Classifications

• • G—PHYSICS
  • G07—CHECKING-DEVICES
  • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
  • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
  • G07D7/06—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency using wave or particle radiation
  • G07D7/12—Visible light, infrared or ultraviolet radiation
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B41—PRINTING; LINING MACHINES; TYPEWRITERS; STAMPS
  • B41F—PRINTING MACHINES OR PRESSES
  • B41F33/00—Indicating, counting, warning, control or safety devices
  • B41F33/0036—Devices for scanning or checking the printed matter for quality control
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR 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
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
  • H04N1/46—Colour picture communication systems
  • H04N1/56—Processing of colour picture signals
  • H04N1/60—Colour correction or control
  • H04N1/603—Colour correction or control controlled by characteristics of the picture signal generator or the picture reproducer
  • H04N1/6033—Colour correction or control controlled by characteristics of the picture signal generator or the picture reproducer using test pattern analysis
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR 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
  • G06T2207/30144—Printing quality

Definitions

• the invention relates to methods for the qualitative assessment of a material with at least one identification feature according to the preamble of claim 1, 2 or 3.
• camera systems are increasingly being used for different applications, for example in inspection systems, web monitoring systems or register measuring systems, these systems being arranged in or on a printing press or a machine that processes a printing material. Furthermore, there is a requirement that these systems perform their function “inline”, ie integrated in the work process of the printing press or printing machine, which is due to the large amount of data supplied by the camera system and the fast process flow of the printing press or the printing press for the respective machine
• Camera system means a considerable challenge, in order to achieve a reliable assessment, preferably of each individual identification feature, for example in a quality control even for recognition features that are difficult to identify using spectrophotometry, despite the high transport speed of the material, and the short time available for assessment
• Such camera systems often use electronic image sensors, in particular color cameras with an image sensor consisting of a CCD chip, the light-sensitive pixels of which correspond to the i In the observation area recorded color an output signal z. B. in three separate signal channels, mostly for the colors red, green and blue.
• the sensitivity distribution of the color cameras used does not correspond to the sensitivity distribution of the human eye, this leads to the fact that the image data supplied by the color cameras lead to a distorted visual impression during further processing, for example, the display on a color monitor, so that the test is adequate For this reason alone, qualitative assessment of the printed material is hardly possible.
• the position of a recognition feature to be assessed in the test process varies within certain tolerance limits within a defined expectation range.
• the position of a window thread as z. B. is used for banknotes or tokens, vary relative to the printed image of the banknotes or tokens on a printed sheet due to the properties of the production process for producing the window thread.
• positional tolerances of certain identification features which are in principle tolerable, can generate an error message, since when comparing a print pattern defined as a target value with the current print image, image position by image position is compared one after the other, so that positional deviations of identification features are identified as errors that are not.
• the printed material for example a printed sheet printed with banknotes, which is provided with a silver thread, hologram or kinegram
• an illuminating device in such a way that the light reflected by the printed material falls into a photoelectric sensor.
• the image thus recorded by the photoelectric sensor can then be evaluated in an evaluation device, for example a standard computer with suitable evaluation software, and checked for printing errors.
• the prerequisite for the evaluation here is that a recognition feature which varies in its position has a sufficiently high reflectivity, for example in the form of a shiny silver thread. It is therefore disadvantageous that recognition features whose image properties do not deviate sufficiently from the image properties of the other printed image after being recorded by the photoelectric sensor, as is the case, for example, with colored window threads, cannot be recognized with sufficient reliability by the evaluation device.
• DE 101 32589 A1 discloses a method for the qualitative assessment of printed material with at least one identification feature, in which an image of the material to be assessed is recorded with an image sensor and the geometric contour and / or the relative arrangement of several for this image in an evaluation device Identification features are evaluated among each other.
• the subsequently published DE 102 34086 A1 discloses a method for signal evaluation by an electronic image sensor in the pattern recognition of image contents of a test specimen, in which a decision is made about the belonging of the test specimen to a specific class of test specimen.
• a measuring arrangement for identifying valuable objects by digital image analysis is known, a narrow-band excitation light source, e.g. B. a tunable laser, the object with light of a narrow Irradiated frequency range in a selected location, with light reflected from the object or an emission induced in the object by its irradiation z.
• B. captured by a multitude of pixels, photometrically calibrated CCD camera, digitized and sent to a computer as a data set that characterizes each pixel and stored in a memory, whereby the photographically recorded object can also be measured so that the data set contains information Using a geometrical arrangement of different objects, their distance from each other or a depth of their relief structure can be added.
• the data set created from this image acquisition can, for. B. be made available over the Internet for a comparison of this data record with a data record created by another object in order to check the other object elsewhere for a match with the first object, ie with the original, and thus for its authenticity.
• CH 684222 A5 discloses a device for classifying a pattern, in particular of a bank note or of a coin, wherein a multi-stage learning classification system on the pattern sequentially carries out at least three tests by comparing feature vectors with vector nominal values, a light source performing the pattern illuminates and a sensor measures the radiation reflected from the pattern at discrete times.
• methods for pattern recognition determine similarities, such as distance measurements for segmented objects, or they calculate global threshold distributions. These methods are based on translation-invariant output spectra. In reality, however, situations often occur, such as object shifts under the recording system, different substrates during the recording or aliasing effects, so that in many cases a direct comparison of these output spectra with stored target values cannot be carried out.
• the invention has for its object to provide methods that can be used in the printing industry for the qualitative assessment of a material with at least one identification feature.
• the advantages that can be achieved with the invention consist in particular in that material, in particular printed material with at least one identification feature, is reliably qualitatively assessed even if the color image recorded by the material, in particular the identification feature, has optical properties that are not sufficiently reliable with spectrophotometric methods alone are to be identified. Since the proposed method does not presuppose that the material to be assessed qualitatively has a pronounced reflectivity, practically any optically perceptible property or nature of the material can be defined as its distinguishing feature, which results in a significantly expanded field of application for the method. The determination of what the identifying feature should consist of can therefore be decided based on the application. The test is aimed solely at the fact that there is an optically perceptible difference between the identification feature and its surroundings. This difference is used to assess the material qualitatively, which may also include z. B. to identify or to check its authenticity.
• the proposed method leads to good results in particular if it can also be assumed that the position of the identification feature varies within an expected range determined by tolerance limits.
• the colors detected by the image sensor with regard to hue, saturation and brightness are arranged with sufficient accuracy in a color space which corresponds to the color perception of the human eye, so that the material is displayed by a display device, e.g. B. a color monitor in the form of a color image is reproduced as true to color as the material would be inspected directly by the human eye, so that a reliable qualitative assessment of the material and thus, in particular, of its recognition feature is possible on the basis of the color image.
• ⁇ E As a color space is z. B. especially the so-called CIELAB color space, which has found widespread use in printing technology.
• An important parameter for a color deviation is given in the CIELAB color space by the color difference ⁇ E between the target and actual values of the parameters L, a and b characterizing the CIELAB color space, the parameter L for the brightness, a for the red-green Value and b stands for the yellow-blue value.
• These parameters are also called CIE values.
• Further parameters are the hue difference ⁇ H and the saturation difference ⁇ C, with the hue difference ⁇ H being particularly important as a parameter in multicolor printing, because a color cast is subjectively more disturbing than a saturation difference ⁇ C that indicates a difference in brightness.
• B up to 1 an invisible color difference, from 2 a slight difference, from 3 a recognizable difference, from 4 a clear difference and from 5 a strong difference.
• the range of values for the CIE values a and b ranges from -100 for green or blue to +100 for red or yellow, the range for brightness L from 0 (black; total absorption) to 100 (white; total reflection).
• S cones
• M cones
• L three types of cones
• the maximum absorption of the S-cone type is in the blue range, namely at 420 nm.
• the M-cone type absorbs maximally in the green spectral range, namely at 534 nm.
• the L-cone type has its absorption maximum at 564 nm in the yellow / red spectral range.
• Three-pin vision is called trichromatic vision.
• the individual color impressions are triggered by differently strong stimuli of the individual cone types. An equally strong attraction of all cone types leads to the impression of the color white.
• color sensation phenomena such as e.g. B. the color antagonism and color constancy are not explained.
• Color antagonism means that certain colors can never be seen in transitions, so that no color transition between these colors is possible. Colors that show the color antagonism are called counter or complementary colors. The color pairs red / green and blue / yellow and black / white are worth mentioning here.
• the color constancy compensates for the different spectral distribution of the light, which is dependent, for example, on weather or daylight conditions.
• the counter-color model assumes that the cones are arranged in receptive fields, namely in blue / yellow fields and red-green fields.
• Receptive fields are to be understood here as neurons and the way in which the stimuli of the cones are processed further by the neurons.
• Two types of receptive fields are essentially responsible for color vision. The first receptive field gets its input from the L and M cones, the second receptive field from the S cones together with differently weighted stimuli from the L and M cones. It is assumed that at the level of the neurons or receptive fields, a subtractive color mixture is used to stimulate the cones.
• the trichromatic model most commonly used in technology to describe additive color images is the RGB model.
• the color space is described by the three primary colors red, green and blue.
• a particular disadvantage of this model is that the description made by the RGB model does not correspond to the sensation of the human eye, since in particular the behavior of human perception, that is to say the perception by the sensory organs, is not taken into account.
• Electronic image sensors in particular CCD chips for color cameras, have i. d. R. a variety of z. B. arranged in a matrix on photosensitive pixels, for. B. a million or more, of which i. d. R. each provides a first electrical signal that correlates with the color image in accordance with the colored light recorded in the observation area.
• B. is divided into three separate signal channels, each signal channel at the time of viewing usually provides a part of the first electrical signal corresponding to the primary colors red, green and blue. Such a signal is referred to as an RGB signal.
• a spectral sensitivity of each signal channel (R; G; B) is set to the spectral sensitivity of the human eye.
• the first electrical signal as a whole is also adapted to the color perception of the human eye with regard to hue, saturation and brightness. A color image recorded with such a color camera is consequently composed of a large number of pixels.
• the method according to the invention is characterized in that a second electrical signal is obtained from at least one reference image and is stored in a data memory, the second electrical signal forming at least one desired value for the first electrical signal that at least the color image of the identification feature indicates a color deviation from the reference image and / or the identification feature to a membership in a specific class of identification features and / or to a specific geometric contour and / or to a relative arrangement to at least one additional identification feature of the material in each case by comparing the first signal with the second signal is checked for reaching or matching the target value.
• the material and / or its identification feature is preferably always used simultaneously with respect to at least two of the aforementioned Checked criteria.
• At least two of the checks of the color image are carried out, in particular the checking of the identification feature for a color deviation from a reference image and the checking of the identification feature for its belonging to a specific class of identification features or for a specific geometric contour or for a relative arrangement to other identification features of the material preferably at the same time in parallel and independent test processes.
• an assessment of printed material in an ongoing printing process of a printing press or in a running work process of a machine that further processes the printed material for quality control is also possible Possible.
• the material is, in particular, high-quality printed products which, for. B. require a very careful examination for security reasons and to the z. B. high demands are made with regard to the stability of their printing properties, in particular, therefore, banknotes or tokens.
• the color image is preferably checked for a color deviation from the reference image in that the part of the first signal belonging to the color image provided in the first signal channel is linked to the part provided in the second signal channel by means of a first calculation rule, thereby generating an output signal of a first counter-color channel that the part of the first signal belonging to the color image provided in the third signal channel is linked to the part in the first and the second signal channel by means of a second calculation rule, whereby an output signal of a second counter-color channel is generated, and that the output signals of the counter-color channels are compared be classified with target values.
• the identification feature is checked for belonging to a specific class of identification features preferably by the fact that the The first electrical signal provided by the image sensor is converted into a translation-invariant signal with at least one feature value by means of at least one calculation rule, the feature value is weighted with at least one fuzzy membership wedge function, and a higher-level fuzzy membership function is generated by linking all membership functions using a calculation rule consisting of at least one rule that a sympathy value is determined from the superordinate fuzzy membership function, that the sympathy value is compared with a threshold value and that, depending on the result of this comparison, a decision is made as to whether the identification feature belongs to a specific class of identification features.
• the checking of the recognition feature for a specific geometric contour and / or for a relative arrangement to at least one further recognition feature of the material is preferably carried out by storing at least one background setpoint and at least one mask setpoint in the data memory, the background setpoint at least one property of the one to be assessed Material in at least part of a surrounding area surrounding the identification feature and wherein the mask setpoint represents the geometric contour of the identification feature or the relative arrangement of a number of identification features with respect to one another, that when testing the material from the first electrical signal provided by the image sensor and the background setpoint a difference value at least for the expectation range is formed that the current position of the recognition feature is derived from a comparison of the difference value with the mask setpoint and that the qualitative assessment of the material the area of the material to be assessed, which results from the current position of the distinguishing feature, is hidden.
• the adaptation of the first electrical signal to the color perception of the human eye takes place in that the image sensor at every point in time provided RGB signal is interpreted as a vectorial output signal, the coefficients of the RGB signal vector being multiplied by an in particular square correction matrix, so that all parts of the first electrical signal representing a signal channel are approximated to the color perception of the human eye.
• By multiplying the RGB signal vector by a correction matrix on the one hand, it is possible to classify all printing inks relatively precisely into any color space.
• an adaptation of the RGB signal vector by means of multiplication with the correction matrix is easy to implement in terms of data technology, so that an implementation in a real system is possible even with large amounts of RGB signals that are provided by a plurality of pixels of the image sensor at the same time is.
• the coefficients of the correction matrix are of crucial importance for the quality of the proposed correction of the RGB signals, since the RGB signal vectors are transformed in different ways depending on the choice of these coefficients.
• the coefficients of the correction matrix can consist, for example, of empirical values. They are stored in a data store.
• an iterative approximation algorithm is proposed.
• a reference color chart for example an IT8 chart with 288 color patches, is specified to carry out this approximation algorithm.
• the different reference colors are shown in the color fields.
• the classification of the different reference colors in a suitable color space for example the CIELAB color space, is also known.
• Known transformations can be used to calculate corresponding target values for the three signal channels from these predetermined CIELAB values for the different reference colors of the reference color chart.
• a reference color chart is therefore used for the approximation algorithm Input variable and for each reference color a vector with a setpoint for each signal channel as the desired result of the conversion.
• the approximation algorithm for determining the coefficients of the corrector matrix is carried out, the reference color table is recorded with the image sensor of the color camera and the RGB signal vector is determined for each color field. The difference between these RGB signal vectors of the color camera and the vector with the predetermined target values corresponds to the difference between the color perception of the human eye and the sensitivity distribution of the color camera.
• a further correction step can be carried out.
• the coefficients of the RGB signal vectors are converted in such a way that the result corresponds to those RGB signal vectors that would be obtained by illuminating the observation area with a standard light.
• the color correction values for adapting the RGB signal vectors to different illumination sources and changes thereof can advantageously be calculated as follows.
• the starting point of the iteration is a correction matrix, the coefficients of which are specified as output values. These baseline values can either be purely random or selected according to certain empirical values.
• this correction matrix is then multiplied by all the RGB signal vectors provided by the image sensor and the corrected RGB signal vectors thus obtained are temporarily stored in a data memory. Then the coefficients of the correction matrix are changed slightly and the multiplication is carried out again. The change in the coefficients of the correction matrix is only accepted if the corrected RGB signal vectors approach the vectors with the specified target values.
• the approximation of the corrected RGB signal vectors to the vectors with the specified target values is evaluated for each iteration step in order to be able to decide on the basis of this evaluation whether the change in the coefficients of the correction matrix made in this iteration step should be adopted or rejected.
• An advantageous evaluation method provides that for each color field of the reference color table the difference value between the corrected RGB signal vector and the vector with the specified target values for this color field is determined and the sum of all these difference values is added up.
• the change in the correction coefficients of the correction matrix in the current iteration step is only adopted if the sum of all difference values in this current iteration step has become smaller compared to the sum of all difference values in the previous iteration step.
• Another problem with camera systems is the correct adjustment of the color balance, ie the correct weighting of the different signal channels to each other.
• the coefficients of each RGB signal vector can each be multiplied by a correction channel-dependent correction factor.
• a correction vector is added to each RGB signal vector. This correction of the three signal channels of each RGB signal vector corresponds to a linear shift of the individual coefficients of the RGB signal vectors.
• a particularly good color balance is achieved if the correction vector and the signal channel-dependent correction factors are selected such that the corrected RGB signal vectors obtained by applying the correction with the correction vector and the correction factors are essentially exact for the two fields with the reference gray values black and white correspond to the vectors with the specified target values for these two color fields.
• the linear shift of the RGB signal vectors is selected such that corrected results are obtained for the two reference gray values black and white, which correspond to the contrast perception of the human eye. This linear shift is preferably applied to all RGB signal vectors, as a result of which brightness and contrast in the entire color spectrum are automatically corrected as well.
• RGB signal vectors which has the highest coefficients and thus represents the brightest point in the observation area is then filtered out of all these RGB signal vectors.
• all pixels would have to deliver RGB signal vectors that are essentially identical to one another. The respective differences are based on color distortions or a design-related drop in intensity. To compensate for this, correction factors are now selected for each signal channel of each individual pixel, which ensure that when the homogeneously colored material is recorded, all RGB signal vectors correspond to the RGB signal vector at the brightest point in the observation area.
• Color distortions in particular depend heavily on the lighting conditions in the observation area. In order to rule out sources of error due to a change in the lighting conditions, the lighting should therefore correspond to the experimental determination of the pixel-specific, signal channel-dependent correction factors of the lighting during the later use of the camera system.
• the corrected RGB signal vectors which are obtained by correcting the RGB signal vectors originally provided by the color camera, are used to control the separate signal channels of a color monitor used.
• the display of the colors on a color monitor also poses the problem that the display characteristics of the most color monitors do not match the color perception of the human eye. This is based in particular on the fact that the brightness behavior of the color monitors is generally not linear, ie the intensity of the light that is reproduced on the color monitor is a non-linear function of the electrical input signals present on the color monitor, here the RGB signal vectors.
• the coefficients of the corrected RGB signal vector taken as the basis can each be potentiated by a factor ⁇ .
• the nonlinearity of the brightness behavior of most color monitors can be compensated for by this nonlinear conversion of the coefficients of the corrected RGB signal vectors.
• a value in the range between 0.3 and 0.5, in particular approximately 0.45, must be selected for the factor ⁇ .
• the processing of the stimuli in human color vision is simulated.
• a signal vector is provided from each pixel for the color image recorded by the image sensor, the coefficients of which preferably represent three separate signal channels.
• Each of the three signal channels has a characteristic spectral sensitivity.
• the two receptive fields, which represent the second stage of color processing in human vision, are linked by a corresponding combination of the three simulated separate signal channels.
• the red / green field of human color perception represents the first counter-color channel in the technical model.
• the output signal of the first counter-color channel is generated by linking the part of the signal vector in the first signal channel with the part of the signal vector in the second signal channel.
• the link is made by means of a calculation rule which consists of at least one calculation rule.
• the blue / yellow field is generated in the technical model by linking the part of the signal vector in the third signal channel with a combination of the parts of the first and the second signal channel. In the technical model, the blue / yellow field corresponds to the second counter-color channel.
• the output signal of the second counter-color channel is generated by the link described above.
• the link is made by means of a second calculation rule, which consists of at least one calculation rule. In order to evaluate the signal vector of the examined pixel, the output signals of the two counter-color channels are classified in the next step. This makes a decision as to whether the signal vector of the examined pixel and ultimately also the color image corresponds to a certain class, whereby a good / bad classification can be made.
• the spectral range in which the signal channels of the method operate is of no essential importance for the principle of the method, as long as it concerns signal channels with different spectral sensitivity. It is advantageous if the signal channels correspond to the three primary colors of the RGB model, namely red, green and blue, because this makes use of a widely used color model. Each signal channel is advantageously adapted in its spectral sensitivity to the spectral sensitivity of the cone types of the retina of the human eye.
• a Possibility is that a calculation rule of the first calculation rule a weighted difference formation of the part of the signal vector in the second signal channel from the part of the signal vector in the first signal channel and / or a calculation rule of the second calculation rule a weighted difference formation of the weighted sum of the parts of the first and provides second signal channel from the part of the third signal channel.
• At least one signal in at least one counter-color channel is preferably subjected to a transformation rule after and / or before the linkage, in particular a non-linear transformation rule.
• a transformation has the particular advantage that the digital character of electronically generated color images can be taken into account. It is also possible through transformation regulations to transform a signal from the color space into a space in which the irritation of the cones can be described.
• the signals are preferably subjected to a transformation in both counter-color channels.
• the receptive fields in human vision are characterized by a low-pass behavior, it makes sense if at least one signal in at least one counter-color channel is filtered using a low-pass filter.
• the output signal of each counter-color channel is preferably filtered by means of a low-pass filter.
• the method preferably has a learning mode and a working mode.
• an evaluation device processing the signals of the image sensor can be switched between these two operating modes, ie the learning mode and the working mode.
• At least one reference image e.g. B. the recording of at least one individual printed sheet, checked pixel by pixel and the output signals generated by the reference image of the two counter-color channels are stored in a data memory as a second electrical signal forming a desired value.
• the output signals of each counter color channel are then stored pixel by pixel in the data memory.
• the output signals of the corresponding pixel generated by a color image to be checked are then compared with the corresponding values stored in the data memory as the desired value, and a classification decision is then made.
• the values stored in the data memory are formed by several reference data records, so that a permissible tolerance window is defined for each value in the data memory, within which one is used during image verification generated output signal value of a counter color channel may fluctuate.
• the target value of the output signal of a counter-color channel can be determined, for example, by arithmetic averaging of the individual values, the individual values resulting from the reference data sets.
• the tolerance window can be determined, for example, by the minimum and maximum values or by the standard deviation of the output signals of the counter-color channels of each pixel generated by the examined reference images.
• the method for checking the identification feature for its belonging to a specific class of identification features preferably proceeds in the following essential procedural steps: feature formation, fuzzification, interference, defuzzification and decision about a class affiliation.
• the first electrical signal provided by the image sensor is converted into a translation-invariant signal in a feature space by means of at least one calculation rule.
• the aim of the feature formation is to determine such quantities by which typical signal properties of the color image are characterized.
• the Typical signal properties of the color image are represented by so-called features.
• the characteristics can be represented by values in the characteristics space or by linguistic variables.
• the affiliation of a feature value to a feature is described by at least one fuzzy affiliation function.
• This is a soft or fuzzy assignment, the association of the feature value with the feature being present in a standardized interval between 0 and 1, depending on the value of the feature value.
• the concept of the membership function means that a characteristic value can no longer be assigned completely or not at all to a characteristic, but rather can assume a fuzzy membership which lies between the Boolean truth values 1 and 0.
• the step just described is called fuzzification. In fuzzification, therefore, a sharp feature value is essentially converted into one or more fuzzy affiliations.
• a superordinate membership function is generated by means of a calculation rule which consists of at least one rule, all membership functions being linked to one another. The result is a higher-level membership function for each window.
• a numerical value is determined from the superordinate membership function formed in the interference, which is also called the sympathy value.
• a comparison of the sympathy value with a predetermined threshold takes place, on the basis of which the affiliation of the window to a particular class is decided.
• the threshold value forms a further setpoint contained in the second electrical signal.
• the type of the characteristic values in the characteristic space is of minor importance for the basic sequence of the procedure. In the case of time signals, for example, their mean value or variance can be determined as characteristic values. If the procedure for checking the identification feature for its belonging to a certain class of identification features is subject to the requirement that it should process the color images without errors, regardless of the prevailing signal intensity, and furthermore small but permissible fluctuations in the color image should not lead to interference, it makes sense if the conversion of the first electrical signal from the two-dimensional space is carried out by means of a two-dimensional spectral transformation. Examples of a suitable spectral transformation are a two-dimensional Fourier, Walsh, Hadamard or circular transformation. The two-dimensional spectral transformation gives translationally invariant feature values. The amount of the spectral coefficients obtained by a spectral transformation is preferably used as the feature value.
• the membership functions are preferably unimodal potential functions.
• the higher-level membership function is preferably a multimodal potential function.
• the membership function has positive and negative slopes, it is advantageous if the parameters of the positive and negative slopes can be determined separately. This ensures a better adaptation of the parameters to the data records to be examined.
• the method is in turn divided into two different operating modes, namely a learning mode and a working mode.
• a learning mode the parameters of the membership function are determined from measured data records.
• the parameters of the membership functions are matched to so-called reference pictures, ie in the learning mode a membership of the feature values resulting from the reference pictures is derived from the corresponding features by means of the membership functions and their parameters.
• the characteristic values that result from the subsequently measured data sets are weighted with the membership functions, the parameters of which were determined in the learning mode, whereby the characteristic values of the now measured data sets are associated with the corresponding characteristics.
• the parameters of the membership functions are determined on the basis of measured reference data records.
• the data records to be checked are weighted and evaluated with the membership functions defined in the learning mode.
• At least one rule by means of which the membership functions are linked to one another is preferably a conjunctive rule in the sense of an IF ... THEN link.
• the generation of the higher-level fuzzy membership function is preferably divided into the following sub-steps: premise evaluation, activation and aggregation.
• premise evaluation a membership value is determined for each IF part of a rule and, when activated, a membership function is defined for each IF ... THEN rule.
• the superordinate membership function is generated during the aggregation by superimposing all membership functions generated during activation.
• the examination of the identification feature for a specific geometric contour and / or for a relative arrangement to at least one additional identification feature of the material is based on the basic idea when evaluating a position variant identification feature in which the optical properties, for example the reflectivity, do not provide sufficiently reliable identification it is sufficient to include additionally known information about this identification feature in the evaluation.
• the position variant identification feature for example a colored window thread, differs at least in partial areas in the optical properties, for example in the gray value, from the other material to be inspected , for example, distinguishes the print image surrounding the identification feature that there is at least no complete match between the identification feature and the print image
• Itition variants of the identification feature evaluate additional information about the known geometric contour of the identification feature or the relative arrangement of several identification features present in the print image.This additional information is stored in a mask reference stored as mask setpoints for each material to be evaluated, which represents the geometric data in a suitable form
• a background setpoint is stored in the data memory as a reference, which represents the optical properties of the print image in at least part of a surrounding area that surrounds the identification feature.
• the optical reference properties of the background setpoint must differ at least slightly from the optical properties of the identification feature to be identified
• the current first electrical signal provided by the image sensor and the background setpoint are then used to form a difference value representing a difference image, at least for the expected range
• essentially all features of the print image are faded out, the optical properties of which correspond to the background setpoint. Due to their deviation from the background reference value, only position-variant areas of the identification feature and also other elements, such as printing errors or edge deviations, are reproduced in the difference image, the areas of the position variant identification feature having particularly high amplitudes.
• the difference values are compared with the mask setpoints of the mask reference and the current position of the identification feature is inferred from the result of the comparison.
• This method step is based on the consideration that the difference image is essentially determined by the depiction of the position variant identification feature, so that the actual position of the position variant identification feature can be inferred from extensive coverage between the mask reference and the difference image. If it is not possible to determine sufficient overlap between mask setpoints and difference values due to other error influences, this is harmless. B. only leads to an error display in the print image control and to the removal of the corresponding printed sheet.
• the areas of the printed image which result from the current position of the identification feature are preferably hidden during the subsequent qualitative assessment of the material, so that disruptions in the examination of the printed image due to the position variant arrangement of the identification feature are excluded.
• the recognition of the position-variant recognition feature can be improved when this method is carried out by storing a binarization threshold in the data memory. After the difference image has been formed from the current first electrical signal and the background setpoint, the Difference image, all image data whose values are below the binarization threshold are filtered out. I.e. Only those image points are preserved in the difference image that differ sufficiently significantly from the rest of the print image so that the usually other deviations, for example printing errors or edge deviations, can be hidden from the difference image.
• the procedure can be such that the mask reference is shifted until a maximum overlap between the mask reference and the difference image results.
• Various mathematical evaluation methods can be used to evaluate the coverage between the mask reference and the difference image and to find the corresponding coverage maximum.
• the overlap between the difference image and the mask reference should be calculated using suitable mathematical operations, if possible using methods of electronic data processing.
• One way of evaluating the coverage between the mask reference and the difference image is to calculate centers of gravity in accordance with the optical distribution of the pixels in the difference image and to compare these centers of gravity with the focus of the masking experts. A maximum coverage occurs when the sum of the center of gravity differences between the mask reference and the difference image is minimized.
• An arrangement for at least one further identification feature of the material is the storage of a suitable underground setpoint in the data memory.
• the underground setpoint can simply be specified as a process parameter, for example based on one or more empirical values.
• the background setpoint is specifically determined in a learning mode depending on the respective print image of the material to be tested. Two alternatives are given below.
• reference material is used in the learning mode that does not contain the position-variant identification feature.
• printed sheets with banknotes or tokens can be used for which the window thread is not present.
• the underground setpoint can be derived by evaluating this reference material without a distinguishing feature.
• the learning mode can also be carried out with reference material that contains the position-variant identification feature. If, when evaluating the printed image of the reference material, the position-variant identification features appear bright in comparison to the surrounding area, then a threshold value is selected as the background setpoint which corresponds to the values of the darkest pixels of the identification feature. When the material is subsequently checked, it is then assumed on the basis of the threshold value that, at least in the expected range, all pixels which are darker than the background setpoint value do not belong to the positionally variable identification feature. If, on the other hand, the identification feature appears dark in comparison to the surrounding area, a threshold value is selected as the background setpoint, the value of which corresponds to the brightest pixels of the identification feature.
• FIG. 1 shows a block diagram with functional units relevant to the method
• FIG. 3 shows a schematic representation of the method for checking color deviations in the recorded color image with a counter-color model
• FIG. 5 shows a flowchart of the method for checking the identification feature for its belonging to a specific class of identification features
• FIG. 8 shows the mask reference for determining the position of the position-variant identification feature in the difference image according to FIG. 7;
• FIG. 11 shows a second difference image in a schematically represented side view.
• a color camera 01 z. B. is fixed in or on a printing press so that it can use its image sensor 02 to take color images of the material 19 moving past the color camera 01 to be assessed, preferably in the running printing process, is connected to an evaluation device 03. If necessary, the image data recorded by the color camera 01 and evaluated in the evaluation device 03 can be displayed on a color monitor 04, the color monitor 04 being able to be arranged in or on a control station belonging to the printing press.
• the test methods used for the qualitative assessment of the printed material 19 are in connection with the evaluation device 03 in z. B.
• test processes in the respective signal paths preferably taking place independently of one another in the same evaluation device 03.
• the tests preferably run at least approximately at the same time, ie the test processes start at least at the same time.
• the test processes can begin after the evaluation device 03 having at least two operating modes has changed from its learning mode 48 to its working mode 49.
• Each signal path relates to a functional unit 06 for checking at least the color image from the identification feature for a color deviation from the reference image, a functional unit 07 for checking the identification feature for its belonging to a specific class of identification features and a functional unit 08 for checking the identification feature for a specific geometric contour or on a relative arrangement to at least one further identification feature of the material 19, with each test having a comparison point 11; 12; 13 carried out comparison of the first signal 09 provided and suitably prepared by the image sensor 02 of the color camera 01 with a respectively suitably defined target value 16; 17; 18 includes, the setpoints 16; 17; 18 are stored in a data memory 14 belonging to the evaluation device 03. The respective test results in the individual signal paths are again reported to the evaluation device 03 for the purpose of storage there.
• the functional units relevant to the method for the qualitative assessment of a printed material 19 with at least one identification feature can also be implemented in a machine processing the material 19.
• B. a printing press, preferably a sheet-fed printing press, in particular a sheet-fed rotary printing press, preferably nachgeord ⁇ et, but can also be arranged upstream.
• B. multiple identifying features printed sheet 19 is in the sheet printing machine at a speed of ⁇ . B. printed 18,000 sheets per hour and / or then further processed at this speed in the press sheets 19 processing machine.
• the printing speed or the further processing speed z. B. 15 m / s.
• the testing processes for assessing the quality of the printing machine or the machine processing the material 19 are computationally intensive and the speed of movement of the material 19 are high, a reliable assessment is achieved with the proposed method. Since the functional units relevant to the method for the qualitative assessment of a printed material 19 with at least one identification feature are arranged in or on the printing press or the machine processing the material 19, the location of the provision of the reference signal and the location of the test are identical. The color image and its reference image can be recorded with the same functional units, in particular with the same color camera 01, at the same location and evaluated in the same evaluation device 03.
• the color camera 01 With the color camera 01, a color image of a color printed material 19 arranged in the observation area 21 is recorded.
• the color camera 01 has an image sensor 02, preferably designed as a CCD chip 02, which converts the image information acquired in the observation area 21 into electronic image data, which form a first electrical signal 09 provided by the color camera 01 or its image sensor 02.
• a signal vector 22 is generated from each light-sensitive pixel of the CCD chip 02.
• the color camera 01 Corresponding to the number of pixels of the CCD chip 02, the color camera 01 provides a corresponding number of signal vectors 22, identified by a counting index, to the evaluation device 03 for further processing.
• Each signal vector 22 preferably has three coefficients R, G and B.
• the coefficients R, G and B correspond to the color values of the three signal channels red, green and blue, the vectorial first electrical signal 09 emitted by a pixel correlating with the recorded color of the printed material 19 at the corresponding position in the observation area 21.
• a correction vector 24 with the fixed value coefficients aa 2 and a 3 is added to the resultant vector resulting therefrom. This arithmetic operation generates first corrected signal vectors 26 which improve the color balance, the brightness and the contrast of the image data.
• the signal channel-dependent correction factors K ,, K 2 and K 3 and the coefficients ai, a 2 and a 3 of the correction vector 24 are selected such that when the reference gray values black and white are recorded, the signal vectors generated by the color camera 01 22 are transformed in such a way that the corrected signal vectors 26 obtained correspond to such desired values as result in vectors from the conversion of the known CIELAB color values.
• the first corrected signal vectors 26 are then fed to a second correction module 27.
• the second corrected signal vectors 29 result from this multiplication.
• the coefficients K 4 to K 2 of the correction matrix 28 were previously determined in a suitable iteration process in such a way that the image information contained in the first corrected signal vectors 26 relates to the color perception of the human being Eye to be approximated.
• the second corrected signal vectors 29 are then forwarded to a third correction module 31.
• signal channel-dependent correction factors are stored in a data memory 14, which are multiplied by the coefficients R, G and B to adapt the intensity values, which are dependent on the position of the respective pixels.
• the second corrected signal vectors 29 of the first pixel are multiplied by the correction factors K 13 , K 1 and K 15 in order to calculate a third corrected signal vector 32 for the first pixel.
• This correction of the second corrected signal vectors 29 is preferably carried out for all pixels of the image sensor 02.
• the third corrected signal vectors 32 are then forwarded to a fourth correction module 33.
• the coefficients R; G; B the third corrected signal vectors 32 potentiated by a factor ⁇ and the fourth corrected signal vectors 34 are calculated therefrom.
• the potentiation by the factor ⁇ takes into account the non-linear brightness transfer function of a color monitor 04, to which the fourth corrected signal vectors 34 are transferred for display.
• the result of the correction of the signal vectors 22 in the correction modules 23, 27, 31 and 33 is that the color images displayed on the color monitor 04 are adapted to the color perception of the human eye in such a way that the visual impression when viewing the display on the color monitor 04 is good corresponds to the perception of color that would arise if the printed material 19 were viewed directly.
• the image signal is recorded by an image sensor 02 in separate signal channels R; G; B.
• the signal channels R; G; B around the three signal channels red R, green G and blue B.
• Each of the signal channels R; G; B has an adjustable spectral sensitivity. This has the advantage that each signal channel R; G; B can be adapted in its spectral sensitivity to the spectral sensitivity of the respective cone of the retina of the human eye.
• the spectral content of an image is analyzed pixel by pixel.
• the image sensor signals of the signal channels R; G; B linked together.
• each image sensor signal in the counter color channel 38; 39 subjected to a non-linear transformation 41. This takes into account the digital character of the electronically generated recordings.
• the generation of the output signals 43; 44 of the counter-color channels 38; 39 is carried out analogously to the generation of the signals of the receptive fields in the human retina. That is, a link is created using the calculation rules 36; 37 of the signal channels R; G; B performed according to the linkage of the cones of the human retina.
• the image sensor signals of the red signal channel R and the green signal channel G are linked to one another by means of the first calculation rule 36.
• the image sensor signal of the blue signal channel B is linked to the minimum 46 of the image sensor signals of the red signal channel R and the green signal channel G by means of the calculation rule 37.
• the receptive fields of the human retina are characterized by a low-pass behavior. Accordingly, in the present exemplary embodiment, the signals of a low-pass filtering 47, e.g. B. with a Gauss low-pass filter.
• the learning mode 48 has the aim of the pixel-by-pixel generation of target values as reference data values, which in the subsequent working mode 49 with the output signals 43; 44 of the counter-color channels 38; 39 of the corresponding pixels are compared.
• the image contents of a reference image 52 or of a plurality of reference images 52 are analyzed in that the image contents of each pixel in three signal channels R; G; B are recorded and a subsequent perceptual adaptation of the image signals of each signal channel R; G; B is carried out and subsequently further processing of the image sensor signals is carried out according to the previously described counter-color method.
• the output signals 43; 44 of the counter-color channels 38; 39 are then stored in a data memory 14.
• a data memory 14 To allow for permissible fluctuations in the reference images 52 , it makes sense if several reference images 52 are taken into account in the learning mode 48. This makes it possible for the stored target values of each pixel to have a certain permissible fluctuation tolerance.
• the fluctuation tolerance can be determined either by the minimum / maximum values or the standard deviation from the data obtained from the image contents of the reference images 52 of each pixel.
• the working mode 49 there is then a pixel-by-pixel comparison of the output values 43; 44 of the counter-color channels 38; 39 of an inspection image 53 with the target values from the data memory 14.
• the comparison can be carried out using a linear or non-linear classifier 54, in particular using threshold value classifiers, Euclidean - distance classifiers, Bayesian classifiers, fuzzy classifiers or artificial neural networks. Then a good / bad decision is made.
• FIG. 5 shows a flowchart for signal evaluation in the method for checking the identification feature for its belonging to a specific class of identification features.
• a grid of M x N windows 56 is placed over the entire color image to be checked, where M, N> 1.
• Each window 56 advantageously consists of m x n pixels with m; n> 1.
• a square grid of N x N windows 56 is preferably selected, each window 56 consisting of n x n pixels. In the test process, the signal of each window 56 is tested separately.
• the two-dimensional color image of the spatial space is transformed into a two-dimensional image in the frequency space by one or more spectral transformations 58.
• the spectrum obtained is called the frequency spectrum. Since the present exemplary embodiment is a discrete spectrum, the frequency spectrum is also discrete.
• the frequency spectrum is determined by the spectral coefficients 59 - too Spectral values called 59 - formed.
• the amount 61 of the spectral values 59 is formed.
• the magnitude of the spectral values 59 is called the spectral amplitude value 62.
• the spectral amplitude values 62 form the feature values 62, i. H. they are identical to the feature values 62.
• the feature selection 63 follows as a further method step.
• the aim of the feature selection 63 is to select those features 64 which are characteristic of the image content of the color image to be checked.
• Both characteristic spectral amplitude values 62, which define the characteristic 64 by their position in the frequency space and by their amplitude, and linguistic variables such as “gray”, “black” or “white” are possible as characteristics 64.
• the fuzzification 66 the association of each spectral amplitude value 62 with a feature 64 is determined by a soft or unsharp membership function 67; d. H. a weighting takes place.
• the membership functions 67 can be adapted in a learning mode to target values stored as reference data records, it makes sense if the membership functions 67 are designed as parameterized monomodal, that is to say one-dimensional potential functions, in which the parameters of the positive and negative slope are separated from the target values to be examined can be customized.
• the data records of the image content, from which the feature values 62 of the color images to be checked result are weighted with the respective membership functions 67, the parameters of which were determined in the previous learning mode. I.e. for each feature 64 there is a kind of TARGET-ACTUAL comparison between a reference data record, which is expressed in the parameters of the membership functions 67, and the data record of the color image to be checked instead.
• the membership functions 67 create a soft or fuzzy association between the respective feature value 62 and the feature 64.
• interference 68 there is essentially a conjunctive link 69 - also called aggregation 69 - of all membership functions 67 of features 64, as a result of which a superordinate membership function 71 is generated.
• the next method step, the defuzzification 72 determines a specific membership value 73 or sympathy value 73 from the superordinate membership function 71.
• this sympathy value 73 is compared with a previously set threshold value 76, whereby a classification statement can be made.
• the threshold value 76 is set either manually or automatically.
• the threshold value 76 is also set in the learning mode.
• the method for checking the identification feature for a specific geometric contour and / or for a relative arrangement to at least one further identification feature of the material essentially takes place in the following steps.
• a difference image 77 is formed from printed sheets printed with banknotes 19, only a section of the difference image 77 being shown in the area of a banknote 19 in FIG. 6. 6 that the normal printed image of the bank note 19 is hidden in the differential image 77 and only the areas of the printed image which differ significantly from the background reference value are depicted as dark fields in the differential image.
• a dashed, strip-shaped expectation area 78 the position z.
• FIG. 8 shows a mask reference 82 in its geometric shape.
• the data for the width 83 and the length 84 of the window thread openings 79 are stored in the mask reference 82.
• the values for the distance 86 between the window thread openings 79 and the number of window thread openings 79 per bank note 19 are also stored in the mask reference 82.
• the mask reference 82 is shifted relative to the difference image 77 during evaluation by data technology operations until there is a maximum coverage between the mask reference 82 and the dark fields 79 in the difference image 77. If this maximum coverage is reached, the distances 87; 88, which z. B. from the current positions in the X and Y direction of the mask reference 82 relative to the edges of the banknote 19, the current position of the window thread 79 in the printed image can be concluded, so that the areas of the window thread openings 79 in a subsequent check of the printed image can be hidden.
• FIG. 10 shows a second mask reference 89, which represents dark fields 91 corresponding to eight window thread openings 91 during the examination of a bank note 19 on a concavely curved contact surface.
• Fig. 11 schematically shows a difference image 92, in which the window thread openings 91 in dark fields 93, z. B. in window threads 93 have shown.
• the dark field 94 was caused by a printing error 94 and not by a window thread opening 91.
• a window thread opening 91 in the middle did not appear in the difference image 92 due to the insufficient color difference between the background and window thread 93.
• the mask reference 89 is projected onto a projection line 96 and the light-dark distribution resulting therefrom with the light result from the projection of the difference image 92 onto a projection line 97. Dark distribution compared. This one-dimensional comparison of the light-dark distribution enables the position of the window thread 93 to be determined in one direction.
• K 4 to K 12 coefficient 1 3 K 14 , K ⁇ 6 correction factor a ⁇ , a 2 , a 3 fixed value coefficient ⁇ potentiation factor

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• 2003
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