Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/0002—Inspection of images, e.g. flaw detection
  • G06T7/0004—Industrial image inspection
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/30—Subject of image; Context of image processing
  • G06T2207/30108—Industrial image inspection

Definitions

• the invention relates to the field of tire manufacturing, and more particularly the control of the external or internal appearance of tires in progress or at the end of the manufacturing process, in order to determine their conformity. relative to control references established for the purpose of the use to be made of said tire.
• the increase, at constant cost, computer computing power now allows the development on an industrial scale of automatic control means intended in particular to assist the operators responsible for visual control. These means make extensive use of image processing techniques whose performance, in terms of speed of analysis and definition, largely depends on the computing power used.
• the methods used to perform these treatments consist, as a rule, in comparing an image in two or preferably in three dimensions of the surface of the tire to be inspected with a reference image in two and preferably in three dimensions of the surface of said pneumatic. For this purpose, it is sought to match, the image or the surface of the tire to be inspected and the image or the reference surface, for example by superimposing them, and the manufacturing anomalies are determined by analyzing the differences between the two images or both surfaces.
• the manufacturers are committed to developing image analysis methods, complementary to the methods mentioned above, able to bring out anomalies present on the surface of the envelope. These anomalies whose dimensions are reduced, are manifested by a particular coloration, an abnormal shape, or a particular and unusual spatial distribution, and are embedded in the overall image of the tire surface in which they can merge. In addition, their appearance is random on the surface of one tire or tire to another. This results in a scarcity of significant information for determining digital protocols. [006] Thus, the publication EP 2 034 268 uses the wavelet technique to detect repetitive structures such as exposed ply son on the inner surface of the tires.
• the publication EP 2,077,442 proposes a method for selecting filters able to digitally process the image of the surface of a tire and sensitive to a particular defect.
• This filter selection method uses known texture analysis methods, and proposes to select the filters by a so-called genetic selection method. This method consists in statistically varying a collection of previously chosen filters, and measuring using a cost function, the sensitivity of this modification on the detection of a previously identified defect, compared to the collection. initial filters.
• the subject of the invention is a method of digital image processing of the surface of a tire by texture analysis, in which a combination of filters capable of identifying the signature of the image of a film is selected. anomaly present on the surface of the tire and which overcomes the disadvantages mentioned above.
• This method of detecting an anomaly on the surface of a tire comprises the following steps in which:
• a - the image is taken of a given anomaly present on the surface of at least one tire
• this multivariate image is transformed from the filter space into a spectral space of given dimension whose variables are the filters or filter combinations of said collection, to form a spectral image
• D - a classifier is constructed by determining, for this anomaly, the representative areas of the spectral space in which the points of the spectral image of said anomaly transformed in said spectral space are statistically representative.
• the method of detecting an anomaly on the surface of any tire on the surface of which it is desired to detect the presence or absence of said anomaly then comprises the operations in which:
• a digital image is made of all or part of said surface of said tire to be sorted
• the multivariate image of the tire to be sorted, of the image of the tire to be sorted using the filter collection, is determined in the space of the filters;
• the spectral image of the tire to be sorted is formed, by transforming, using the linear transformation, the multivariate image of the tire to be sorted, and,
• the location of the points of the spectral image of the tire to be sorted in the spectral space is analyzed with respect to the zones of the spectral space representative of the anomaly identified with the aid of said classifier.
• the classifier is constructed using a method of linear discriminant analysis type, said representative areas being delimited by hypersurfaces of said factor space.
• the implementation of the method under these conditions may present some difficulties of implementation because of the large number of data to be handled, and also because of the absence of common metrics between the various directions of the spectral space leading to a sometimes difficult statistical interpretation during the construction of the classifi- cator.
• the determination of the adapted metric consists, after step B and before starting step C
• step A the image of a given anomaly present on the surface of a series of several different tires, allowing during the step B to determine the multivariate image of each of these images and, in step C, to construct a single multivariate image by assembling the multivariate images obtained from this series of images.
• the data analysis is carried out according to a principal component analysis method, or according to a correspondence factor analysis method, or according to an independent component analysis method.
• This series of operations greatly improves the construction of the classifier and it is also possible, according to a second embodiment of the invention, to implement the method, in which the factorial space forms said spectral space and in which the spectral image is obtained by the transformation in the factorial space, using the linear transformation, of the multivariate image, obtained from the initial collection of filters.
• step C using a first selection method, it is possible to usefully determine the most relevant factorial axes with respect to the multivariate image transformed in the factorial space. using the linear transformation. We then limit description of said multivariate image at the coordinates of said image, expressed on these axes alone, whose number is less than the number of axes of the factor space, so as to obtain a reduced factor space.
• the first method of selecting the factorial axes consists in preserving the factorial axes whose sum of the inertias with respect to the point cloud of the multivariate image of the anomaly transformed in the factorial space, represents a given percentage of the inertia of all the axes relative to said cloud of points.
• the first method of selecting the factorial axes consists in preserving the factorial axes having the highest signal-to-noise ratio contained in the factors associated with the pixel vectors of the multivariate image transformed in the factorial space relative to to said axes considered.
• this reduction and simplification step is continued by projecting the initial collection of filters into said reduced factor space. Then, using a second selection method, we determine the filters of the initial collection projected in the factorial space whose vectors are farthest from the origin of the factorial axes, so that the number of filters of the initial collection is reduced, and we recalculate the coordinates of the image in the reduced factor space.
• the filters are selected whose quadratic sum of the distances at the origin represents a given percentage of the quadratic sum of the distances with respect to the origin of the set of filters of the initial collection projected in said reduced factor space, or whose square of the distance originally divided by the sum of the squares of the distances at the origin of all the filters of the initial collection projected in said reduced factor space is greater than the inverse of the number of filters (l / L).
• the method in which the reduced factor space forms said spectral space and in which the spectral image is obtained by the transformation in the reduced factor space is then applied. , using the linear transformation, the multivariate image, obtained from the reduced filter collection.
• the invention also comprises a device for controlling and detecting an anomaly on the surface of a tire comprising: lighting and shooting means capable of producing the image of the surface or of a portion of the surface of a tire, and
• FIG. 1 represents a two-dimensional gray level image
• FIG. 2 represents a pixel vector in a multivariate image
• FIG. 3 represents a channel of a multivariate image
• FIG. 4 represents a multivariate image obtained by juxtaposing the responses of the filters
• FIG. 5 represents the multivariate image in the reduced factor space
• FIG. 6 represents an illustration of the mode of selection of the filters in the factorial space
• FIG. 7 represents a simplified diagram of the main steps of the method according to the invention.
• the detection method according to the invention comprises two distinct stages during which, successively, a factor space, a transformation function and a classifier are determined which are able to reveal the presence or the absence of the anomaly and that the it will be assimilated to a learning phase, and a detection step itself, detection of said anomaly on the surface of any tire to be sorted.
• the learning phase begins with the selection of at least one tire having a given anomaly visible on its surface.
• This type of anomaly may be, for example, a molding defect, a grease stain, a reinforcement gap or creep. erasers placed under the reinforcing plies, a foreign material, etc. A two-dimensional black-and-white image of said anomaly is then produced.
• the white corresponds to the value 255, and the black to the value 0.
• the other values i.e. the intermediate gray levels
• a morphological filter in the sense of the present description, is defined as a function that makes it possible to transform an image / grayscale image into another image F (f).
• the starting space of the filter is therefore the set of values of the image of E in T, A (E, T)
• the response image of the filter can also be seen as a function which, at a pixel of E associates a gray level:
• morphological filters such as series of morphological openings and closures of increasing sizes, dilations or morphological erosions, or filters of the type wavelets that analyze the position and frequency of the objects composing the image, or curvelets that analyze the position, the frequency and which adapt to the discontinuity of the objects composing the image.
• a morphological filter is thus defined as a growing and idempotent transformation on a lattice.
• lattice a partially ordered set in which we can order some of its elements, and in which each pair of elements has an infimum (greater of the minor) and a supremum (smaller of the majors).
• a transformation ⁇ is increasing if it preserves the order relation between the images, or that the function ⁇ is increasing when:
• V, g, f ⁇ g ⁇ ( ⁇ ) ⁇ ( ⁇ ).
• a structuring element is a (small) set used to probe the studied image, it can be seen as a tool that would erode (ie remove material) or dilate (ie add material) to a picture.
• the erosion of the function / (ie the grayscale image) by the structuring element B, denoted ⁇ 5 (/), is the function that gives to every pixel xe E the minimum value of the image / in the observation window defined by B:
• a morphological opening by addition y B is defined as the composition of an erosion ⁇ ⁇ with a dilation ⁇ ⁇ for a structuring element B such that:
• a morphological closure by adding ⁇ 5 is defined as the composition of an expansion ⁇ ⁇ with erosion ⁇ ⁇ for a structuring element B such that:
• a wavelet is a summable square function on Euclidean space ", most often oscillating and of zero mean, chosen as a multi-scale analysis and reconstruction tool.
• the next step is to transform each of these images using the filters from the initial collection. We then obtain as many images as filters corresponding to the transformation of the initial image by each of the filters of the initial collection.
• - f j i x i) is a pixel value vector f (x) on the channel /.. .
• the multivariate images are thus discrete functions, with typically several tens or hundreds of channels, or variables or spectral bands as shown in FIG. 3.
• Each pixel of a multivariate image is a vector whose values are associated with an index j corresponding to filter responses.
• the components of each pixel vector have a value corresponding to the value of this pixel in the image transformed using each of the filters of said collection.
• the arrival image expressed in the filter space is then composed of vectors (the pixel vectors) whose variables are the filters of the initial collection.
• the multivariate image is obtained as being the juxtaposition of the responses to each of the filters forming the initial collection of filters. (F 1 (f), F 2 (f), ..., F L (f)). Which gives in the form of an equation:
• the multivariate image is obtained by a series of filters applied to the grayscale image. Sometimes we speak of a stack of images which is the response series of each of the filters forming the initial collection.
• the starting image contains very many channels (or axes), more or less relevant, so it is necessary to select those that are most relevant for the detection of anomalies.
• the advantage of such a transformation is to reduce the size of the image so as to reduce the calculation time, while retaining the information useful for further processing. Since, as a general rule, a single filter does not make it possible to discriminate an anomaly from the rest of the image, the object of the analysis is to determine the filter combinations that will have an answer with respect to the presence or the absence of said anomaly.
• PCA Principal Component Analysis
• CFA Correspondence Factor Analysis
• ACI Independent Component Analysis
• Anglo-Saxon acronyms such as the Fast ICA algorithm, the Joint Approximate Diagonalisation of Eigenmatrices (JADE) algorithm or the Independent Factor Analysis (IFA) algorithm, the use of which may also be considered .
• JADE Joint Approximate Diagonalisation of Eigenmatrices
• IFA Independent Factor Analysis
• F the contingency matrix P L representation of the multivariate image F.
• F is composed of P lines representing the vector pixels (i.e. individuals) and L columns (i.e. the channels or variables) corresponding to the number of filters in the initial collection.
• P lines representing the vector pixels (i.e. individuals)
• L columns i.e. the channels or variables
• the column vectors (i.e. channels or variables) of F are elements of "F :
• ⁇ ( ⁇ ., ..., ⁇ , ..., ⁇ )
• the metric between the individuals (i.e. the vector pixels) underlying the Principal Component Analysis is the inverse metric of the variances.
• the image space of arrival of the transformation ⁇ is called factorial space.
• This factorial space is composed of directional vectors carried by the factorial axes A l , A 2 , .... A k , ... A N ; and whose components, or coordinates of the pixel vectors in the factor space, c, c ' 2 , ..., c, ..., c' N, which are linear combinations of the filter vectors of the starting space, are the factors associated with the pixel vectors.
• the cloud of variables (the filters) and individuals (the pixels) is thus arranged so as to have the maximum of dispersion according to the factorial axes.
• the filter vectors of the initial filter collection have an image in this new factor space of ⁇ , d ', d', ... d ' ⁇ .
• the classifier construction stage which consists of determining the areas of the factor space in which are statistically significant the pixels considered to form the image of the anomaly.
• the factorial axes ⁇ are selected; the most significant ones to describe the coordinates c, c ' 2 , ..., c, ..., c' N of the multivariate image as represented in the factorial space after transformation by the function ⁇ .
• the simplest method is to classify the factorial axes ⁇ ; according to their decreasing inertia compared to the cloud of points formed by the multivariate image in the factorial space. Only the factorial axes whose sum of the inertias represents for example 80% of the total inertia are retained. [086] Another method consists in selecting the axes according to the signal-to-noise ratio measured by the computation of the variance on the channels of C. The variance of the noise corresponds to the height of the peak at the origin, and the variance signal corresponds to the amplitude of the original variance after suppression of the noise peak.
• the spatial covariance centered on the images of the pixel factors is calculated.
• the variance of the noise corresponds to the amplitude of the peak at the origin. This peak is suppressed by a morphological opening with a square structuring element of small, such size than a square of 3x3 pixels.
• the variance of the signal is the amplitude of the covariance at the origin after morphological opening.
• the spatial covariance is defined by the following equation:
• ⁇ g k ⁇ h EW k ⁇ xy k ⁇ x + h) ⁇ .
• c (x) the centered channel k of the image of the factors c '.
• the last stage of reduction of the space consists in selecting the filters of the initial collection corresponding to the anomaly, and whose coordinates in the reduced factor space are the factors associated with the filters represented in the form of vectors of " K , of ⁇ , d (, d, ..., d in the reduced factor space
• the interest is to reduce the number of filters to calculate by obtaining a good approximation of the To do this, we measure the distance between the factorial axes A k of the new space and the variables (the filters) represented as points in the factor space, as shown in a simplified manner in FIG.
• the factor space has been reduced to a two-dimensional space, and the most distant filters from the origin of the factorial axes are retained (d ', d' ⁇ , d ', d', d ', ⁇ 0 ), and the closest filters to the origin
• the method then provides for the construction of a classifier that will make it possible to detect the presence (or absence), as well as the position of the anomaly.
• the classifier allows to isolate certain areas of the spectral space formed by the space of the filters, by the factorial space or preferably by the reduced factor space, in which are located in a significant way the clouds of points corresponding to the spectral image of said anomaly obtained by the projection of the multivariate image by means of the linear transformation ⁇ .
• the most appropriate method is to apply a method of analysis based on a linear discriminant analysis, better known by its acronym LDA, and which will be briefly recalled below.
• LDA linear discriminant analysis
• This method of analysis is intended to separate classes of points by hypersurfaces, assuming that the distribution of points in a class is Gaussian. This works well in many cases, even if the points of the classes do not quite have a Gaussian distribution.
• the LDA can of course be used in multidimensional spaces.
• log- ! log J ky + log-
• This equation is that of the boundary between classes k and /. This is the equation of a hyperplane in dimension p. The classes of points will thus be separated by hyperplanes.
• N k is the number of individuals in class k;
• the discriminant functions ⁇ i (x) are of quadratic form and the method of analysis is then evoked.
• the zones are then separated by quadratic hypersurfaces and no longer by hyperplanes. Once these parameters have been determined statistically, we are then able to identify the areas of the spectral space in which the pixels of the spectral image representing a given anomaly will be statistically representative. In case of statistically representative presence of pixels in these areas it will be possible in return to conclude the probability of the presence of an anomaly and to determine the location on the image.
• the construction of the classifier can also be done at three distinct stages of the implementation of the invention, the steps of which have been described above.
• the classifier just after determining the factor space and the transformation function ⁇ .
• the construction of the classifier is done after reducing the number of filters in the initial collection and the dimensions of the factor space.
• the gray level digital image is produced of the surface of the tire that is to be sorted.
• the multivariate image is then determined.
• ⁇ ( ⁇ ) ( ⁇ ( ⁇ ), ⁇ ( ⁇ ), .. ⁇ of the surface or of the surface element of said tire to be sorted, using, depending on the case, the filters of the initial collection, (F l (f), F 2 (f), ..., F L (f) or the filters of the reduced collection selected, F 1 (/), F 2 (/), ..., F n (/), ..., F M (/), on the elements of the image of the tire to be sorted.
• This multivariate image is projected using the application ⁇ in the spectral space that can be formed, according to the variant embodiments of the invention, by the space of the filters, by the factorial space or by the reduced factor space, and in which the classifi- cator has been constructed.
• the position of the cloud of points of the spectral image of the surface or of the surface element of said tire to be sorted in the spectral space is then observed, and it is questioned, using the classifikator, to know if the pixels are distributed statistically representative in the areas of the spectral space delimited by the classifier, and if there is potentially (or not) an area of the image of the tire to be sorted which could correspond to said anomaly.
• the detection method of an anomaly just described is adapted to highlight a given anomaly present on the surface of a tire.
• the method described above is intended to apply preferably to search for anomalies present on the surface of a tire. It is also particularly useful when seeking to examine the interior surface of a tire on which known patterns such as streaks or streaks formed by the patterns present on the baking membrane in order to promote the flow of occluded gases. These patterns, which are generally more or less randomly located from one tire to another because of the elastic nature of the baking membrane, are not strictly speaking anomalies. As such, it is appropriate to determine the series of filters relevant for the identification of these streaks by applying the teachings of the method according to the invention, then to identify these streaks so as not to assimilate them with anomalies.

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