EP3234915A2

EP3234915A2 - Method for detecting stipes in a tyre

Info

Publication number: EP3234915A2
Application number: EP15821143.3A
Authority: EP
Inventors: Alexandre Joly; Régis VINCIGUERRA; Alexandre CHARIOT
Original assignee: Michelin Recherche et Technique SA Switzerland; Compagnie Generale des Etablissements Michelin SCA; Michelin Recherche et Technique SA France
Current assignee: Compagnie Generale des Etablissements Michelin SCA; Michelin Recherche et Technique SA France
Priority date: 2014-12-19
Filing date: 2015-12-16
Publication date: 2017-10-25
Also published as: US20170330338A1; WO2016097598A2; JP2018501480A; WO2016097598A3; WO2016097598A4; CN107111873A

Abstract

In this method for referencing stripes present in numerical representations (10) of tyres, automated means perform the following steps in order to reference the types of striation: - determining at least one representation comprising one type of stripe (1, 2) to be referenced, - identifying at least one segment of pixels or voxels in the representation (10), and - recording at least a value relating to differences between the grey scale levels or colour levels of pixels or voxels in the segment.

Description

Method of detecting streaks in a tire

The invention relates to the detection and referencing of streaks in images.

It seeks to detect streaks in particular in tire images, so as to locate the areas of tires where information is etched. One can also seek to locate these areas to check later that they have no defect. A striated zone is such that it presents a pattern, for example a shape, a line or a curve, regularly repeated in a given direction

Streak detection methods are known in images in which a spectral approach is used to detect streak frequencies. For this purpose, Fourier filtering is generally used. The Fourier transform of the image to be studied is carried out. In the image obtained, which represents the frequencies of the image studied in the Fourier space, peaks are obtained which correspond to different frequencies of gray levels or colors of the image. We thus find frequencies corresponding to streaks of the initial image, and we deduce the presence and location of these streaks in the initial image. However, this approach is expensive in computing time, especially on large images whose Fourier transform must be calculated. It is moreover rather delicate and imprecise, in particular because it is complicated to separate the frequencies corresponding to streaks frequencies corresponding to background noise. Indeed, the frequency peaks corresponding to streaks are rarely clearly defined in the image obtained by the Fourier transform.

Another type of streak detection method in an image consists of comparing the studied image with so-called reference images comprising streaks, and calculating a correlation rate between the images. The major disadvantage of this type of method is that it requires a very large memory to hold the reference images and a very important computation time to compare the image portions between them and determine a correlation rate.

An object of the invention is to provide a method that is less expensive in computing time and in memory, and is simpler, more accurate and more reliable than the aforementioned methods.

For this purpose, a method is provided for referencing streaks present in digital tire representations, in which automated means perform the following steps in order to reference types of streaks:

determining at least one representation comprising a type of streaks to be referenced, identifying at least one segment of pixels or voxels of the representation, and

- record at least one value relative to differences between levels of gray or pixel colors or voxels of the segment.

Thus, the or each recorded value is then used as a control to which one or more values measured on a studied image are compared, in order to determine whether the image studied comprises streaks or not. The segmental approach makes it possible to reference values which relate to a series of successive pixels, instead of studying the image pixel by pixel for example. This approach is particularly well suited when the segment is perpendicular to streaks, the values relating to differences between the gray levels or colors then highlighting the variation of pixel levels. This method is independent of the type of streak detection method used later. The same observations are worth considering voxels rather than pixels. The same goes for the rest of the time, each time we consider pixels.

The digital representations on which are implemented a method according to the invention can be of three types:

- representations called "2D", which correspond to two-dimensional images in which each pixel carries a luminance information,

Representations called "2.5D", which correspond to two-dimensional images in which each pixel carries depth information,

- Representations called "3D", which correspond to volumes in three dimensions in which each voxel carries a luminance information.

In the case where the acquisitions used present information of the "relief" type, each pixel of the image carries topographic information of depth of the streaks. The level of gray or color corresponds to this depth of the streaks. In the case where the acquisitions carry a luminance information, the level corresponds, for example, to the contrast between the background and the top of the streaks.

Advantageously, the value or at least one of the values is chosen from the following group:

an average period of periods relating to pixels or voxels of the segment,

an average of absolute values of differences between gray levels or colors within each pair of pixels or adjacent voxels of the segment, and

preferably also a length determined from the average period.

Thus, the average serves as a control to determine if a segment of pixels of a studied image is in a potentially streaked zone or not. Indeed, the more streaks there are, the more gray level pixels are next to each other, and the higher the average difference in levels between adjacent pixels. The average period makes it possible to establish an average interval of distance between the vertices of the streaks or between the valleys of the inter-striations. Finally, the length makes it possible to compare segments large enough to contain relevant information about the streak area and small enough that the calculation of the other reference value (s) is not too time-consuming. The length of the segment is advantageously fixed at 3.5 mean periods. These three combined parameters, period, average, and length, make it possible to reference types of striations. Indeed, one can have, in a zone of striations, fine and numerous striations, large and few, or other types of striations. Each streak type therefore corresponds to a value or a combination of values of the three reference parameters.

There is also provided a method for controlling a tire, in which, to locate a zone of grooves in a digital representation of a tire, automated means perform the following steps:

consider at least one pixel or voxel of a zone of the representation and,

for the or each pixel or voxel considered:

identifying a segment of pixels or voxels centered on the pixel or voxel considered, determining at least one value relating to differences between gray levels or colors of pixels or voxels of the segment, and

- Compare the value or values to one or predetermined thresholds.

Here again, the segmental approach makes it possible to determine values that relate to a sequence of successive pixels, instead of studying the image pixel by pixel. This approach is therefore particularly well suited when the segment to be studied is perpendicular to streaks, the values relating to differences between the gray or color levels then highlighting the variation of pixel levels. Nor is it necessary to seek to detect frequency peaks in an image transformed by complex calculations, for example by Fourier analysis, since one is directly interested in the image to be studied in order to detect the streaks. In addition, by moving from a pixel considered to a pixel considered adjacent, the segment centered on this pixel comprises values relating to differences in levels of pixels or voxels already calculated previously. It is thus possible to calculate values relating to differences within several rapidly overlapping segments by factoring the calculations.

Advantageously, the value or at least one of the difference values is an average of absolute values of differences in the levels within each pair of adjacent pixels or voxels of the segment.

Thus, the average of the absolute values of the differences makes it possible to determine whether the segment is in a strongly streaked zone or not. This value is simple and quick to determine, and we remove the pixels that are not part of a streaked area. For example, if the average difference is small, it means that the segment, and therefore the pixel located at its center, is in a rather homogeneous area in terms of gray levels or colors. On the other hand, if the average difference is high, it means that by traversing the segment of a pixel located at one end to the pixel located at the other end, the levels of gray or colors vary considerably. In this case, it is considered that the pixel located in the center of this segment may be included in a streak zone, and it is not eliminated.

Preferably, the value or at least one of the difference values is an average period of periods relating to the pixels or voxels of the segment.

Thus, the average period represents the average distance between, within the segment, two identical changes of values of adjacent pixels. This corresponds to the average interval between two peaks of streaks of the segment, or between two inter-strikeths.

Advantageously, the value or at least one of the values relating to the differences is a number of periods relating to pixels or voxels of the segment.

Preferably, the value or at least one of the values relating to the differences is one or more periods relating to pixels or voxels of the segment.

Thus, for example, each period, for example an interval between two peaks of streaks, can be compared to a predetermined value.

Advantageously, the automated means associate a binary value "0" or

"1" to each pixel or voxel of the segment depending on its level, and the value or at least one of the difference values is relative to changes between values within adjacent pairs of pixels or voxels when the automated means traverse the segment in one direction, these changes being identical and preferably the first pixel of each pair comprising a value identical to that of a pixel or a voxel of the segment located at an end of the predetermined segment as a function of the direction.

Thus, the segment is binarized so as to distinguish only two types of pixels: those located in a strip, those located between two stripes. All calculations of values relating to differences between gray levels or colors of pixels or voxels of the segment, and in particular the calculation of periods, are then simplified.

Moreover, by making the computation of values relative to the pixels or voxels of the binary value of the first pixel or voxel of the segment, it is considered, when calculating a value relative to the differences in levels, that is an interval between two vertices of streaks, an interval between two inter-strikeths, but not both at the same time. In this way, the calculation time is further reduced.

Advantageously, to associate a binary value with a pixel of the segment, an average value of the values of the pixels or voxels of the segment is determined;

each pixel or voxel of the segment is assigned a binary value as a function of the difference between its value and the average numerical value.

Thus, for example if the value of the pixel or voxel is greater than or equal to the average value, it is associated with the binary value "1", otherwise "0".

Preferably, the automated means associates a binary value "0" or "1" with each pixel or voxel of the segment as a function of its level, and the value or at least one of the values relating to the differences is relative to changes between values within pairs of adjacent pixels or voxels when the automated means traverses the segment in one direction, these changes being identical and preferably the first pixel of each pair comprising a preference value different from that of a pixel or a voxel of the segment located at one end of the predetermined segment depending on the direction.

Thus, the secondary periods represent either an interval between two inter-striketholes, or an interval between two vertices of streaks, but not both, in the same way as the values defined above and complementary to them. Thus, if the values defined above concern an interval between two vertices of streaks, the secondary periods concern the intervals between inter-striketholes, and vice versa.

Advantageously, the segment is composed of at least three consecutive pixels or voxels of the representation.

There is also provided a method of checking tire compliance, wherein automated means perform the following steps:

determining at least one expansion of a basic representation comprising at least one striation zone of a tire so as to obtain an expanded representation;

determining at least one erosion of the basic representation so as to obtain an eroded representation; and

determining a difference between the dilated representation and the eroded representation so as to obtain a difference representation.

The dilation allows to obtain in the dilated image, in place of the striation zone of the base image, a smooth streak zone, the intervals between the streaks having been erased by the expansion. The erosion makes it possible to obtain in the eroded image, instead of the striation zone of the base image, a zone of intervals of smooth striations, the streaks being erased by erosion. Thus, in a case where the streaks of the basic representation are perfect, the difference representation must comprise a perfectly homogeneous zone at the same place as the position of the streak zone in the basic representation. In practice, if the streak area does not include a defect, then the difference representation includes an area corresponding to the streak area, whose gray or color levels are substantially constant, within a noise tolerance range. In the opposite case, then the difference representation comprises one or more zones whose gray or color levels are very different from those of the surrounding pixels.

There is also provided a computer program comprising code instructions able to control the implementation of the steps of the method according to the invention when it is executed on a computer.

Finally, there is provided according to the invention a streak control device in tire representations, capable of implementing a method as described above.

According to another aspect of the invention, an aim is to provide a method for analyzing the conformity of tire grooves which is less expensive in terms of calculation time and faster to implement.

For this purpose, a tire conformity control method is provided, in which automated means perform the following steps:

Such a method does not require the use of a reference, and in this it has the advantage of being simpler to implement than known methods. Preferably, the automated means determine one or more structuring elements of the expansion and erosion as a function of a dimension of the streaks, a gap between the striations and / or a striations orientation.

Thus, the structuring elements are adapted to each type of streaks detected upstream. This makes it possible to carry out the most relevant expansion and erosion operations possible, by deleting the streaks and the intervals between the striations in the most correct manner, while preserving the other elements.

Advantageously, the automated means determine at least two dilations of the basic representation with different respective structuring elements, to obtain the expanded representation. Preferably, the automated means determines at least two erosions of the basic representation with respective different structuring elements, to obtain the eroded representation.

Thus, for complex shapes such as streaks comprising several orientations or different thicknesses within the same zone of striations, the striations are separated into different types of streaks, and the dilation and / or erosion operations are repeated. for each type of streak, with structuring elements adapted to the different types of streaks of the zone.

Advantageously, digital values of pixels or voxels of the difference representation are compared with at least one predetermined threshold.

Thus, if some of the values of the pixels or voxels are far from the threshold, it is considered that the streaks of the basic representation comprise a defect. On the other hand, if all the values are within a predetermined interval relative to the predetermined threshold, it is considered that the striation zone of the basic representation does not include a defect, and that the tire therefore conforms.

Preferably, the threshold or at least one of the thresholds is a median of values of the pixels or voxels of the difference representation.

Advantageously, the basic representation does not include any color other than black, white and gray levels.

But the basic representation can also include black, white and gray.

Preferably, in advance, to locate a zone of grooves in a digital representation of a tire, automated means perform the following steps:

considering at least one pixel or voxel of an area of the representation and, for the or each pixel or voxel considered:

identify a segment of pixels or voxels centered on the pixel or the voxel considered,

determining at least one value relating to differences between gray levels or colors of pixels or voxels of the segment, and

- Compare the value or values to one or predetermined thresholds.

Thus, the segmental approach makes it possible to determine values that relate to a sequence of successive pixels, instead of studying the image pixel by pixel. This approach is therefore particularly well suited when the segment to be studied is perpendicular to streaks, the values relating to differences between the gray or color levels then highlighting the variation of pixel levels. It is also not necessary to try to detect peaks of frequencies in an image transformed by complex calculations, for example by Fourier analysis, since one is directly interested in the image to be studied in order to detect the streaks. In addition, by passing from a pixel considered to a pixel considered adjacent, the segment centered on this pixel comprises values relating to differences in levels of pixels or voxels already calculated previously. It is thus possible to calculate values relating to differences within several rapidly overlapping segments by factoring the calculations.

There is also provided a method for referencing streaks present in digital tire representations, in which automated means perform the following steps in order to reference types of streaks:

determining at least one representation comprising a type of streaks to be referenced,

identifying at least one segment of pixels or voxels of the representation, and

- record at least one value relative to differences between gray levels or colors of pixels or voxels of the segment.

Thus, the or each recorded value is then used as a control to which one or more values measured on a studied image are compared, in order to determine whether the image studied comprises streaks or not. The segmental approach makes it possible to reference values which relate to a series of successive pixels, instead of studying the image pixel by pixel for example. This approach is particularly well suited when the segment is perpendicular to streaks, the values relating to differences between the gray levels or colors then highlighting the variation of pixel levels. This method is independent of the type of streak detection method used later.

There is also provided a computer program comprising code instructions adapted to control the implementation of the steps of the conformity checking method according to the invention when it is executed on a computer.

There is also provided a tire conformity control device capable of implementing one of the previously described methods.

Finally, there is provided a computer-readable storage medium comprising a program according to the invention in registered form.

Preferably, the device comprises a recording medium comprising a database of values relating to streaks.

We will now describe an embodiment of the invention by way of non-limiting example and in support of the accompanying drawings in which:

- Figures 1 and 2 illustrate digital images containing streak areas; FIGS. 3 to 5 schematically illustrate respectively a digital image, a segment of this image, and the segment in binarized form; FIG. 6 illustrates a method according to one embodiment of the invention;

FIGS. 7 to 11 schematically illustrate respectively an image, a segment of the image, the segment in binarized form, another segment of the image and this segment in binarized form;

FIG. 12 illustrates a method according to another embodiment of the invention;

FIGS. 13 to 16 schematically illustrate a digital image, the image in eroded form, the image in dilated form, and a difference image between the dilated and eroded images;

FIGS. 17 and 18 respectively illustrate a digital image comprising a zone of streaks having a defect and the difference image resulting from this image according to one embodiment of the invention, and

- Figure 19 illustrates a device adapted to implement a method according to the invention.

The tire control method aims at creating a tire image database to reference types of streaks and then to detect streaks similar to the types of streaks referenced in test images. The streak conformance check method aims to check whether tire striation areas have defects. I SEO process

The method first consists of referencing streak types and then detecting streaks in images using the referenced streaks.

Figures 1 and 2 illustrate different types of streaks in two-dimensional images 10 and 20. These types of striations differ from each other in the thickness of the striations, their orientation, their straightness, the interval between the striations, as well as the gray levels of the striations and the intervals of striations of each type. Figure 1 also proposes two zones 1 and 2 of different types of streaks. These are all types of streaks that we want to reference at first, and detect in a second time when we find these streaks in an image.

The steps of the different embodiments that will be presented are executed by automated means 91 belonging to a device 90, which notably comprises a processor 94, a memory 95 and is connected to a database 92. These elements are illustrated in FIG. In order to execute the method, the device applies a computer program. This program can request as input an image or a series of images including zones of streaks to be referenced, as well as an image or images to be studied. In output, it provides the user with data on each type of reference streaks, as well as the types of streaks determined and their location in the images to be studied. In addition, the same or a separate program allows for the application of a compliance check method described below. It then requests an image comprising a streak zone and outputs a so-called "difference image" image, as well as data concerning pixels representing possible defects. The input image may be provided automatically by the method itself when it has detected streaks in an image. Thus, the same program makes it possible to determine streaks in an image of a tire, and at the same time to determine whether or not these streaks have defects.

In addition, this program can be made available on a telecommunications network, such as the web, or an internal network, so as to allow its download by a user.

Similarly, the program or equivalent instructions may be recorded on a computer-readable storage medium 93 such as a hard disk, a USB key, a CD, or any other equivalent medium, which may include the database.

In order to perform the referencing of streak types, "reference" images comprising streak zones such as images 10 and 20 of FIGS. 1 and 2 are selected to construct a reference base. The more reference images are available in the database, the more different types of streaks will be referenced, and therefore the more different types of streaks can be detected in study or tire test images. This reference base may include any image comprising a striated zone, even not explicitly described in the present application.

In this case, we consider the schematic image 30 of Figure 3 comprising vertical streaks 3. In the streak area, we select a segment 4 of pixels. It is called "reference segment". Each pixel of the image 30, and a fortiori of the reference segment 4, includes a gray level value. In concrete terms, a reference segment 4 of 21 pixels is selected. One could have selected a reference segment comprising another number of pixels. This number corresponds to a reference segment large enough to intercept several streaks and small enough that the calculations described below are not too time-consuming. Once the reference segment 4 has been selected, the following steps are performed:

1) calculating the gray level differences, in absolute value, between each pair of adjacent pixels of the reference segment 4. Thus, on the segment 4 of FIG. 4, which represents on a large scale and schematically the segment of reference 4 of FIG. 3, the difference in absolute value between the level of gray of the pixel 6 and the gray level of the pixel 7, then the difference between the pixel 7 and the pixel 8, and so on.

2) We sum these differences, divided by the number of pixels of the reference segment, minus one unit, that is to say in this case by dividing by 20, so as to obtain the average of the differences of gray levels between each pair of adjacent pixels in the segment. This average is recorded as a "reference average" in a database.

3) An average of the gray levels of the pixels of the reference segment is calculated.

4) Within the reference segment, the pixel values are binarized according to the average of the previously calculated gray levels. Thus, if a gray value of one pixel equal to or exceeds the average of the gray levels of the reference segment, the corresponding pixel is assigned the value "0". If a gray value is below average, the corresponding pixel is assigned the value "1". A segment 50 is thus obtained, illustrated in FIG. 5. It is from this segment 50 that the calculations described below are carried out:

5) Distances, called principal periods, of the binarized pixel segment 50 are determined. A main period is the shortest distance, in number of pixels, between two changes between values within adjacent pairs of pixels when traversing the segment from left to right, these changes being identical and the first pixel of each pair comprising a value identical to that of the first pixel of the segment at the left end. Thus, in FIG. 5, the first pixel 14 located at the left end has the binary value "1". The first change is therefore sought between a binary value pixel "1" and a binary value pixel "0". This change takes place between the pixels 15 and 16. The second identical change is then sought, that is to say between a pixel of binary value "1" and a pixel of binary value "0", by traversing the segment of left and right. This change takes place between pixels 17 and 18. This gives the main period 11, formed of eleven pixels. In the same way, the next one or more main periods 12 in the segment are obtained. One could perform a calculation of the same type by traversing the segment from right to left. The first pixel of the segment whose binary value is observed would then be the first pixel located at the right end of the segment.

6) The average period of the main periods of the segment is then calculated and recorded in the database. It will be called the "average reference period" thereafter.

7) One sets the "reference length" of a segment. It is fixed in this case at 3.5 times the average reference period. We could have chosen a number other than 3.5, knowing that this number must remain strictly greater than 1.

With the above steps, the type of streaks of FIG. 3 was entered as a reference in the database. The three data entered for this type of streaks, namely the reference average, the average reference period, and the reference length, must make it possible to detect this type of streaks in any image to be studied, if these streaks are present.

Il The Streak Detection Process

We are now studying the detection of streaks in an image, that is to say the method that makes it possible to detect and locate streaks in a given image, by comparing them with the streaks referenced by the three data recorded for each type of streak, as explained previously. If a large number of types of striations have been referenced, each type of streak referenced can be compared to the values that will be determined during the detection. For this purpose and with reference to FIG. 6 which illustrates a method according to a preferred embodiment of the invention, the following steps are performed for a given type of streaks:

A) In an image to be studied, in this case image 60 of FIG. 7, a pixel 61 is selected. A 21-pixel study segment 62 centered on the pixel 61 is determined. This segment is illustrated in detail in FIG. 8. In the same way as in steps 1) and 2) of the referencing method, the average of the absolute values of the differences of levels within each pair of pixels of the segment Study 62. The result is then compared to a "reference average" of a type of stria recorded using the referencing method, a type of striation to which the image to be studied is to be compared. For this purpose, the average calculated on the study segment 62 of the image 60 is compared with a predetermined value interval centered on the "reference average" of the type of streak considered. If the average calculated on the study segment is in the range, then the segment is subjected to step B). If the result is not included in the interval, then another type of referenced streaks to which the study segment 62 is to be compared is compared, and step A) is repeated for the new referenced streak type considered. . This amounts to using a high threshold and a low threshold on both sides of the reference average and to compare the result with these thresholds.

If the result is not included in any value range for all referenced streak types, then this means that pixel 61 does not belong to any type of referenced streak. We stop any test for the pixel 61, and we can start the process again with another pixel.

This criterion removes a large majority of bad pixels, leaving only the pixels on areas a minimum textured, but not necessarily still streaking.

B) The segment is binarized in the same way as in step 4) of the referencing method, and the same calculations are performed as in steps 5) and 6). Thus, we obtain an average period of the main periods on the study segment 62, represented schematically by the binarized study segment 63 of FIG. 9. This average period is then compared with the reference period recorded for the type of streaks considered successfully in step A). In the same way as before, this comparison is carried out by a range of values centered on an "average reference period". If the average period of the segment 63 belongs to the range of values, then the pixel 61 and its binarized study segment 63 go to step C), otherwise another type of streaks is selected and the method is repeated. step A), for the new type of streaks considered.

This criterion removes areas that do not look like the type of streaks searched, ie the types of streaks referenced.

C) The number of main periods of the binarized study segment 63 is compared with the "reference length" recorded corresponding to the type of striation considered, with a range of values centered on the value of the reference length, in the same way than in the previous steps.

This criterion mainly removes some bad areas near the edges of the texts, but also the lateral borders of zones of striations that could have been recognized and localized. These areas can be found later by binary morphology steps such as dilations or erosions.

If the study segment passes this step, proceed to step D). Otherwise, step A) is repeated with a new type of reference streaks.

D) We compare all the periods of the binarized study segment 63 to the reference period of the reference segment, again for the same type of streaks as in the previous steps. For this purpose, we take into account not only the main periods, but also secondary periods that correspond to the changes between values within pairs of adjacent pixels when the test segment is traversed in one direction, these changes being identical and the first pixel of each pair comprising a preference value identical to that of a pixel or voxel of the segment located at an end of the predetermined test segment depending on the direction. An example of a secondary period on a segment is the period 13 of the segment 50 of FIG. 5. Thus, each of these periods is compared with an interval predetermined values centered around the average reference period of the type of striations considered.

If all the primary and secondary periods are within the range, then the pixel 61 on which the study segment 62 is centered is considered to belong to the type of streaks for which steps A) to D) were performed.

Otherwise, the process is repeated in step A) with a new type of reference streaks.

If all types of reference streaks have been compared to the segment without the pixel successfully passing step D), it is considered that the pixel 61 of the study segment 62 does not belong to streak types that have been referenced, and we stop the process. It can be repeated in step A) with another pixel on which we will center another study segment.

In the present case, it is very likely that the pixel 61 of the segment 62 does not pass the step B), see is eliminated in step A), in view of the gray levels of the study segment, if we compare this segment only to a type of stria referenced similar to that of Figure 3. In addition, this segment has no main period or secondary period.

On the other hand, if the process is repeated with the pixel 64 of FIG. 7, and the study segment 65 centered around the pixel 64 is selected, it is likely that the latter passes the process to step D ) successfully compared to a referenced type of streak similar to that of Figure 3, in view of the gray levels and binary values of the study segment 65 seen in detail in Figure 10 and the study segment binarized 66 of FIG. A main period 67 and a secondary period 68 are furthermore determined. It is then considered that this pixel 64 is part of a striation zone similar to a streak zone of the type of FIG. 3 to which it has been compared.

Likewise, as soon as a pixel has successfully passed step D), the process is restarted with another pixel, for the same type of referenced streak.

In one embodiment, the method stops when all the pixels of the image have been considered, that is to say when all these pixels are passed at least through step A of the method.

In another embodiment, only a certain portion of the image, or some pixels of the image, is selected, and the method is applied only to these pixels.

For example, a user may have visually located an area that might contain streaks in an image, and decide to apply the process only to that area of the image.

In a variant, during step A), certain differences between pixels are recorded. Indeed, if we first perform calculations for a given pixel, then calculations for a pixel located on the same pixel line of the image, it is possible that their study segments include identical pixels. It is then useful to reuse in the calculation already calculated results.

It should be noted that step A) is independent of the other three steps because there is no need to binarize the segments to achieve it. This step is actually the simplest of all, that's why it's done first.

In another embodiment illustrated by the diagram of FIG. 12, as soon as a pixel is not admitted at a given step other than step A), instead of restarting the process in step A) with another type of streaks, the test of the same step is carried out with another type of streaks. In this way, a pixel can pass the test of step A) for a given type of streaks, pass step B) for another type of streaks given, and so on.

In another embodiment, the ranges of values that serve as a comparison are also stored in the database. They are not necessarily centered on the reference values such as the average reference period, the reference length or the reference average. They can understand these values without being centered on them. In this way, some variations from the reference values are tolerated in one direction but not in another.

In another embodiment, only one type of streaks or several types of particular streaks in the study segments are to be detected. This consists of comparing the data of the study segment with the referenced data concerning these types of striations and not with the other types of referenced streaks. III Method of checking conformity of striations

Once streaks have been located on an image of a tire, the conformity of these striations is studied. The purpose is to verify that the areas comprising streaks do not present defects that could alter the understanding of the symbols expressed by these streaks. This is called compliance control by vision. It is necessary to first locate the streaks for two reasons: the usual compliance checks of the other zones can not be applied to the streak zones, and the criteria for measurement and tolerance of defects in these streak zones can be different from other areas.

In a flat area, a fault is characterized by a higher or lower elevation than the average of the area.

In a zone of striations, the principle of the method in the present mode consists of filter the streaks in two different ways to obtain two images of flat areas: an image representing an average of the streaks, and an image representing an average of the vertices of the streaks. We then study an image resulting from the difference between the two aforementioned images, and which should contain pixels of relatively constant value in the streak zone. The defects are then visible when portions of the normally constant areas have "abnormal" values.

In this case, we want to know if the streaks of the image 70 of Figure 13 have defects.

For this purpose, an erosion of the image 70 is performed, so as to obtain an eroded image. The structuring element is chosen so that the streaks disappear in the eroded image. Thus, to choose the structuring element, one takes into account the interval between the striations, the orientation of the striations, and their size. Erosion takes place in gray level, one can also take into account the gray levels of the streaks and intervals. The eroded image 71 of Figure 14 thus represents an average of the bottoms of the streaks, in other words an average of the hollows between the streaks.

An expansion of the image 70 is also carried out. The structural element of the expansion is chosen to expand the striations so that they fill the gaps between them, on the expanded image 72 of FIG. to choose the structuring element of the dilation are the same as those of erosion. The dilated image 72 thus represents an average of the vertices of the streaks.

A difference is then made between the expanded image 72 and the eroded image 71 so as to obtain a difference image 73. This image here has a homogeneous content. There is therefore no defect in the streak area of the image 70.

On the other hand, by performing the same process with the image 74, having a slight defect 81 where streak portions are erased, a difference image 75 is obtained which highlights this defect, by a portion 82 whose gray levels are abnormal with respect to a relatively homogeneous area around said defect.

The method according to the invention makes it possible to automatically detect these defects, by comparing the value of the gray levels of the pixels with the median value of the pixels of the difference image. Thus, if the value of one of the pixels of the difference image is too far from the median value of the pixels of the image, it is considered that the pixel in question makes visible a defect in the streak zone of the image.

In another embodiment of the invention, one can perform several dilations and / or several erosions. For example, if a streak area has striations oriented in different directions, or with different thicknesses, then one can identify a type of streaks, perform the operations for that streak. type of streaks, then reapply the method for another type of streaks identified in the area. In this way, defects of one type of streaks and defects of another type of streaks are obtained, where appropriate, in an area where these streaks accumulate.

In another embodiment, the images include colors other than shades of gray. The above calculations, concerning the streak detection method, and the conformity control method, can in particular be carried out on each type of color independently of each other, so as to detect and / or control, for example, streaks of red, of green, of blue. The calculations can also relate to values resulting from combinations between these color values.

In another embodiment, the images form non-two-dimensional but three-dimensional spaces including voxels. Thus, in addition to grayscale or other colors, each voxel includes a luminance value. Previous calculations can therefore be further performed on the depth levels. Thus, even with identical or similar colors, it is possible to reference, determine, and / or control streaks which are distinguished from each other by their relief.

The method for detecting the streak zones described in Part II and the streak compliance checking method described in Part III may be implemented independently of one another. In particular, it is possible to control the conformity of the streaks according to the method of Part III after detecting a zone of striations other than according to the method of Part II, and vice versa.

Claims

1. A method for referencing streaks present in digital tire representations (10; 20), characterized in that automated means perform the following steps in order to reference types of streaks:

determining at least one representation (30) comprising a type of streaks (3) to be referenced,

identifying at least one segment (4) of pixels or voxels (6, 7, 8, 14, 15, 16, 17, 18) of the representation, and

2. Method according to the preceding claim, wherein the value or at least one of the values is chosen from the following group:

an average period of periods (11, 12, 13) relating to pixels or voxels (6, 7, 8, 14, 15, 16, 17, 18) of the segment (4),

preferably also a length determined from the average period.

A method of controlling a tire, wherein, for locating a streak area in a digital representation of a tire (10; 20; 30; 60), automated means performs the following steps:

considering at least one pixel or voxel (61, 64) of a zone of the representation (10; 20; 30; 60) and

for the or each pixel or voxel (61, 64) considered:

identifying a segment (62, 65) of pixels or voxels centered on the pixel or voxel (61, 64) considered,

determining at least one value relating to differences between gray levels or colors of pixels or voxels of the segment (62, 65), and

- Compare the value or values to one or predetermined thresholds.

The method according to the preceding claim, wherein the value or at least one of the difference values is an average of absolute values of differences of the levels within each pair of pixels or voxels (6, 7, 8, 14, 15, 16, 17, 18) of the segment (62, 65).

The method according to any one of claims 3 to 4, wherein the value or at least one of the difference values is an average period of pixel or voxel periods (67, 68) (61, 64). ) of the segment (62, 65).

The method according to any one of claims 3 to 5, wherein the value or at least one of the difference values is a number of periods (67, 68) relating to pixels or voxels of the segment ( 62, 65).

The method of any one of claims 3 to 6, wherein the value or at least one of the difference values is a length of a period

(67, 68) relating to pixels (61, 64) or voxels of the segment (62, 65).

The method according to any one of claims 3 to 7, wherein the automated means associates a binary value "0" or "1" with each pixel or voxel of the segment as a function of its level, and the value or at least l one of the differences-related values relates to changes between values within adjacent pairs of pixels or voxels when the automated means traverses the segment in one direction, these changes being identical and preferably the first pixel of each pair comprising a value identical to that of a pixel or a voxel of the segment located at one end of the predetermined segment depending on the direction.

The method of any one of claims 3 to 8, wherein the automated means associates a binary value "0" or "1" with each pixel or voxel of the segment as a function of its level, and the value or at least l one of the differences-related values relates to changes between values within adjacent pairs of pixels or voxels when the automated means traverses the segment in one direction, these changes being identical and preferably the first pixel of each pair comprising a preference value different from that of a pixel or a voxel of the segment located at an end of the predetermined segment depending on the direction.

10. A method of checking the conformity of tires, characterized in that automated means perform the following steps:

determining at least one expansion of a basic representation (70; 74) comprising at least one striation zone of a tire so as to obtain an expanded representation (72); - determining at least one erosion of the basic representation (70; 74) so as to obtain an eroded representation (71); and

determining a difference between the expanded representation (72) and the eroded representation (71) so as to obtain a difference representation (73; 75).

11. A computer program comprising code instructions adapted to control the implementation of the steps of the method according to any one of the preceding claims when executed on a computer.

12. Streak control device in digital tire representations, suitable for implementing a method according to at least one of claims 1 to 10.

13. Device according to the preceding claim comprising a recording medium comprising a database of values relating to streaks.