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
  • G06T7/0008—Industrial image inspection checking presence/absence
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/10—Segmentation; Edge detection
  • G06T7/155—Segmentation; Edge detection involving morphological operators
• • 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
  • G06T7/001—Industrial image inspection using an image reference approach
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/10—Segmentation; Edge detection
  • G06T7/11—Region-based segmentation
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/10—Segmentation; Edge detection
  • G06T7/136—Segmentation; Edge detection involving thresholding
• • 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
  • G06T2207/30248—Vehicle exterior or interior
• • 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
  • G06T2207/30248—Vehicle exterior or interior
  • G06T2207/30252—Vehicle exterior; Vicinity of vehicle

Definitions

• the invention relates to the field of tire manufacturing, and more particularly the field of visual control of the latter in progress or at the end of the production process.
• different lighting and digital imaging means are used to acquire images of the tires, for subsequent digital processing to detect imperfections previously detected visually by the operators.
• imaging means make it possible to take different images, whether two-dimensional or three-dimensional, of the inner and / or outer surface of the tire to be inspected.
• the tires have certain areas on which streaks are present, and other areas with no streaks. These striations generally have a width of the order of a few millimeters, and a height of the order of a millimeter.
• the present invention is therefore to provide a segmentation solution to overcome the aforementioned drawbacks.
• the present invention is therefore to provide a method for segmenting a tire image so as to distinguish the areas having streaks of those not showing.
• the present invention relates to a method of segmenting an image, representative of a tire, into a first zone having streaks and a second zone having none, the method comprising the following steps:
• the step of flattening the image comprises a step of detecting a carrier signal on which the striations lie.
• 2014PAT00305WO calculates the average of the neighboring pixels (located within a certain distance) and located on the same line; we then subtract this value from the pixel.
• the operation consists in calculating, in a horizontal window of size 2r +1 centered on the pixel (x; y), (considering the specificity of recollement of the right and left edges, expressed by the minimum), the average pixels, and to subtract the latter from the value of the pixel (and this repeated for all the pixels of the image).
• the segmentation method of the present invention could also be used on images representing all types of objects, and not necessarily tires.
• the flattening step would not necessarily prove useful, since it is made necessary in the case of a tire due to the rounded shape of the object.
• the next step of the method consists in performing a thresholding operation, in order to transform the flat image, which is in gray levels, into a binary threshold image. This makes it possible to create an output mask comprising a first set of pixels containing the streaks, and a second set of pixels comprising the other elements.
• the first two consist in calculating on the one hand the average of the gray levels of all the pixels of the row (respectively of the column) on which is a pixel, on the other hand the standard deviation, and of add up these two values.
• the third criterion is to verify that the pixel belongs to a valid line, that is to say a line that is not too close to the top or the bottom of the image; or a line that does not contain too many outliers, or a line that is not too close to a line containing a large number of outliers.
• This detection step is performed as follows:
• the variance of the gray levels of the pixels not belonging to the binary threshold image is calculated, which amounts to excluding any streaks,
• the calculation is carried out a second time by horizontally expanding the threshold image, which amounts to excluding more pixels from the calculation.
• the ratio between the two variance values calculated in this way is then carried out. If this ratio becomes greater than a predetermined threshold, it will be considered that the line has a streak.
• this step of detecting streaks is made necessary by the specific nature of the object, namely a tire. Indeed, as indicated above, the streaks are due to the manufacturing process, and can therefore be interrupted and therefore only present on part of the lines. Hence the need to detect
• the next step is to evaluate the number of streaks on each line. To do this, we implement the following steps:
• the variance is calculated along each of the columns of an input image, to form a one-dimensional image having a similar regularity to the streaks.
• the input image will be chosen as the flat image or the thresholded image.
• a maximum of the decomposed image does not correspond to a desired period of the striations, but to a harmonic, that is to say a value due to a group of striations. which is repeated regularly in the image. Therefore it is useful, in a particular embodiment, to look at the fractions of the determined maximum to detect possible candidates for the streak period.
• the best components that can be streaks are detected, and they are retained in a result set. To do this, we go through the related components of the thresholded image by decreasing size, and we retain them if two conditions are met:
• the connected component if it was added to the result set, must not result in a line of the thresholded image having a number of elements of the set Result greater than the number of striations detected in the previous step, and
• a method according to the invention also comprises the following steps:
• a step of reevaluating the number of streaks in the image and a step of filtering the determined set of pixels, as a function of the number of striations reevaluated, to obtain a second set of pixels of the image.
• a method according to the invention further comprises a step during which one completes empty spaces of the image to obtain a third set of pixels of the image.
• a method according to the invention comprises a step in which is eliminated, the third set of pixels, supernumerary components, to obtain a fourth set of pixels of the image representing streaks.
• the objective of this step is to remove these peaks value.
• a morphological opening is used, which consists of removing all the narrow mountains, whatever their altitude, followed by a morphological closure which consists of removing all the narrow canyons, whatever their depth.
• the opening operation consists firstly in replacing the value of each pixel of the image by the minimum value of the pixels located in a certain neighborhood, then in starting the operation again, this time taking the value Max.
• the closing operation consists in performing the same two operations, but in the opposite direction (first the maximum value, then
• the chosen neighborhood consists of the set of pixels located on the same line, that the pixel studied (it is called opening and closing by a linear structuring element) and at a distance less than a certain threshold.
• a threshold value is preferably chosen for eliminating, on each line, small mountains and canyons. However, this choice of radius must represent a compromise between a value that is too low and that would not allow cleaning. correct, and too high a value that could lead to the removal of some elements of interest streaks.
• the cleaning step is performed beforehand.
• the cleaned image will be:
• the flattening step is performed using an AvgSub operation.
• the calculation of the thresholded image is performed as follows:
• the first condition makes it possible to mark as invalid the first ten and the last lines of the image
• the second condition makes it possible to mark as invalid all the lines having more than 5% of pixels marked as aberrant in the image.
• This formula outputs a mask of size equal to the size of the input images, and where a pixel will be present if it is on a valid line (first condition), if its value is greater than the average plus the standard deviation pixels of the same line as him (second condition), and if its value is greater than the average plus the standard deviation of the pixels of the same column as him (third condition).
• the step of detecting lines with streaks is performed as follows:
• the ratio between these two values is calculated in a new unidimensional Image image of size equal to the height of the input images.
• the ratio between the two values is also calculated in the unidimensional image Ratio but by cleaning with openings and closures, as follows:
• the variance ratio threshold which acts as a limit is 1.5: if the min value Ratio is greater than this threshold, then we consider that the streaks are present on all the lines of the image, if the max ratio is less than this ratio, so the image has no streaks.
• Ligne_NOTOK_NOTSTRIE l. -
• the final Line2 image which assigns the final label of each line of the input image, is a mix between Line and a cleaned version of Ligne2_tmp.
• the Line2 image that assigns a label to each line of the input image is composed by mixing the Line and Line2 tmp information. If all lines have a satisfactory variance ratio (greater than 1.5), then the streaks are present over the entire height of the image and Line2 will be a copy of Line. Otherwise, if only certain lines have a satisfactory variance ratio, then Line2 is equal to a cleaned version of Ligne2_tmp except for lines with too many outliers, where the NOTOK PNM Line label is copied (this operation is carried out thanks to the use of the minimum);
• Ligne2_tmp The cleaning of Ligne2_tmp is done thanks to an opening, followed by a closing. However, we notice in images with streaks that they do not all stop on the same line: they disappear gradually, and do not disappear in the same lines. For this reason, Line2_tmp erosion is carried out in order to widen the labels of the strapless lines and to include, as a precaution, these "fuzzy" areas as strapless lines.
• the step of evaluating the number of streaks on each line is preferably carried out as follows:
• the variance of each of the columns of an input input image which is, according to the embodiment, the flat flat image or the threshold threshold image is calculated. This calculation is performed excluding, thanks to Ligne2, the elements located on lines of
• the image Var_col thus obtained is a one-dimensional image of the same size as the width of the input images. We see that this image has a repeating pattern as many times as there are streaks in the image. We will then perform a Fourier analysis of this image Var_col to find the number of striations present on each line of the image. This calculation is as follows:
• the size of the image F is equal to the largest power of two strictly less than L (Input) plus 1. Thus, for images 40,000 pixels wide, F is 32769 pixels.
• the image F is such that a peak on F (1000) means that there is, in the image, a pattern locating every 1000 pixels. Therefore, it is useful to look for the peaks of F.
• a geodesic reconstruction of an image D in F3 is then carried out in order to recover the carrier signal which can then be suppressed.
• the image D is an image of the same size as the image F3, having the value in all points except the abscissa 0 where it is F3 (0).
• the step of detecting the best candidates of the binary image is performed as follows:
• V ' ⁇ ms if 3 ⁇ 4 ⁇ ⁇ . ;
• the candidate set is then constructed by adding the elements of S if they do not conflict with the elements already added in Candidate. For this, we build a sequence of sets R:
• the present invention provides a method implementing a number of original features compared to known solutions of the state of the art.
• the means for performing a detection of the lines of the image where streaks are present are different from the known solutions, since the principle of taking a mask of candidate pixels to belong to striations, and to observe how the variance (calculated excluding the elements of this mask) evolves according to the expansion of this mask, is original. Indeed, in the present invention, looking for relief elements that cause a shadow projected on the image, and detecting the lines of the image having streaks in attempting to detect the lines having a drop shadow.
• the present invention aims to provide a method for dividing the lines of the image into two categories: those where streaks are present, and those that do not. It has been found that the known solutions, namely the conventional approach of minimizing intra class variance or maximizing inter-class variance, do not work (especially since classes can have strong variances). In the present invention, means are used to equalize the class variances using a linear time algorithm, which overcomes the disadvantage of known solutions.
• the first lies in the fact of making a Fourier transform not on each line of the image, as presented in the known solutions, but on a signal in one dimension, representative of the lines of the image.
• This signal is obtained by calculating the variance of each column of the image: thanks to the relief of the streaks and their shadow, we obtain a signal with the same period as the streaks of the image. This solution makes it possible to reduce the calculation times implemented.
• the second element comes from the fact of carrying out morphological operations on the results of the Fourier transform in order to clean it of parasitic elements which could distort the result obtained.
• a method according to the invention implements, for the selection of the best candidate components that can belong to a streak, a series of placement operations and removal of candidates by decreasing as and when constraints on their position. This pattern of decreasing constraints as it goes is the opposite of all the solutions of the state of the art which generally consist in increasing the constraints over time.

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• Engineering & Computer Science (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Theoretical Computer Science (AREA)
• Quality & Reliability (AREA)
• Image Analysis (AREA)
• Image Processing (AREA)

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• 2015
  • 2015-06-29 FR FR1556081A patent/FR3038111B1/fr not_active Expired - Fee Related
• 2016
  • 2016-06-28 EP EP16744440.5A patent/EP3314574A1/de not_active Withdrawn
  • 2016-06-28 CN CN201680038311.4A patent/CN107924566A/zh not_active Withdrawn
  • 2016-06-28 WO PCT/FR2016/051600 patent/WO2017001766A1/fr not_active Ceased
  • 2016-06-28 US US15/736,595 patent/US20180137616A1/en not_active Abandoned

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