DE4322870C1 - Method and arrangement for assessing the success of cleaning procedures on contaminated surfaces - Google Patents

Method and arrangement for assessing the success of cleaning procedures on contaminated surfaces

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Publication number
DE4322870C1
DE4322870C1 DE19934322870 DE4322870A DE4322870C1 DE 4322870 C1 DE4322870 C1 DE 4322870C1 DE 19934322870 DE19934322870 DE 19934322870 DE 4322870 A DE4322870 A DE 4322870A DE 4322870 C1 DE4322870 C1 DE 4322870C1
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DE
Germany
Prior art keywords
image
color value
arrangement
picture
cleaning
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Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
DE19934322870
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German (de)
Inventor
Rupert Kreuzkamp
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
KREUZKAMP, ROBERT, DR., 72074 TUEBINGEN, DE
Original Assignee
Otto Tuchenhagen GmbH and Co KG
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Publication date
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Priority to DE19934322870 priority Critical patent/DE4322870C1/en
Application granted granted Critical
Publication of DE4322870C1 publication Critical patent/DE4322870C1/en
Anticipated expiration legal-status Critical
Expired - Fee Related legal-status Critical Current

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Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01BMEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00Measuring arrangements characterised by the use of optical means
    • G01B11/30Measuring arrangements characterised by the use of optical means for measuring roughness or irregularity of surfaces
    • G01B11/303Measuring arrangements characterised by the use of optical means for measuring roughness or irregularity of surfaces using photoelectric detection means

Abstract

The invention relates to a method for assessing the success of cleaning procedures on contaminated surfaces by using an optoelectronic system for image acquisition, image processing and image representation. The aim is to achieve an assessment which is current and quantitative. This is ensured in terms of process engineering owing to the fact that the image of the surface to be assessed is acquired in sections, that the image section is divided into a multiplicity of pixels forming an image matrix and each pixel is assigned a digital colour value signal f_i (colour value), the respective discrete colour value being a function of the spectral energy distribution and the intensity of the radiation emitted by the pixel, that digitised image matrices are generated in temporal sequence and stored, and that in freely selectable time cycles the respective image matrices are compared over all pixels, differences are determined quantitatively and/or represented and/or read out. In order to carry out the method with the aid of relatively simple means, it is proposed to configure the arrangement from a commercial CCD camera as picture-taking (picture-recording) device (1a) and a commercial, portable personal computer (notebook) as image processing system (7a) in which an image-reproducing device (5a) is integrated in the form of a liquid crystal display (LCD display) (Figure 15). <IMAGE>

Description

The invention relates to a method for assessing the Success of cleaning procedures on soiled surfaces surfaces using an opto-electronic image acquisition, image processing and image display system and an arrangement for performing the method.

In chemical, pharmaceutical and in particular production plants especially come from the food and beverage industry automated cleaning processes today almost without exception or procedures for use, with all appendix parts are subjected to a so-called CIP cleaning den (CIP: Cleaning in place, which means something like: Cleaning on the spot in the flow). The duration of the Cleaning, d. H. the intervention of the cleaning agent the dirty surface to be cleaned, the choice of Detergent and the cleaning process itself ba are usually based on empirical empirical values, where with these often only when entering and testing the Plant can be determined and determined.

Because over the useful life of a production plant on the one hand the production conditions and on the other hand but also the operating conditions of the cleaning process rens can change, must be checked at intervals of cleaning success. this happens possibly directly by visual inspection areas of the plant that are critical for cleaning Laboratory analysis of selected, at reini arranged critical locations of the system and from of these easily removable test areas or indirectly through ongoing quality control of the product.

In this context, it has already been proposed  particularly cleaning-critical areas, for example that of a tank with an image capture device for example a video camera to monitor and on the Basis of the visual obtained in this way Information about an image display device, a commercial monitor, be represented, the success check the cleaning procedure and follow it if necessary Such a method can indeed be carried out in a timely manner cleaning procedure to be carried out from it however, the information that is extracted is of a qualitative nature and with their help it is usually not possible the cleaning process optimally and eco-friendly nomically - and thus also environmentally friendly.

The other monitoring procedures of the Cleaning success (test areas, quality control), the directly or indirectly quantitative statements about the Condition of the dirty surface to be cleaned ma can not be applied promptly and are suitable therefore only limited to assess the cleaning success and in no way for tracking and recording kinetic processes.

To assess the surface change of objects So far, a number of methods have been known the one with the respective surface via a video camera recorded and a derived from this The image signal is then processed quantitatively becomes. In this context, DE 41 39 107 C1 a method for determining and evaluating Surface changes on natural or artificial Specimens exposed to weathering described which the specimens before and during the weathering in predefinable time intervals by means of a electronic camera under defined and reproducible lighting and imaging conditions recorded, the images digitized, in a computer  saved, using the computer grayscale histograms of the pictures produced by forming the difference between the grayscale histograms of the und weathered and the weathered specimen as the surface changes Function of weathering determined and the change can be displayed graphically or in tabular form.

From DE 42 01 935 A1, an error is still determined known process, in which an illustration of a by a test object illuminated by a light Video camera recorded and an error detection of the ge checked object on the basis of an image signal from this video camera is forwarded. This method is characterized by a combination of two Inspection zones that are adjacent to one another have given size on a screen that is formed by the image signal, an accumulation of the Brightness of each of these two inspection zones on the full screen and forwarding an error assessment division if there is a difference between these saved over a predetermined threshold increases.

DE 41 33 315 A1 finally describes a Vorrich to test a surface in which to test the object is illuminated by a light source and the beam of light transmitted or reflected from the object is recorded by a video camera and one by the video camera derived image signal by an elek tronic processor for determining faulty Places on the object to be checked is processed. The known device contains, among other things, a one direction for obtaining an image signal, an analog Digital converter, a memory and one set up each device for generating a histogram signal, for setting a small window on a screen, to put a window address for scanning the memory and for  Assessment of the presence of a faulty location.

Both the operational practice and the one with the off Laying and optimization of cleaning processes for the specialist areas mentioned at the beginning have long been looking for methods of recording reini kinetic processes on the basis of timely quantitative information about the current state the dirty surface to be cleaned and the quan titative review of cleaning success. method according to the above-mentioned, printed status In any case, technology is not suitable, the first one hend mentioned cleaning problems of a be to find a peaceful solution.

It is an object of the present invention to achieve the foregoing Helping needs with relatively simple means.  

The goal is achieved by applying the features in claim 1 solved. Advantageous refinements of the method according to the invention are the subject of dependent claims. A Arrangement for performing the proposed method is by applying the features in the secondary claim 6 realized, with advantageous embodiments of the proposed arrangement subject to further subclaims are.

The invention uses the knowledge that the to be cleaned dirty surface characteristic color values points. Now, as provided by the invention, a de Finished image section in a variety of one image matrix-forming picture elements, so is everyone these picture elements have a color value. Will this color value is now captured using a suitable image recording device and finally converted into a digital color value signal f_i delt, this creates a descriptive digital image matrix, a so-called digital dirt picture. The color value and the resulting digital, discrete color value signal f_i are spectral Energy distribution and the intensity of each Image element emitted radiation dependent. The color values consist of the basic color value of the top area and / or the dirt to be removed men. Is a certain real existing dirt pattern each with a distinctive digital image matrix digital dirt pattern, assigned, whereby cleaning pro cedure on the surface to be cleaned by removing Soiling can cause color changes again in a clear and distinctive new one manifest digital image matrix. Will be in time now sequence of color value signals, digital image matrices are generated and saved, so you can in freely selectable times image matrices over all picture elements with each other compared, differences determined qualitatively and displayed and / or read out. In any case  the image evaluation takes place promptly and it can be on Because of the possibility of the success of the cleaning procedure to be assessed quantitatively, even while cleaning procedural interventions to control who made the.

It has been shown that the color value distribution h_f = f (f) a very sensitive assessment criterion for the Er follow cleaning procedures on soiled surfaces Chen represents. The color value distribution h_f is nothing than the frequency distribution of all color value signals f_i, where the frequency h_i with which a color value signal f_i is contained in an image matrix, from the sum of all Image elements of the same color value signal f_i in this image matrix results. An advantageous embodiment of the method according to the invention provides in this context that in the digitized image matrix the frequency h_i, with which contains the discrete color value signals f_i, determined and a frequency distribution h_f = f (t) over all Color value signals f_i determined the difference between two color value distributions det, and the result displayed and / or read out becomes.

In addition, according to a further embodiment of the Method according to the invention proposed that from the digitized image matrix the mean and / or the Variance of the color value distribution h_f determined and / or are shown and / or read out. Either the mean as well as the variance are further sensitive quantitative assessment criteria together connection with checking the cleaning success and the recording of cleaning kinetic processes.

Another embodiment of the method according to the Er The invention provides that if the value is exceeded or fallen short of in each case predetermined limit value for the mean value  or for the variance a signal to control a Cleaning procedure is generated. This is it possible to promptly control the cleaning procedure access.

The proposed method according to the invention is in principle suitable, any color values and color value record distributions and process them digitally. It it is understood that digital processing is a lot number of different colors a much higher Computational effort is required, as is the case for example is when only gray values are recorded and processed as color values be prepared. In one digitized with eight bits Dirt pattern of the area A = p × q, which, for example, in N = m × n picture elements is divided, each gray value g_i can be a discrete value accept between 0 and 255.

It can be advantageous to clean the Ver color stains, as this is another issue staltung of the method according to the invention provides where thereby also with a gray value processing Dirt images with higher contrast and more significant ones Gray value distributions can be determined. But it can also be useful, colored test stains to apply, the removal of the Pollution and thus the differences between gerei cleaned and uncleaned areas of the surface stand out more clearly.

In the context of the present invention, De  finances from the field of stochastics required, such as the frequency h_i mentioned at the beginning, the frequency distribution h_f and the mean and Variance of a distribution. These and other definitions are below using the example of a digitized with eight bits Dirt image in which the color value signals f_i as to simplify the relationships Gray value signals g_i are recorded and processed, in summary shown. In the following, the gray value signal g_i (hereinafter shortened to: gray value g_i) and an indexed for other stochastic sizes Spelling used, which, however, to the immediate bare explanation of equation 1 to 6 limited remains. The introduction to the description remains with until long-established spelling as a replacement for the indi graced.

In a dirt image digitized with 8 bits, each gray value g i can assume 256 discrete values:

g i ∈ {0, 1, 2,. . . , 255} (1)

The frequency h i with which a gray value g i is contained in an image matrix with N = n × m pixels (pixels = picture elements) results from the sum of all pixels of the same gray value:

h i = Σg i (2)

The probability p i with which a pixel with the intensity g i appears results from the ratio of the frequency h i to the total number N of the pixels in the image section:

The following also applies:

The mean value <g <of the gray value distribution results from the sum of all possible gray values multiplied by their probability:

The is calculated as a measure of the scatter of the gray values Variance σ²:

The arrangement for performing the method is based on an image pickup and an image display device, wherein between these two one as an image processing system functioning electronic data processing system is seen. In this data processing system the image information coming from the image recording device, which are usually available in analog form an analog / digital converter, an image memory and there via a digital / analog converter the image reproduction device fed. The image memory is with an external NEN data storage device, for example a mass storage device, connected for reading in and reading out the image information.

The proposed arrangement is relatively simple and realize inexpensively if they like this one further design provides from a Standard video camera as an image capture device, one Monitor as a display device and one Personal computers as electronic Data processing system is configured.

Since a step-by-step image processing according to the Video standard (25 frames / sec) with a simple as picture processing system used personal computer is not possible who the recorded dirt images in freely selectable Freezes in the image memory and then in data storage, such as a hard drive, abge  saves. In this way, a Biblio thek dirt images of different cleaning processes archive and make available for later analysis.

For numerous applications, it is advantageous if the proposed arrangement is portable that, for example, for mobile use in art which and users can be used. This is after a further advantageous embodiment of the arrangement provided according to the invention that as an image recording device a CCD camera and as electronic data processing plant a portable Personal computer (for example notebook) in which the image reproduction device in the form of a liquid crystal display is integrated, are provided. The CCD camera has one Light detector with which the black and white images more precise and with a higher resolution than with a Standard video camera is possible, can be recorded nen.

To the accuracy of the proposed method and A white sees improving its reproducibility tere embodiment of the arrangement according to the invention before, the lens of the image recording device via an Ab shielding the area between the lens and a Shields the observation zone from incident external light, to sit on the observation zone, being an object lighting is provided within the shield. On this way you get defined lighting conditions nisse that are free from outside light influences.

Use and implementation of the procedure according to the Erfin are based on three use cases shown. Gray value signals g_i (hereinafter briefly: Gray value g_i) as an example in the place of Color value signals f_i according to the invention. In addition, exemplary embodiments the arrangement for performing the proposed Ver described. It shows  

Figure 1 is a schematic representation of a first arrangement for performing the method according to the invention.

Fig. 2 shows a schematic representation of the Anordnungsver ratios of an image recording device in connection with a first application;

Fig. 3 is also a schematic representation of another arrangement of the image pickup apparatus in a second application;

FIGS . 4 and 5 a basic histogram and a difference histogram in connection with the first use case according to FIG. 2;

Fig. 6 is a from the histograms shown in Fig 4 and 5 resulting Abtragshistogramm.

Fig. 7 to 9, a basic histogram or difference histogram or Abtragshistogramm in connection with the second application case of FIG. 3;

Figs. 10 to 12, three in time series in conjunction with a third application recorded dirt photos

FIG. 10a to 12a, the dirt images according to Figure 10 to 12 respectively associated basic histograms.

Fig. Is a time chart of the mean value <g <(equation (5)) as t_a and t = t_e result 13 of the gray value distribution of a plurality of basic histograms between the instants t =;

Fig. 14 is a representation of the time course of the variance (equation (6)), as it results from the basic histograms shown in FIG. 13 and

Fig. 15 is a schematic representation of a second embodiment of the arrangement for performing the method according to the invention.

An image pickup apparatus 1 (Fig. 1), for example, a standard video camera, directs the Won nenen image information via a line 8 a one functioning as the image processing system electronic Since tenverarbeitungsanlage 7, for example a personal computer (PC), too. There, the present image information in analog form are converted in an analog / digital converter 2 into digital image information and via a line 8 b are read for the purpose of temporarily storing in an image memory. 3 From there, the image information is transmitted via a line 8 c to a digital / analog converter 4 and is displayed in “real time” on an image display device 5 , for example a monitor, connected to the latter via a line 8 d. The image memory 3 is connected via a line 8 e to an external data memory 6 , for example a mass memory designed as a hard disk or tape. In the data memory 6 , the dirt images temporarily stored in the image memory 3 at freely selectable time intervals can be read into a library and archived so that they can be read out for later analysis at any time.

A first application of the method according to the invention is documented in FIG. 2. On an observation zone 9 , a soiled surface to be cleaned, there is a defined, limited contamination, a dirt spot 11 . The observation zone 9 consists of a gray plastic surface and the dirt spot 11 is a recorded black circle with a diameter d = 3 cm. A selected image section 10 with the dimensions A = p × q = 5 cm × 5 cm = 25 cm² is digitized once with and after the dirt stain 11 has been removed with a damp cloth, once without a stain. The digitized image section 10 contains an image matrix with N = 251 × 251 = 63 001 picture elements. The front edge of the lens of the standard video camera 1 is arranged at a distance 1_3 from the dirty surface. Object lighting 12 has the distances 1_1 or 1_2 indicated in the figure from the front edge of the lens of the standard video camera 1 or from its housing.

The image section 10 is digitized in the initial state a (black circle on a gray plastic surface) and in the final state e (cleaned gray plastic surface). The respective gray value distributions for the initial state a and the final state e result from the digitized image matrix in a basic histogram ( FIG. 4). The gray value distribution in the range of the gray value g = 15 results from the black spot in the image section 10 , the contamination 11 . The gray value distribution of the gray plastic surface is shown in the range g = 65 to 95. After cleaning, the gray value distribution marked with e results. It can be seen that the gray value distribution representing the dirt spot 11 has disappeared in the region g = 15; for this, the gray value distribution of the cleaned plastic surface has expanded into the area of higher gray values, whereby increasing gray values mean lighter and decreasing darker gray. Since each change in the gray value of a picture element causes a change in the gray value distribution, ie a decrease in the frequency of a certain gray value leads to an adequate increase in the frequency of another gray value, the areas under the distribution curves a and e are the same.

Subsequently, the difference d = e - a between the gray value distributions a and e takes place, which can be found in the difference histogram ( FIG. 5).

The difference distribution is shown again as a so-called removal histogram in FIG. 6. One recognizes on both sides of the scaled gray value axis g areas hatched in opposite directions, the area marked with A_g representing the difference distribution of the resulting cleaned surface and the areas below the gray value axis g representing the difference distribution of the soiled surface A_v eliminated. After cleaning, the dirt is on the one hand the stain 11 and on the other hand, soiling has also been removed, which are within the gray scale range of the plastic surface. From the distribution h_v = f (g) (difference distribution below the gray value axis g), the total area A_v of the surface cleaned of dirt and the partial areas A_v1 (dirt spot 11 ) and A_v2 (basic contamination of the plastic surface 9 ) are calculated.

On the basis of the method according to the invention documented in FIGS. 4 to 6 in connection with a simple application case, it becomes clear how, with its help, the success of cleaning procedures can be quantitatively assessed and interpreted.

A second application, a cleaning test under real conditions, is shown in FIG. 3 with regard to the test setup on which it is based. Gray plastic boxes (observation zone 9 ) are cleaned as part of a cleaning experiment in an industrial car wash. To evaluate the removal, test areas (image sections 10 ) which have contaminants 11 are marked (image sections with an area A = 5 cm × 5 cm = 25 cm²) and numbered. The test surfaces 10 are digitized both before cleaning and after cleaning. The meaning of the distances 1_1, 1_2 and 1_3 has already been explained in FIG. 2.

The results of the evaluation of the second application are plotted in the illustrations in FIGS . 7, 8 and 9. The gray value distribution of a certain test area in the initial state before cleaning (gray value distribution a) and those in the final state after cleaning (gray value distribution e) can be found in the basic histogram according to FIG. 7. The gray values of both the contaminated and the cleaned surface are in the range g = 105 to 150.

From the difference histogram (distribution d = e - a) it can be seen that the removed dirt is in the region of the basic gray value of the surface ( FIG. 8).

With the help of the removal histogram ( Fig. 9), the worn-off contamination can be quantified. The result is:

A_v = A_g = 13 920 picture elements,

ie the removed, soiled surface A_v is equal in area to the resulting cleaned surface. Since, in the present second application, the image section 10 is divided into N = 361 × 361 = 130 321 picture elements, soiling was removed during the cleaning process, which covered a proportion of 13 920/130 321 picture elements, ie 10.7% of the total surface.

In a third application, test areas are cleaned under real conditions, and the intended image sections are digitized at certain time intervals. The respective dirt patterns are shown in FIGS. 10, 11 and 12. The dirt image shown in FIG. 10 is received (the surface before cleaning state) at time t_a. The associated histogram of the gray value distribution g is shown in FIG. 10a. The mean value <g <can be calculated from the gray value distribution according to equation (5) and the variance according to equation (6). The corresponding results are shown in the illustration. The procedure is the same at a time t = t_i = 35 seconds and at a time t = t_e = 180 seconds (final state e after cleaning). The respective results can be found in FIGS . 11a and 12a.

The examination at time t = t_i is selected here as a representative for a large number of possible adequate examinations over the entire duration of the cleaning process. The mean value and variance curves resulting from the examination are shown in FIGS . 13 and 14, respectively.

The courses can each be divided into four areas I to IV, whereby the focus should first be on the mean course ( Fig. 13). In area I the wetting of the soiled surface and the swelling of the soiling take place. Both processes lead to a lightening of the surface and thus to an initially strong and then to a more moderate increase in the mean gray value up to a maximum for t = t_i. For times t_i <t <42 seconds (area II) the detachment of isolated dirt particles begins. A large-scale detachment of the contamination is observed for times 42 <t <60 seconds (area III), the removal leading to a steady and very strong decrease in the mean gray value. In the subsequent area IV, residual contamination on the surface is detached, whereby the mean gray value decreases to the basic gray value of the cleaned surface (area IV; t = t_e). In the present case, the cleaned surface has darker gray values than the dirty one.

The variance curve ( FIG. 14) confirms knowledge gained from the mean value curve on the one hand, and on the other hand provides additional quantitative information about the gray value distribution. As is well known, the variance is a measure of the spread of the values of the distribution function, with small variance meaning a slim and large an abundant distribution function. The wetting of the surface (area I) is documented in the course of the variance by a sharp increase, ie the gray value distribution becomes wider, with the surface turning whitish in the present case. In the further course of area I there is a slight decrease in the variance, ie a somewhat slimmer gray value distribution. This can be explained by the fact that the swelling causes the gray color to stabilize over large areas of the surface. In area II, the distribution becomes slimmer due to the detachment of individual dirt particles, the variance decreases.

Due to the large detachment of dirt in the Area III is now initially an enlargement Variation up to a maximum for t = 50 seconds then there is a very strong decrease a. The gray value distribution is bimodal in this area; this means that parts of the surface too taking on their basic gray value in the cleaned state uncovered, but other subareas Show gray value of the pollution. In itself closing range IV (t <60 seconds) takes place through the removal of residual dirt on the surface a gradual approximation of the variance to the value for the slim gray value distribution of the cleaned art fabric surface.

In order to be able to carry out cleaning-kinetic processes and to check the cleaning success on site at the customer or user, a second embodiment of the arrangement for carrying out the method according to the invention shown in FIG. 15 is proposed. Instead of the personal computer (PC) used in the arrangement according to FIG. 1, a portable personal computer (notebook) 7 a is now provided, in which an image display device 5 a in the form of a liquid crystal display (LCD display) is integrated. Moreover, the electronic data processing system 7 is constructed a principle in the same way as those in the arrangement shown in Fig. 1. In place of the standard video camera 1 in the Anord voltage of FIG. 1 used is now a CCD camera provided which by themselves distinguishes a light detector that can take black-and-white images more precisely and with a higher resolution than is the case with the standard video camera 1 . In addition, the lens of the CCD camera 1 a via a shield 13 , which shields the area between the camera lens and an observation zone 9 from incident external light, is placed on the observation zone 9 , with lens illumination 10 being provided within the shield 13 . With the struck second, very compact arrangement, one can meet the requirement for a mobile and flexible use of such arrangements; on the other hand, the quality of the digitized image information and its reproducibility are extremely high.

It is understood that those with the method according to the Er quantifiable assessment criteria are more diverse than described above and themselves the specialist applying the method with increasing Er increasingly gain experience in dealing with this. So it is not possible at this point to deal with everyone the assessor opening the method according to the invention related possibilities of interpretation and interpretation with cleaning kinetic processes or checking of cleaning success. What is certain is that suggested method demonstrates the success of Cleaning procedures on dirty surfaces promptly to be assessed quantitatively.

Claims (9)

1. Procedure for assessing Reini's success procedures on dirty surfaces Use of an opto-electronic image acquisition, Image processing and imaging system, with the characteristics that the image of the subject to be assessed Surface captured in sections that the image from cut into a variety of form an image matrix divided into picture elements and each picture element a digital color value signal f_i is assigned, the respective discrete color value signal from the spectral energy distribution and the intensity of the is dependent on the radiation emitted, that in chronological order from the color value signals digitized image matrices are generated and saved and that in freely selectable time cycles the ever image matrices over all image elements compared to other, differences quantified and displayed and / or read out.
2. The method according to claim 1, characterized in that that in the digitized image matrix the frequency h_i, with which the discrete color value signals f_i in these are included, found and a common distribution h_f = f (t) over all color value signals f_i determines the difference between two color value Distributions formed and the result shown and / or read out.
3. The method according to claim 1 or 2, characterized records that from the digitized image matrix Average and / or the variance of the color value distribution lung h_f = f (f) determined and / or represented and / or can be read out.  
4. The method according to claim 3, characterized in that when falling below or exceeding one in each case given limit for the mean or for the Variance a signal to control a cleaning pro cedure is generated.
5. The method according to any one of claims 1 to 4, characterized characterized that the surface to be assessed is colored.
6. Arrangement for performing the method according to one of claims 1 to 5, with an image recording and an image reproduction device, with the features that between the image recording device ( 1 ; 1 a) and the image reproduction device ( 5 ; 5 a) as an image processing system functioning electronic data processing system ( 7 ; 7 a) is provided, in which the image information via an analog / digital converter ( 2 ) an image memory ( 3 ) and from there via a digital / analog converter ( 4 ) the image display device ( 5 ; 5 a) are supplied, the image memory ( 3 ) being connected to an external data memory ( 6 ) for reading in and reading out the image information.
7. Arrangement according to claim 6, characterized in that as a picture recording device ( 1 ) a standard Videoka mera, as a picture display device ( 5 ) a monitor and as an electronic data processing system ( 7 ) a personal computer are provided.
8. Arrangement according to claim 6, characterized in that as an image recording device ( 1 a) a CCD camera and as an electronic data processing system ( 7 a) a portable personal computer in which the image reproduction device ( 5 a) is integrated in the form of a liquid crystal display, are provided.
9. Arrangement according to one of claims 6 to 8, characterized in that the lens of the Bildaufnahmege advises ( 1 ; 1 a) via a shield ( 13 ), the loading area between the lens and an observation zone ( 9 ) of incident external light shields, is placed on the observation zone ( 9 ), object lighting ( 10 ) being provided within the shield ( 13 ).
DE19934322870 1993-07-09 1993-07-09 Method and arrangement for assessing the success of cleaning procedures on contaminated surfaces Expired - Fee Related DE4322870C1 (en)

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DE19842989B4 (en) * 1998-09-21 2004-09-16 Hrch. Huppmann Gmbh Brewery system with camera surveillance
DE19524498B4 (en) * 1995-07-05 2008-09-04 Mahr Multisensor Gmbh Image processing system
DE102010005616B3 (en) * 2010-01-25 2011-06-16 Federal-Mogul Burscheid Gmbh Device for evaluating roughness or homogeneity or quality of e.g. sharpened surface of piston ring, has evaluation components determining portion of bright and dark image points, where roughness is evaluated based on certain portions
DE102010031564A1 (en) * 2010-07-20 2012-01-26 Krones Aktiengesellschaft Intelligent control of a bottle washing machine
DE102010053989A1 (en) * 2010-12-09 2012-06-14 Heidelberger Druckmaschinen Ag Device for controlling cleaning operation of cleaning device in offset printing machine, has sensor that transmits sensed surface data of to-be-cleaned component in printing machine to control computer, to control cleaning operation
WO2013041608A1 (en) * 2011-09-20 2013-03-28 Dürr Ecoclean GmbH Cleaning system
DE102015106777A1 (en) * 2015-04-30 2016-11-03 Marianne Zippel Method and inspection system for determining and checking the surface cleanliness of industrially cleaned workpieces or machine components

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