier transform is respectively applied to correlation and bicorrelation functions of the signal in order to find a periodic pattern representative of a product if necessary to the nearest phase shift.

Description

Card counting device

The invention relates to the field of counting devices of thin products stacked side by side for small series. More specifically, it is a matter of counting, in an automated manner and with a good rate, the number of thin products contained in a batch of small series.

There are counting devices as described in patent FR 2,718,550 entitled "Product Counting Device". This device allows the counting of large series of thin products stacked side by side. Typically, the brightness is tested by the saturation of the signal, provided by a sensor and if there is saturation, the counting is not done and the counting system produces a signal "no product found". If there is no saturation, the counting device counts. The counting device uses an intercorrelation system, in a pre-processing step of the stored signals. Then, the counting of the objects is done by the determination of the vertices and the valleys (in other words local maxima and minima for representative luminosity values associated with the pixels) and the number of objects counted is memorized. The device performs a plurality of counts each of which is stored and only at the end of this plurality of counts does the device construct a histogram of the results and search if a value corresponds to a memorized success rate.

However, these devices are not suitable for the automatic processing of small series because they do not allow to count the number of elements in small series in an automated way, with a good rate. The counting of thin products is generally part of a processing chain, before, for example, physical or software personalization operations or packaging operations. Often, the counting of small series of thin products, such as series of customizable cards of fifteen elements, is done by hand, this means of counting giving a good performance. There is therefore a need for a suitable device, having a rate to avoid counting small series by hand.

The present invention therefore aims to overcome one or more disadvantages of the prior art by creating a device for counting, in an automated manner, the number of thin products produced in small series, with a good rate.

This objective is achieved by means of a device for counting series of thin products, stacked side by side, in a determined direction in a holding means, the stacked thin products being all of identical thickness and constituting a stack, the device comprising at least:

a lighting means of the stack producing one or more light beams covering at least the entire length of the stack,

detection means comprising at least one detection circuit, comprising a plurality of photosensitive elements, and at least one optical device, associated with the detection circuit, for focusing light rays reflected by the stack,

- Storage means, characterized in that it comprises processing means receiving signals from the detection circuit or circuits, able to extract from these signals brightness levels correlated with a dimension along the stacking axis expressed in pixels, the processing means generating a determined signal x (n) corresponding to the received signals and including:

extraction means for extracting, from the determined signal x (n), a pattern representing a thin product; and calculating means for calculating the number of thin products, by intercorrelation of the determined signal with the extracted pattern, to determine an intercorrelation signal corresponding to the number of patterns present and corresponding to the number of thin products of the stack.

Thus, it is advantageously allowed, after an estimation of the pattern representing a card or other thin product, to find precisely the number of times the pattern is present in the acquired signal: each time that this pattern is present in the signal, it corresponds to a map. Reliable counting can be ensured for cards in a pile stack, even in the presence of certain stacking irregularities in the pile (spacing between two non-contiguous cards for example, oblique map in the stack, etc.).

According to another particularity, the processing means also comprise:

pretreatment means for carrying out a Fourier transform allowing to provide from the received signals a transformed signal revealing harmonics and then to determine the characteristics of a filtering means for filtering the transformed signal with conservation of at least 1 harmonic ; said determined signal x (n) being a filtered signal resulting from the pretreatment.

According to another feature, the pretreatment means comprise reconstitution means carrying out a transformation of

Fourier inverse on a filtered transformed signal provided by said filtering means, for outputting a preprocessed signal corresponding to the determined signal.

According to another feature, the extraction means are arranged to extract the representative pattern of a thin product in the pre-processed signal.

According to another feature, the means for extracting a pattern include:

means for parameterizing the thickness determining the first harmonic in the Fourier transform of the signals received and the corresponding thickness of the product,

first calculation means for performing correlation or convolution functions on the preprocessed signal in a first step, then a Fourier transform calculation for estimating in a second time, for each of the frequencies of the Fourier domain, the module and the argument of the Fourier transform of the pattern representing the periodic signal position corresponding to a thin product; and

second calculation means using an inverse Fourier transformation to calculate said first pattern from results obtained by the first calculation means.

According to another particularity, the first computing means elaborate an autocorrelation function c (τ) of the filtered signal x (n), defined (for example here in its non-normalized version) by the formula:

N-τ-i c (τ) = X x (n) x (n + τ), τ = [θ, Ep-1] n = 0

where N is the number of pixels of the image of the filtered signal, x (n), n = [0 .. N-1] is the denoised signal and Ep is the thickness of a thin product expressed in pixels.

According to a variant, the first calculation means develop a convolution function conv (τ) of the filtered signal on itself, defined by the formula:

N-τ- \ co "v (r) = Σ X (Λ) .X (Γ - W), τ = [θ, Ep -l] π = 0

According to another particularity, the first calculation means are arranged to calculate the Fourier transform of the autocorrelation function c (τ) of the filtered signal x (n), n = [0 .. N-1] in order to determine the module of the Fourier transform of the periodic signal portion.

According to another feature, the means for setting the thickness of the thin products determine the thickness in pixels and the first calculation means realize, for a first half of the frequencies of the plurality of frequencies, in order to determine the argument of the Fourier transform of the periodic signal portion, an estimate of the values of the argument functions 6> ,,, (f) for f = [0, N-1] with N = (Ep + 1) / 2 if N is odd or N = Ep / 2 + 1 if N is even, where θ _m {f) is an odd and Ep-periodic function, Ep being the thickness of a thin product expressed in pixels; this estimation being carried out by means of n-correlation of order greater than 2 arranged for:

- use a 2-variable operator whose definition is: b (τ _x , τ ₂ ) = Σx (n) x (τ _x + ri) x (τ ₂ + ή) for r, = [0, Ep - 1] and T ₂ = [O, Ep -I] n where N is the number of pixels of the image of the filtered signal and x (n), n = [0 .. N-1] is the filtered signal; compute the Fourier transform of the n-correlation function b (τ1, τ2) in the Fourier domain, via a two-dimensional Fourier transform, to obtain a matrix set of linear relations expressing the arguments of the function of n correlation according to the arguments of the pattern in the frequency domain of Fourier; and - invert the system (matrix inversion, or reduce to a triangular system) to go back from the n-correlation argument to the argument of the pattern in the Fourier domain.

For example, an invertible matrix for passing the argument of the transform of the n-correlation function to the argument of the pattern in the Fourier domain can be calculated. The resolution of the system can also be done by reducing the linear system to a triangular system. The resolution is then iterative.

According to another particularity, the means for parameterizing the thickness comprise means for estimating the thickness Ep using a first Fast Fourier transformation FFT, the estimation means producing:

- a calculation of the FFT and its module;

a localization of the fundamental by a search for a maximum on the module of the FFT, while in the Module vector, of size N, the position of the fundamental is noted Xfonda; a calculation of the thickness Ep, taking into account that the position of the fundamental corresponds to a thickness Ep expressed in pixels: Ep = N / Xfonda; and

- a rounding of the value found for Ep to the nearest integer value.

According to another feature, filtering means are provided to provide the extraction means with a filtered and denoised signal, the second calculation means for determining a first periodic pattern representative of a thin product to a possible phase shift. According to another feature, the extraction means execute at least one de-signaled signal processing algorithm for determining the signal pattern used for the intercorrelation, the shape of the pattern retained for a series of products being counted being estimated after a comparison between the first periodic pattern detected in the denoised signal and a reference pattern stored in the storage means.

According to another feature, the parameterization means associated with the processing means are provided for storing the reference pattern during a counting performed by the counting device with a standard batch of thin products. The reference pattern can also be chosen from a series of standard geometrical shapes (slot, inverted slot, triangle, parabola portion, etc.).

According to another particularity, the filtering means is a comb filter configured to filter out, in the received signals, noise and frequencies that do not correspond to harmonics, in order to obtain a pre-processed signal in which frequencies far from the harmonics and may correspond to gaps or spaces between thin products are eliminated.

According to another feature, the signal pattern extraction means comprise circular registration means making it possible to avoid obtaining a pattern offset by phase shift, the circular registration means reproducing from the first pattern patterns with different phase shifts, the phase shift finally applied being determined by using a reference pattern.

According to another feature, the means for calculating the number of thin products comprise: means for calculating the intercorrelation between the extracted signal pattern and the denoised signal, making it possible to supply the intercorrelation signal; and

pattern counting means in the denoised signal, by detecting the local maxima of the intercorrelation signal. According to another particularity, the circular registration means comprise:

means for determining, from the first pattern, patterns with different phase shifts;

means for calculating a scalar product for calculating the scalar products for the different patterns with the reference pattern; and

- Comparison means for determining a maximum among the calculated scalar products, the phase shift applied finally corresponding to that for maximizing the dot product with the reference pattern. According to another particularity, the processing means generate a vector representative of the signals received and perform a FFT fast Fourier transform on this vector, the filtering means receiving the fast Fourier transform of this vector and realizing a frequency Fourier filtering after a determination of harmonics. According to another feature, said vector is generated by a program executing a zero-padding zeros filling method so that said vector corresponds to an increased signal size and groups a number N _zp of signal samples, N _zp being a power of 2, the program being provided with a function of deletion of the added zeros, this deletion function being activated to make it possible to obtain said filtered signal after application of the IFFT inverse fast Fourier transform. According to another particularity, the intercorrelation calculation means calculate the cross correlation / («) between the estimated motive word (k), of size Ep, and the denoised signal χ (k), of size N, by using the following formula: k = Ep- \

For n = * £ ..N - & / (/!) = Σ word (k) .x (n - - ^ k) ιt = 0 ^ where n is the number of pixels in the image of the denoised signal, x (k) the denoised signal and Ep is the thickness of a thin product expressed in pixels.

According to another feature, a CIS module (equipped with a sensor CIS "contact image sensor"), arranged longitudinally and vis-à-vis the stack constitutes the lighting means and the detection means, the CIS module being of length at least equal to that of the stack, or the CIS module making displacements in the longitudinal direction of the stack vis-à-vis a zone covering at least the entire length of the stack in several steps.

According to another feature, the device comprises a plurality of CIS modules, arranged longitudinally and opposite the stack, each CIS module comprising detection means and lighting means by a plane beam according to the determined direction , the sum of the lengths of the CIS modules being at least equal to the length of the stack. According to another particularity, the CIS modules illuminate the stack according to a lighting line, each CIS module being inclined at a given angle so that its light plane beam meets this feature.

Another purpose is the use of a counting system according to the invention to allow adaptations of certain manufacturing operations depending on the batch and to follow each batch permanently.

This object is achieved by the use of the counting device by which information is transmitted, via means of communication, by the processing means to a processing system, such as a personalization machine, downstream of a processing line. , the transmitted information including the number of thin products calculated by the device for each series constituting the stack and / or information making it possible to deduce this number and / or an identifier associated with each series. According to another particularity, the processing system customizes the products of the series, physical or software personalization operations to be applied to each element of a series being associated with the information transmitted by the processing means.

A further object of the invention is to enable the device to be used for personalization of smart cards or similar portable objects. For this purpose, the invention also relates to a use of the counting device, characterized in that a logical customization station, processing a series of thin products comprising an integrated circuit, allows the inscription, in memory of the integrated circuit, personalization information for the use for which the product is intended. Another object is to provide a method of processing detection signals that perform well and allow a rapid analysis of the signal to count the number of products of the same thickness in a more or less compact stack.

This object is achieved by a method for processing at least one signal originating from the detection circuit (s) (of the optical type) of a device for counting thin products, characterized in that it comprises:

a step of preprocessing said signal, including a filtering of the signal to produce a filtered signal;

an estimation step in the filtered signal of a representative pattern of a product can be thick;

a step of calculating intercorrelation information between the estimated pattern and the filtered signal, for detecting patterns present in the filtered signal; and

a step of signaling, by an interface of the device, information representative of the number of thin products processed by the device, by counting the maxima detected in the intercorrelation information. Thus, it is permissible according to the invention

According to another particularity, the filtering during the preprocessing step of said signal is performed after a Fourier transformation and by using a comb filter. The filtering can also be done by implanting a conventional finite or infinite impulse response filter.

According to another feature, the method comprises a step of converting the signal, before the filtering, into data representative of brightness levels in correlation with a stack thickness dimension expressed in pixels, the estimation step defining a first representative periodic pattern of a thin product to a possible phase shift, and then using a reference pattern to achieve a circular registration to obtain a second pattern estimated without phase shift.

According to another particularity, the signaling step comprises a display of a number of smart cards to be processed by a chip card personalization machine and / or a transmission of the information representative of this number to the personalization machine. .

An additional object of the invention is to provide a program executable by a computer system for controlling the treatment adequately to obtain a fast and reliable count.

For this purpose, the invention relates to a computer program directly loadable in the memory of a computer and including computer codes for controlling the steps of the method when said program is executed on a computer, said program thus allowing a count of series thin products of a stack.

The invention, its characteristics and its advantages will appear more clearly on reading the description made with reference to the figures referenced below: FIG. 1 represents a logic diagram of steps which summarizes the general course of a counting method according to FIG. 'invention. FIGS. 2A and 2B show an example of an amplitude graph of a fast Fourier transform associated with the signals coming from the photosensitive elements, and respectively illustrate the decomposition of the signal in harmonics and the pre-filtering of Fourier for the search of patterns.

Figs. 3A and 3B respectively show a useful signal (denoised) and the corresponding signal including noise.

Figure 3C illustrates a modeling of the pattern to be searched for in the denoised signal. Figures 4A and 4B illustrate the possible presence of a phase shift.

FIGS. 5A and 5B respectively illustrate a reference pattern used in the circular registration, and an example of a circular registration run.

FIG. 6 represents a general diagram of a search for the optimal pattern according to one embodiment of the invention.

Fig. 7A is a perspective view showing an example of a counting device having a CIS module covering the entire stack.

FIG. 7B shows a perspective view showing an example of a counting device comprising a CIS module covering the entire stack by longitudinal displacements.

Figure 8 illustrates the cross-correlation between the pretreated signal and the estimated pattern.

FIG. 9 represents an example of a counting device comprising a transverse CIS module performing a longitudinal displacement.

FIG. 10 represents an example of a counting device comprising a CCD matrix camera performing longitudinal analyzes along several longitudinal lines. FIG. 11 represents an example of a counting device comprising a CCD matrix camera performing one or more longitudinal analyzes by displacement in the longitudinal direction. Fig. 12 is a perspective view showing an example of a counting device comprising a CCD camera.

Figure 13A illustrates a signal obtained with a better contrast than that of Figure 2A. Figure 13B illustrates a similar signal obtained with poor contrast from that obtained in Figure 2A.

The invention will now be described with reference to FIGS. 1 to 17. FIGS. 7A and 7B show a counting device comprising a CIS module (3). One or more CIS modules (3, 3d) may be arranged longitudinally. A module (3, 3d) CIS comprises illumination means, a photosensitive cell and an integrated optical focusing device. FIG. 12 represents a counting device comprising lighting means (7), mirrors (9a, 9b) and a camera (8) CCD. Other cameras of the same type, comprising an optical device and a photosensitive circuit and producing an electrical signal depending on the light received are usable.

Focusing the light rays reflected by the stack (5) allows recovery of one or more signals via at least one detection circuit. These signals are extracted to allow processing, in which it is sought to analyze the variations in brightness levels in correlation with a stack thickness dimension expressed in pixels. The device allows counting of series of products (2) thin, stacked side by side, by determining the repetition of a representative pattern of a product (2) in a filtered signal and denoised resulting from a transformation of received signals. Advantageously, a first Fourier transformation is used before comb filtering to obtain the denoised signal thereafter. A system based on Fourier transform and statistics with an order greater than 2 is used to allow to precisely define a periodic pattern representative of a thin product to a possible phase shift, via a calculation of the argument and the module of the transformed signal pattern in the Fourier domain.

For a good presentation of the products (2) such as smart cards, facilitating the counting operation, the device can comprise a rectangular container (4) containing the products (2) thin, only the products (2) at the ends of the stack (5) being shown in Figures 7 to 11. The products (2) thin can be maintained, so non-limiting, by a shrinkable transparent film or shims resting on the tray (4). The tray (4) serves in a non-limiting manner means for holding thin products (2).

In another embodiment, a magazine for processing thin products (2) is directly used. The stack (5) is illuminated along its entire length, by a plane beam of light rays (6, 6d) produced by the lighting means of a module (3, 3d) CIS or by a lighting means. diodes whose rays are focused in a plane by an optical device. The beam (6, 6d) plane projected against the stack (5) produces a bright line (T). The line (T) is then analyzed by means (3, 3d, 9a, 9b, 8) for detecting the reflected light intensity, associated with processing means (10). In a slightly different embodiment, the lighting means comprise a fluorescent tube (7) which illuminates, by means of multidirectional spokes (7a), the entire upper part of the stack (5), including the area of the line ( T) light cited above, analyzed by the detection means associated with the processing means. In the present description, the analysis of a longitudinal light line (T) by the detection means (3, 3d, 9a, 9b, 8) associated with the processing means is called longitudinal analysis of the stack (5). The analysis according to several segments of the stack (5), over its entire length, by the processing means (10) associated with the detection means is also understood as a longitudinal analysis.

The light rays (6) emitted by the light source (s) allow a longitudinal analysis of the batch of products, that is to say parallel to the long side of the tray (4). The relative displacement of the tray with respect to the CIS module (s) is transverse, that is to say parallel to the small side of the tray (4), and involves longitudinal analyzes on different longitudinal zones. The line (T) longitudinal light is indeed moved to different levels depending on the width of the stack (5). By for example, 100 longitudinal analyzes are performed in a transverse movement (M4a, M3a) of back and forth, back and forth. In an alternative embodiment, different longitudinal analyzes are performed by transverse displacements not perpendicular to the longitudinal direction, the line (T) on the stack (5). In another embodiment a fluorescent tube (7) more powerful than diodes, illuminates the entire upper part of the stack (5). In this case, a matrix photosensitive cell, for example a CCD matrix _1, can simultaneously perform longitudinal analyzes on different longitudinal zones without relative displacement of the tray (4) with respect to the lighting and detection means.

A CIS module (3, 3d) or the camera (8) CCD are connected to a processing circuit for transmitting electrical signals from the transformation of light energy into electrical energy by the photosensitive cells. The electrical signals produced contain information for each pixel of the CIS or CCD photosensitive cell. The electrical information is generally translated into levels, digitized and stored by the storage means. The storage and storage phases, already contained in patent FR 2 854 476 entitled "device for counting stacked products", will not be described here. Each photosensitive cell CIS or CCD comprises, for example 10,000 photosensitive elements, to analyze the entire length of the stack (5) and allow the counting of a batch of products of, for example, a maximum of 1000 products. Each photosensitive element makes it possible to detect a light signal and to express this signal in the form of an electrical signal representative of at least 256 brightness levels. This signal for 256 levels of brightness is translated into 8-bit words, each word is stored in the device memory. Thus for the example given, the memory consists of 10,000 one-byte words. In an alternative embodiment, the photosensitive elements of the CIS or CCD photosensitive cells may be sensitive to rays of different colors and to their constitution by a combination of red, green and blue. In another example embodiment, the photosensitive cell is a matrix comprising for example 2000 photosensitive elements, for the analysis of the length, by 2000 photosensitive elements, for the analysis of the width. Simultaneous longitudinal analyzes are therefore possible along several longitudinal lines (T) of the stack (5), at different distances from a long edge of the stack (5). In this case the analysis of the light rays reflected by the stack (5) is performed in two dimensions, unlike other embodiments in one dimension. The analysis performed in two dimensions allows several different longitudinal analyzes of the stack (5), the counting device being fixed, while the analysis in one dimension requires a displacement, for example of the stack (5), to perform several different longitudinal analyzes.

The representative information, for example of the brightness level, stored in memory in digital form is translated in the form of a graph, as illustrated by the curve (C1) of FIG. 8, and show variations in brightness. The graph has peaks representing maximums and troughs representing minimums of the signal from the electronic circuits associated with the photosensitive cells. The processing means (10) make it possible to analyze these variations by treating, for example, all the values taken in order according to their position. For example the rightmost pixel is processed, then the next one to the left and so on. A processing algorithm relies, for example, on the comparison of at least two successive values in order to determine the direction of variation of the curve.

The processing of the data representative of the level of brightness, stored in memory, will now be described in conjunction with FIGS. 1 to 6 and 7A.

The s (n) signal (s) collected by a longitudinal analysis of the stack (5) of thin products (2) are recovered by the processing means (10) which then determine the repetition of a pattern (M) representing a product using a processing algorithm of a Noise signal. Fourier filtering is carried out beforehand to eliminate the hollows in the recovered signal, the noise elimination being able to be carried out just after for the counting signal reconstituted by an inverse Fourier transformation. The flow of the count is illustrated in FIG. 1. The counting method thus comprises:

a step (51) of pretreatment of the recovered signal, including a filtering of the signal to produce a filtered signal, the filtering preferably being a filtering performed on the Fourier transform (fast or otherwise) FFT of the signal with a comb filter; a step (52) for estimating in the filtered signal (Sf) and optionally denoised (Sd) of a pattern (M1, M2) representative of a product (2) can be thick, the estimation being facilitated by using the denoised signal (Sd) as shown in Figure 3A;

a step (53) for calculating intercorrelation information between the estimated pattern and the filtered signal (Sf) and optionally de-noiseed (Sd), for detecting patterns (M1, M2) present in the filtered signal (Sf), respectively denoised (Sd); and

- A step (54) of signaling, by an interface of the device, an information representative of the number (N) of products (2) thin processed by the device, by counting the maxima detected in the intercorrelation information.

The method first comprises a step (50) of converting the signal, prior to filtering, into data representative of brightness levels in correlation with a stack thickness dimension expressed in pixels. In one embodiment of the invention, the signaling step (54) comprises a display of a number of smart cards to be processed by a chip card personalization machine and / or a transmission of the representative information. from this number to the personalization machine. The aforementioned steps (50, 51, 52, 53, 54, 55) can be performed automatically on a computer connected to the detection means (8). All signal processing and calculations can be i performed by a program loaded directly into the computer memory and specifically used to count the number (N) of thin products (2). As illustrated in FIGS. 5A and 5B, the shape of the pattern retained for a series of products (2) being counted can be estimated after a comparison between the first periodic pattern (M1) detected in the denoised signal and a reference pattern ( Mref) stored in the storage means.

The estimation step (52) may make it possible to define the first periodic pattern (M1) representative of a thin product (2) with a possible phase shift, as illustrated in FIGS. 3C, 4A and 4B. Preferably, the reference pattern (Mref) is used to perform the circular registration illustrated in Figure 5B, to obtain a second pattern (M2) estimated without phase shift. By way of nonlimiting example, parameterization means associated with the processing means may be provided in the device to obtain the reference pattern (Mref) during a counting performed by the counting device with a standard batch of products ( 2) slightly thick. Other configuration modes for the reference pattern (Mref) can of course be used.

To carry out the step (53) for calculating the intercorrelation information, the processing means (10) provide, for example, an intercorrelation signal (C2), as illustrated in FIG. 8, and pattern counting means (M2). ) in the denoised signal, by detecting the local maxima (S) of the intercorrelation signal (C2).

With reference to FIGS. 2A and 2B, the pretreatment step (51) can be performed as follows.

It is first necessary to consider the signal of size N acquired by the detection / acquisition machine (8): s (n), n - 0..N-1

The pretreatment step (51) may consist of denoising this signal s (n) to the maximum by filtering the frequencies that do not correspond to harmonics (comb filter). The steps of the filtering are for example the following ones: i) "Zero padding" method (filling of zeros) An addition of zeros is made at the end of the counting signal s (n) so that the number of samples N _zp is a power of 2 (This is necessary to calculate the FFT transform). The signal after the filling of zeros s _{zp is} written: S _2p (n), n = O..N _zp -1 If n <N: s _zp (n) = s (n)

If n≥N: s _zp (n) = 0

Thus, the recovered vector corresponds to an increased signal size and groups an even number N _zp of signal samples.

ii) Calculation of the FFT The Fast Fourier Transform (FFT) of the vector S _zp (size

N _zp ) is a complex vector S _zp (n), n = 0..N _zp - 1. This vector makes it possible to estimate the different frequencies contained in the signal s _zp . iii) Location of the fundamental

When a signal has a high frequency, its FFT transform has a special character. In Figure 2A, which traces the module of

S _2p (") - a succession of decreasing peaks of height ^is observed The graph shown is produced for a thickness Ep of product (2) parameterized at 18 pixels and shows the modulus of the FFT transform as a function of normalized frequencies. The peaks represent the periodic character of the signal.The first peak (hO) is called the fundamental (or first harmonic) and the other peaks (hi, h ₂ , ...). ) are called the harmonics iv) Frequency filtering The filtering is done by truncation of the FFT transform, as illustrated in Figure 2B.There are only the frequencies around the harmonics.The frequency bandwidth (p) of each bandwidth is This frequency width is denoted by 2 * b _P. The filter thus obtained forms a comb filter The processing means (10) advantageously use this type of comb to eliminate by filtration. rage of noise and especially the frequencies do not not corresponding to harmonics. Frequencies distant from the harmonics and which may correspond to differences between the products (2) thin are eliminated.

The FFT transform of the filtered signal is denoted S _F (n), n = 0..N _zp -1. It is thus obtained as follows:

S _F (n) = S _zp (n) if minjp - h _t , i = 0 ... number _ harmonics) ≤ b _p

S _F (n) = 0 otherwise v) Reconstitution of the counting signal

To find the filtered signal from its FFT transform, an IFFT inverse fast Fourier transform is applied. Then we delete the zeros at the end of the signal. The filtered signal thus obtained is the pre-processed signal. It is denoted x (n). The algorithm for calculating the fast Fourier transform and the other calculation algorithms are known per se will not be detailed here (see, for example, Methods and Techniques of Signal Processing, by Jacques Max and Jean-Louis Lacoume, Dunod Editions, at subject of the FFT transform).

It will be understood that the means (10) for processing the device are provided with at least one program that makes it possible to memorize all the intermediate results obtained successively during the processing, for example using storage tables. The different calculation algorithms are respectively used by calculation modules arranged to retrieve the appropriate information (portions of the signal being processed, results of the previous operations, etc.).

During frequency filtering, only a part of the harmonics can be kept. In theory, it is indeed quite possible to count the number of thin products (2) in the signal by keeping only the fundamental (also called harmonic 0). Take the case of a signal with a very good contrast as shown in Figure 13A (the harmonic 0 has a module with a value of about 60000, much higher than the module of other harmonics) In this case, 96% of the useful energy is concentrated in the first harmonic. Simple bandpass filtering, (or even low-pass) around the fundamental is enough to collect most of the useful information. The counting system of thin products works very well.

Let us take a second example, that of a signal with a bad contrast as shown in Figure 13B (the harmonic 0 has a module with a value of about 4000 and the harmonic following a module of about 2000).

In this case, the energy remains important for the first three harmonics.

Comb filtering retaining at least the first three harmonics is necessary and sufficient.

In most cases, comb filtering that keeps all harmonics can be done. But these two examples show that depending on the case, it is possible to keep less. In all cases, you must keep at least the fundamental so that you can calculate the number of thin products. A harmonic comparison system can be used to limit the filtering to a certain number of harmonic (s). The step (52) of estimating the pattern (M1) to a possible near phase shift will now be more particularly described in connection with FIGS. 3A,

3B and 3C.

The principle of the processing is based on the following model: the signal x (n) is the sum of a noise w (n) and a useful signal y (n) composed of a repetition of motives word (n) representing the slice of a map.

Φ) = y (n) + w (n) (R1)

For example, if the pattern (M1) representing a card is a sawtooth as shown in FIG. 3C, the signals y (n) and x (n) have the appearance represented by the respective traces (Sd, Sf) of the Figures 3A and 3B. The denoised signal plot (Sd) has a geometric character easily recognizable in the example of Figure 3A.

In the case of a counting of smart cards or similar portable objects, the thickness of the card expressed in pixels may be denoted Ep. Its value is fixed arbitrarily at the beginning of the treatment. The estimation of the thickness can be made at the beginning of treatment using a first FFT: - Calculation of the FFT and its module.

- Localization of the fundamental by a search of maximum on the module of the FFT. In the Module vector, of size Λ /, the position of the fundamental is noted Xfonda. - The position of the fundamental corresponds to a thickness Ep expressed in pixels: Ep = N / Xfonda.

We then round Ep to the nearest integer value. In the example of FIGS. 3A to 3C, the purpose of the estimation step (52) is to estimate the periodic pattern (M1) which repeats itself regularly in the denoised signal. It is understood that a modeling of the counting signal facilitates the search for an optimal pattern in its representativeness of a card. It is thus necessary to estimate in the signal y (n) the pattern related to the thickness (e) of a card: word (n) for n = [0, Ep-1]. For this, the processing means (10) perform an estimation of the Fourier transform (FT) of the pattern:

Word (f) = FT [word (n)], n = [0, Ep-1]. (R2)

For each frequency f, the word word (f) in the Fourier domain is expressed by: Word (f) = R _m (f) e ' ^ΘΛn . The search for Word (f) takes place in two phases:

- Estimation of the module ">">

- Estimation of the phase θ _m (f)

Once the Fourier transform of the periodic signal portion Mot (f) is estimated for each frequency f, the word pattern (n) will be easily calculable by an inverse Fourier transform.

The processing means (10) then make it possible to estimate respectively the module and the argument of Mot (f). For the estimation of the modulus R _m (f) of Word (f), the processing can simply consist in carrying out the autocorrelation c (τ) of the observed signal. c (r) = Σx ()) x (r +)), r = [0, Ep-I] (R3) The Fourier transform of the relation (R3) gives the module of Word (f):

C (J) = Word (f) .Mot (-f) = K (/) (R4)

As can easily be appreciated by those skilled in the art, the module can also be found with a convolution of the signal with itself: conv (τ) = Σ x {n) x (τ-ri) _t τ = [0, Ep - 1] n

Concerning the estimation of the argument #, "(/) of Mot (f), it can be clever to simplify the problem by symmetry. Indeed, the problem of estimating Ep values θ _m (f) for f = [0, Ep-1] can be simplified by half by using the symmetry properties of the FT Fourier transform of a real sequence. : θ _m {f) is an odd and p-periodic function. The simplified problem is then:

Estimate θ _m (f) for f = [0, N-1] with N = (Ep + 1) / 2 if N is odd

Ep / 2 + 1 if N is even

To estimate the arguments # ", (/) of the simplified problem, we use a somewhat more complex operator, called bicorrelation, and well known in mathematics, for example in the field of higher order statistics. This is a 2-variable operator. Its definition is as follows:

^ ^ R = = r ((((((((((((((((((((((((((((((τ ₂ + n) for r, = [0, Ep - 1] and τ ₂ = [0, Ep - 1] (R5) n In the Fourier domain (2-dimensional FT), the transform of

sr is of type 2_ _l b (τl, τ2) e ^N ; and the relation above becomes: n = 0

B <J _ι , f ₂ ) = Word (S _ι ) Word (f ₂ ) .Mot (-f _ι -Z ₂ ) (R6)

We note θ _b (f _x , f ₂ ) the argument of S (^ f ₂ ) - θ _b (f _λ , f ₂ ) can be expressed as a function of θ _m (/):

The above relationship is one of the fundamental properties of bicorrelation. The following documents deal specifically with this kind of properties:

- Higher-Order spectra analysis, A nonlinear signal processing framework; Chrysostomos L. Nikias / Athina P. Petropulu

- Signal processing, "Higher order statistics for signal processing", J. L. Lacoume / P.O. Amblare / P.Comon (the relation

R7 being indicated on page 115 of this document).

Now write the relation for fi varying from 0 to N-1 and for f ₂ = 1. There comes a system of N equations (linear system):

θ _b (0, 1) θ _m (0) + θ _m (1) θ _m (1) θ _b (1 V θ _m (1) + θ _m (V θ _m (2) θ _b (2, 1) θ _m (2) + θ _m (1) θ _m (3)

(R8). θ _b (N-1, 1) = θ _m (N-1) + θ _m (V - θ _m (N)

It should be noted here that the last relation of the above system involves θ _m (N). For reasons of imparity and periodicity of the Fourier transform of a real sequence, we have: θ _m (N) = - θ _m (N-1) if N is odd θ _m (N) = - θ _m (N-2) if N is even The above system allows Theta _B = [θ _b (0,1) ... θ _b (N-1,1)] to be expressed as a function of Theta _M = [θ _m (0) ... θ _m (N-1)]. Matricially, the system (R8) is written as follows:

Theta _B = A. Theta _M (R9) The value of matrix A depends only on Ep. The last row of matrix A of the system varies according to the parity of Ep.

Here are the matrices of the system for Ep = 16 and Ep = 17:

1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 2 -1 0 0 0 0 0 0 0 2 -1 0 0 0 0 0 0

0 1 1 -1 0 0 0 0 0 0 1 1 -1 0 0 0 0 0

0 1 0 1 -1 0 0 0 0 0 1 0 1 -1 0 0 0 0 A (16) = 0 1 0 0 1 -1 0 0 0 A (17) = 0 1 0 0 1 -1 0 0 0

0 1 0 0 0 1 -1 0 0 0 1 0 0 0 1 -1 0 0

0 1 0 0 0 0 1 -1 0 0 1 0 0 0 0 1 -1 0

0 1 0 0 0 0 0 1 -1 0 1 0 0 0 0 0 1 -1

0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 2

These matrices are always invertible, regardless of the value of Ep.

The matrix system described above makes it possible to find the values of θ _m (0) at θ _m (N-1) by the following relation:

Theta _M = A ¹ . Theta _B (R10)

Matrix A links the arguments of the bicorrelation (correlation to a higher order) to the arguments (θ _m ) of the pattern. By a resolution of the system (Calculation of A ^"1 , or by being reduced to an equivalent triangular system), we obtain θ _m in the Fourier space.When the modulus and argument of Mot (f) are calculated, the motive is easily deduced by an inverse Fourier transform (IFT).

With reference to FIGS. 4A, 4B, 5A and 5B, the means (10) for processing the counting device make it possible to perform a circular registration to eliminate any phase shifts. Indeed, in many cases, the estimation of the pattern by the calculations described above is not yet satisfactory. Considering for example the signal illustrated in FIG. 4A, the algorithm used to search for the pattern will give the estimate of the pattern (M1) as shown in Figure 4B. A phase shift is apparent. The estimate is a correct estimate of the pattern (M2) to a pure phase shift. For the estimated pattern to be correct, a reference pattern (mref) is used. The reference pattern (Mref) may for example have the shape shown in Figure 5A, inverted U-shaped (here three segments).

An example of additional processing applied to the pattern obtained in FIG. 4B is illustrated in FIG. 5B. The registration may consist of applying different phase shifts to the reconstituted periodic pattern (M1), until the pattern (M2) resembles the reference pattern (Mref). Among all the patterns (m) possible, the one giving the maximum of the dot product with the reference pattern (Mref) corresponds to the pattern of a card. This is shown in FIG. 5B, in which the scalar products found (from top to bottom) are respectively: Product_Scalary (Motif, MotifRef) = 0.7

Product_Scalary (Motif, MotifRef) = 0,5 Product_Scalaire (Motif, MotifRef) = 0,3 Product_Scalaire (Motif, MotifRef) = 0,2 Product_Scalaire (Motif, MotifRef) = 0,3 Product_Scalaire (Motif, MotifRef) = 0,5

Product_Scalary (Motif, MotifRef) = 0,7 Product_Scalary (Motif, MotifRef) = 0,9

The pattern (M2) obtained after registration then corresponds to a correct estimate of the pattern of a card or similar portable object, thin.

FIG. 6 summarizes the processing method implemented to make it possible to estimate a trace in the signal representative of a thin product (2). Signal x (n) repeatedly contains the word pattern (n) whose Fourier transform can be expressed as r (n) e ^{lθ (n)} . After calculating the module and the argument of Word (f), ironing in the real domain and then circular registration (by determining a maximum of scalar product with the reference pattern (Mref)), we obtain the pattern (M2) modeled by word (n). Once that the pattern is estimated, the counting is done by calculating the cross correlation / («) between the estimated motive word (k) (of size Ep) and the denoised signal x (k) (of size N). This step, also called adapted filter, is carried out as follows:

k = Ep- \ c-

For n = X JV - ^ - / («) = £ word (k) .x (n - ^ - + k) k = 0

The counting is done by detecting the local maxima (S) or vertices of the intercorrelation signal (C2), as shown in FIG. 8. Having a preprocessed signal x (k) makes it possible to establish an exact count, without risk of error due to a small spacing between two consecutive products (2).

In an exemplary embodiment, FIG. 7A, the device is composed of a CIS module (3) projecting a beam of light rays (6). The light rays (6) are projected on the stack (5) of thin elements (2), contained in the tray (4), in a longitudinal direction, forming a line (T) light on the stack (5). ). In another embodiment (not shown), the device may comprise three CIS modules combined so that the light rays and the modules 3a, 3b, 3c cover the entire length of the stack (5). The CIS modules are for example placed so that a part of the treated areas overlap. In addition, the modules can be inclined so that the illuminated areas are aligned. Two of the modules can be inclined at a given acute angle to the vertical and the other module can be inclined at an acute angle to the vertical. In this case, the modules are inclined so that the intersection of the plane light beams with the stack (5) forms a line (T) light.

In an embodiment variant not shown, the CIS modules are not inclined, the longitudinal analysis being performed according to several segments whose sum of lengths is at least equal to that of the stack (5). An initialization phase makes it possible to determine the relative positions of the CIS modules. In another exemplary embodiment, FIG. 7B, the device comprises only one CIS module (3d) which moves relative to the stack (5) in several positions (PO1, PO2, PO3) in a longitudinal direction. This module (3d) traverses the entire length of the stack (5) after several displacements and several stops at given positions (PO1, PO2, PO3) in order to treat, each time, an area (ZO1, ZO2 , ZO3) of the stack (5). The different positions (PO1, PO2, PO3) are chosen so that each zone partially overlaps the adjacent zone. The processing means identifies the signals corresponding to the overlap and eliminates the duplicate signal portion. A calibration step concerning overlapping areas is also described in patent FR 2 854 476 in order to efficiently process duplicate data.

In FIG. 7A and in the three-module variants 3a, 3b, 3c, the relative displacement of the module (s) (3) CIS with respect to the tray (4) is realized, according to one embodiment, by a transverse displacement ( M4a) of the tray, relative to the longitudinal direction of the lighting, the module or modules (3) being fixed. In another embodiment, this same relative displacement is achieved by a transverse displacement (M3a) of the CIS module (s) (3), the tray (4) being fixed. In the embodiment of FIG. 7B, the relative displacements are in a transverse or longitudinal direction. A relative longitudinal displacement is carried out parallel to the longitudinal illumination in order to position the module (3d) CIS above the various zones of the tray (4), this displacement (M4b, respectively M3b) being achieved either by moving the tray ( 4), the module (3d) CIS being fixed, either by moving the module (3d) CIS, the tray (4) being fixed. Once in position (PO1, PO2, PO3), a possible displacement (M3a, respectively M4a) relative transverse module (3d) CIS relative to the tray (4) is performed, for example, perpendicular to the longitudinal illumination. In all cases, the transverse displacements (M3a, respectively M4a) relative to the module (s) relative to the tray (4) involves several longitudinal analyzes according to different longitudinal zones of the stack (5).

Figures 9, 10 and 12 illustrate the use of a camera (8), for example matrix or linear CCD type. The camera (8) CCD is associated, but not limited to two mirrors (9a, 9b) and means (7) lighting. This type of device is detailed in patent FR 2 718 550. The sensitive photo sensor is, for example, linear and allows the longitudinal analysis according to a feature (T). The associated lighting means are, for example, a fluorescent tube or diodes whose illumination rays are focused or not. Several longitudinal analyzes are, for example, made according to the same line (T) with different illumination intensities.

In an alternative embodiment, several longitudinal analyzes are, for example, performed according to different features (T1, T2, T3), by a relative displacement of the stack (5) relative to the camera (8) CCD and the device lighting. The means (7) of illumination, for example, made by diodes whose rays are, according to a nonlimiting example, focused by an optical device, and requires transverse relative displacements, in order to perform several different longitudinal analyzes.

In the case where the lighting means is formed by a fluorescent tube (7), the entire upper surface of the stack (5) is illuminated, but with different intensities. The area closest to the tube is illuminated at a higher light intensity than the more distant areas. This type of illumination of variable intensities, is combined or not to relative transverse displacements to achieve different longitudinal analyzes according to different features (T1, T2, T3) longitudinal, with different light intensities. One variant comprises the variation of the luminous intensity obtained by controlling the lighting means, according to a variable power.

In the case of a relative displacement, the means (8, 9a, 9b) of detection are fixed and the tray (4) is movable (M4a), the tray (4) is fixed and the means (9a, 9b , 8) are movable at least in part, the mirrors (9a, 9b) and / or the camera (8) CCD being movable. In another embodiment, the photosensitive sensor of the camera (8) CCD is matrix. This type of photosensitive sensor allows analysis in two dimensions, depending on the length and width of the stack (5). In the case of a matrix photosensitive sensor, transverse displacements are not necessary to perform several longitudinal analyzes. The camera (8) CCD analyzes, for example, the entire length of the stack (5), as shown in Figure 9, where the stack (5) is analyzed over its entire length with a longitudinal displacement (M8) of the camera (8) CCD. Several lines, covering the entire length of the stack (5) are analyzed, the lines being very close, or even glued, at a distance for example of 5/100 of a centimeter or more distant at a distance, for example from one or several millimeters. The lines (T, T1, T2, T3) analyzed are also illuminated according to different light intensities. The elements or products (2) thin are stacked in a tray (4) and are set so as to have the long edge to the top of the tray (4). The products (2) to be counted are arranged side by side, without limitation a front face of a product against a reverse side of another product. Figures 7 to 11 show a view of products (2) thin stacked side by side, the tray (4) being shown under the stack (5). The products (2) thin are therefore placed on their edge, oriented transversely in the tray (4), that is to say parallel to the short sides of the tray (4) rectangular. In the customization card example, a stack contains up to 500 cards. The counting device detects the slice of each product (2) and thus determines the number (N) of products. An example of data processing is the detection of the variation of brightness. In FIG. 8, the data translated in the form of a graph represent the brightness as a function of the position. In this example, a maximum will be the value of an electrical signal corresponding to a received high intensity light signal, with respect to the adjacent signals. Similarly, a minimum will be the value of an electrical signal corresponding to a light signal received of low intensity, relative to adjacent signals. In a nonlimiting manner, a maximum can be interpreted by the treatment program as the medium of a product (2) to be counted and a minimum is interpreted as the joining of two products (2) to count. The junction between two products (2) thin is indeed darker and the middle of a thin element is lighter.

After the data processing, the counting device can indicate the number of thin products (2) in a series. Thanks to the memorization of information provided by the operator, concerning the nature of the products, the device associates with each series the nature of the products. Thus, in the further processing of the stack, another processing system downstream of the processing chain receives data specifying the nature of each product (2) and can therefore determine the customization or verifications to be performed. The downstream processing system communicates with the processing means of the counting device by means of communication, in a known manner. The communication means comprise, for example, a wired link or infrared or radio wave and communication interfaces adapted to the type of connection. According to one variant, the communication means are mediums, such as diskettes or disks, associated with readers of these mediums. The type of customization to operate is also taken into account. This treatment is therefore done automatically, directly by inserting the tray or the magazine containing the stack (5) into the processing system, or by transferring the stack (5) to another support. A check can be made by comparing the number (N) found by the device for the products of the complete stack (5), with a number of products provided by a product series management device (2).

The number of products (2) in each series is deduced from these results. The operator knows the nature of each small series comprising the stack and thus determines the nature of each product (2) at a given position. In the case where the thin stacking products (2) all have the same format and are processed by a personalization machine, the entire stack can be processed directly, information additional nature of the series that can advantageously be provided to the personalization machine. The personalization machine has processed a total of N elements, the processing performed being a function of their position in the stack (5). An alternative embodiment, as shown in FIG. 11, comprises at least one transverse CIS module (3t) producing a transverse illumination, for example perpendicular to the longitudinal direction of the stack (5). The module (3t) transverse CIS comprises detection means and lighting means in a transverse plane beam which illuminates transversely the stack (5). The module (3t) transverse CIS placed vis-à-vis the stack (5) performs the analysis of the illuminated transverse linear zone. The analysis of the entire length of the stack (5) is performed by a displacement (M3t) of the transverse module, in the longitudinal direction of the stack (5). The longitudinal displacement (M3t) of the transverse CIS module (3t) is performed at a determined speed. The photosensitive cells of the transverse module transform the light energy of the rays reflected by the stack (5) and focused on photosensitive cells of the detection means, into electrical signals which are the image of the light intensity. The processing means of the counting device sample these signals and convert the analog values of the electrical signals into image computer codes of these analog values, placed in the storage means. When the transverse CIS module has covered an area comprising the entire length of the stack (5) by its lighting means associated with its detection means, the stack (5) has been analyzed over its entire length and over a zone. of determined width. The two-dimensional analysis thus makes it possible to perform several longitudinal analyzes on the stack (5). The longitudinal analyzes are performed along lines (T1, T2) near or far (T1, T3) of several millimeters. It should be obvious to those skilled in the art that the present invention allows for many embodiments of the invention. other specific forms without departing from the scope of the invention as claimed.

ANNEX

Fourier Transform (FT) The Fourier transform of the signal (real or complex) s (n), n = 0..N - \ is denoted by S (n), n - 0..N - \. It is obtained by the following relation:

/ V-I

S (n) = Σ s (k) exp (-2πjnk I N)

A = O This transformation evaluates the frequency content of a signal.

Fast Fourier Transform (FFT) An algorithm to compute the Fourier Transform of a signal faster was developed by Cooley and Tuckey in 1965. This processing is faster, but only works if the signal size is a power. of 2. This algorithm is called Fast Fourier Transform.

Inverse Fourier Transform (IFT)

This transformation makes it possible to recover a signal s (ή) from its Fourier transform S (n). Its formula is the following: s (n) = Σ S (k) exp (2qnk I N)

A = O

Fast Inverse Fourier Transform (IFFT)

As for the simple Fourier transform, there is a fast algorithm for calculating the inverse Fourier transform.

Two-dimensional Fourier Transform (FT2D)

Let a two-dimensional signal s (m, n), m = 0..M-1 Λ = 0 .._ V - 1,

There is a definition of the Fourier transform for this signal: As for a 1-D signal, we can define fast transforms and

Inverse associated with this transformation.

Claims

1. Device for counting series of products (2) thin, stacked side by side, in a determined direction in a holding means (4), the products (2) thick stacked being all thickness (e) identical and constituting a stack (5), the device comprising at least:

- means (7) for illuminating the stack (5) producing one or more light beams (6, 7a) covering at least the entire length of the stack (5), - means (8, 9a, 9b) ) comprising at least one detection circuit, comprising a plurality of photosensitive elements, and at least one optical device, associated with the detection circuit, for focusing light rays reflected by the stack (5),

- Storage means, characterized in that it comprises processing means (10) receiving signals from the detection circuit or circuits, able to extract from these signals brightness levels in correlation with a dimension along the axis stacked in pixels, the processing means (10) generating a determined signal x (n) corresponding to the received signals and including:

extraction means for extracting, from the determined signal x (n), a pattern (M2) representing a thin product (2); and

calculating means for calculating the number of thin products, by intercorrelation of the determined signal with the extracted pattern, to determine an intercorrelation signal corresponding to the number of patterns present and corresponding to the number (N) of products (2) slightly thick of the stack (5).

2. Device according to claim 1, wherein the processing means (10) also comprise: pretreatment means for carrying out a Fourier transform allowing to provide from the received signals a transformed signal revealing harmonics and then to determine the characteristics of a filtering means for filtering the transformed signal with conservation of at least 1 harmonic ; said determined signal x (n) being a filtered signal resulting from the pretreatment.

3. Device according to claim 2, wherein the pretreatment means comprise reconstitution means performing an inverse Fourier transform on a filtered transformed signal supplied by said filtering means, in order to deliver a preprocessed signal corresponding to the determined signal.

4. Device according to claim 3, wherein the extraction means are arranged to extract the representative pattern of a product (2) thin in the pretreated signal.

5. Device according to claim 3 or 4, wherein the means for extracting a pattern comprise:

means for parameterizing the thickness determining the first harmonic in the Fourier transform of the received signals and the corresponding thickness of the product; first calculation means for performing correlation or convolution functions on the preprocessed signal in a first time, then a Fourier transform calculation to estimate in a second time, for each of the frequencies of the Fourier domain, the module and the argument of the Fourier transform of the pattern representing the periodic signal position corresponding to a product (2 ) thin; and

second calculation means using an inverse Fourier transformation to calculate said first pattern (M1) from results obtained by the first calculation means.

6. Device according to claim 5, wherein, the first calculation means develop an autocorrelation function c (τ) of the filtered signal x (n), defined by the formula:

c (τ) = Σ x (η) x (τ + ή), τ = [θ, Ep - 1]

where N is the number of pixels in the image of the filtered signal, x (n), n = [0 ...

N-1] is the filtered signal and Ep is the thickness of a thin product (2) expressed in pixels.

7. Device according to claim 5, wherein the first calculation means develop a convolution function conv (τ) of the filtered signal x (n) on itself, defined by the formula:

COHv (Y) = Σ x (n) x (τ - n), τ = [θ, Ep - 1]

where n is the number of pixels of the image of the filtered signal, x (n) is the filtered signal and Ep is the thickness of a thin product (2) expressed in pixels.

8. Device according to one of claims 5 to 7, wherein the first calculation means are arranged to calculate the transform of

Fourier of the autocorrelation function c (τ) of the filtered signal x (n), in order to determine the modulus of the Fourier transform of the periodic signal portion.

9. Device according to one of claims 5 to 7, wherein the thickness parameter setting means Ep thin products (2) determine the thickness in pixels and the first calculation means realize, for a first half of the frequencies of the plurality of frequencies, in order to determine the argument of the Fourier transform of the periodic signal portion, an estimation of the values of the argument functions 6> _m (/) for f = [0, N-1] with N = (Ep + 1) / 2 if N is odd or N = Ep / 2 + 1 if N is even, where θ _m (f) is an odd and Ep-periodic function, where Ep is the thickness of a thin product (2) expressed in pixels; this estimation being carried out by means of n-correlation of order greater than 2 arranged to: - use a 2-variable operator whose definition is as follows: b (τ _ι , τ ₂ ) = Σx (n) x ( _τ ι + n) x (τ ₂ + n) for r, = [0, I _/? -l] and τ ₂ = [0, Ep-X] where n is the number of pixels of the image of the filtered signal and x (n) is the filtered signal;

compute the Fourier transform of the n-correlation function b (τ1, τ2) in the Fourier domain, via a two-dimensional Fourier transform, to obtain a matrix set of linear relations expressing the arguments of the function of n correlation according to the arguments of the pattern in the frequency domain of Fourier; and

- invert the system to go back from the n-correlation argument to the argument of the pattern in the Fourier domain.

10. Device according to claim 9, wherein the thickness setting means comprises means for estimating the thickness Ep with the aid of a first Fast Fourier transformation FFT, the estimation means realizing: a calculation of the FFT transform and its module;

a localization of the fundamental by a search of maximum on the module of the FFT transform, while in the Module vector, of size N, the position of the fundamental is noted Xfonda;

a calculation of the thickness Ep, taking into account that the position of the fundamental corresponds to a thickness Ep expressed in pixels: Ep = N /

Xfonda; and

- a rounding of the value found for Ep to the nearest integer value.

11. Device according to one of claims 5 to 10, wherein filtering means are provided to provide the extraction means a filtered and denoised signal, the second calculation means for determining a first periodic pattern (M1) representative of a product (2) thin with a possible near phase shift.

12. Device according to claim 11, wherein the extraction means execute at least one signal processing algorithm denoised to determine the signal pattern serving for intercorrelation, the shape of the pattern retained for a series of products (2). in the process of counting being estimated after a comparison between the first periodic pattern (M1) determined in the denoised signal and a reference pattern (Mref) stored in the storage means.

13. Device according to claim 12, wherein the parameterization means associated with the processing means (10) are provided for storing the reference pattern (Mref) during a count performed by the counting device with a standard batch of products. (2) thin.

14. Device according to one of claims 1 to 13, wherein the filtering means is a comb filter configured to filter out in the received signals, noise and frequencies not corresponding to harmonics, to obtain a pretreated signal in which frequencies far from the harmonics and which may correspond to gaps or spaces between the thin products (2) are eliminated.

15. Device according to one of claims 5 to 14, wherein the signal pattern extraction means (M2) include circular registration means to avoid obtaining a pattern offset by phase shift, the circular resetting means reproducing from the first pattern (M1) patterns with different phase shifts, the finally applied phase shift being determined by using a reference pattern (Mref).

16. Device according to one of claims 5 to 13 or 15, wherein the means for calculating the number of thin products (2) comprise:

means for calculating intercorrelation between the extracted signal pattern (M2) and the denoised signal, making it possible to supply the intercorrelation signal (C2); and

pattern counting means in the denoised signal, by detecting local maxima of the intercorrelation signal (C2).

17. Device according to claim 15 or 16, wherein the circular registration means comprise: - means for determining, from the first pattern (M1), patterns (m) with different phase shifts;

means for calculating a scalar product for calculating, for the various units (m), scalar products with the reference pattern (Mref); and - comparison means for determining a maximum of the scalar products calculated, the phase shift applied finally corresponding to that for maximizing the dot product with the reference pattern (Mref).

18. Device according to one of claims 1 to 17, wherein the processing means (10) generate a vector representative of the signals received and perform a FFT fast Fourier transform on this vector, the filtering means receiving the fast Fourier transform of this vector and realizing a frequency Fourier filtering after a harmonic determination.

The apparatus of claim 18, wherein said vector is generated by a program executing a "zero-padding" zeros filling method for said vector to correspond to an increased signal size and includes a number of N _zp samples of signals, N _zp being a power of 2, the program being provided with a function of deletion of the added zeros, this deletion function being activated to enable to obtain said filtered signal after application of the IFFT inverse fast Fourier transform.

20. Apparatus according to claim 16, wherein the intercorrelation calculating means calculates the correlation I (n) between the estimated pattern word (k), of size Ep, and the denoised signal x (k), of size N , using the following formula: k = Ep- \ Ep

For n = / («) = Σ word (k) .x (n - ^ - + k)

2 ^"£ 2 2 = 0 where N is the number of pixels of the image of the denoised signal, x (k) is the noise-suppressed signal and Ep is the thickness d ^ a product (2) thin in pixels.

21. Device according to one of claims 1 to 20, wherein a module (3, 3d) CIS, arranged longitudinally and vis-à-vis the stack (5) constitutes the lighting means and the detection means, the module (3, 3d) CIS being of length at least equal to that of the stack (5), or the module (3, 3d) CIS making displacements in the longitudinal direction of the stack (5) facing an area covering at least the entire length of the stack in several steps (PO1, PO2, PO3).

22. Device according to one of claims 1 to 20, comprising a plurality of CIS modules, arranged longitudinally and vis-à-vis the stack (5), each CIS module comprising detection means and lighting means by a plane beam according to the determined direction, the sum of the lengths of the CIS modules being at least equal to the length of the stack (5).

23. Device according to claim 22, wherein the CIS modules illuminate the stack (5) along a line (T) lighting, each CIS module being inclined at a given angle so that its beam plane lighting meet this trait (T).

24. Use of the device according to one of claims 1 to 23, characterized in that information is transmitted, via communication means, by the processing means (10) to a control system. processing, of type personalization machine, downstream of a processing chain, the transmitted information comprising the number (N) of products (2) thin calculated by the device for each series constituting the stack (5) and / or information making it possible to deduce this number (N) and / or an identifier associated with each series.

25. Use according to claim 24, wherein the processing system customizes the series products (2), physical or software personalization operations to be applied to each element of a series being associated with the information transmitted by the processing means.

26. Use of the counting device according to one of claims 1 to 23, characterized in that a logical customization station, processing a series of products (2) thin with an integrated circuit, allows the inscription in memory of the circuit integrated, customization information for the use for which the product is intended.

27. A method of processing at least one signal from the detection circuit or circuits of a device according to the preamble of claim 1, characterized in that it comprises:

a step (51) of preprocessing said signal, including a filtering of the signal to produce a filtered signal;

a step (52) for estimating in the filtered signal a representative pattern of a thin product (2);

a step (53) for calculating intercorrelation information between the estimated pattern and the filtered signal, for detecting patterns present in the filtered signal; and

a step (54) of signaling, via an interface of the device, information representative of the number (N) of thin products (2) processed by the device, by counting maxima detected in the intercorrelation information.

28. The method of claim 27, wherein the filtering during the step (51) of preprocessing said signal is performed after a Fourier transform and using a comb filter.

The method of claim 27 or 28, comprising a step (50) of converting the signal, prior to filtering, into data representative of brightness levels in correlation with a stack thickness dimension expressed in pixels, the estimation step (52) defining a first periodic pattern representative of a thin product (2) with a possible phase shift, and then using a reference pattern (Mref) to perform a circular registration to obtain a second pattern estimated without phase shift.

30. Method according to one of claims 26 to 29, wherein the step (54) signaling comprises a display of a number of smart cards to be processed by a chip card personalization machine and / or a transmission of the chip. representative information of this number to the personalization machine.

31. A computer program directly loadable into the memory of a computer and including computer codes for controlling the steps of claim 27, 28 or 29 when said program is run on a computer, said program thus allowing counting of sets of thin products (2) of a stack (5).