EP2433245A1 - Dispositif et procédé permettant d'identifier l'auteur d'une oeuvre d'art - Google Patents

Dispositif et procédé permettant d'identifier l'auteur d'une oeuvre d'art

Info

Publication number
EP2433245A1
EP2433245A1 EP10728585A EP10728585A EP2433245A1 EP 2433245 A1 EP2433245 A1 EP 2433245A1 EP 10728585 A EP10728585 A EP 10728585A EP 10728585 A EP10728585 A EP 10728585A EP 2433245 A1 EP2433245 A1 EP 2433245A1
Authority
EP
European Patent Office
Prior art keywords
image
characteristic features
characteristic
features
database
Prior art date
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.)
Ceased
Application number
EP10728585A
Other languages
German (de)
English (en)
Inventor
Werner Scholzen
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.)
Individual
Original Assignee
Individual
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Individual filed Critical Individual
Publication of EP2433245A1 publication Critical patent/EP2433245A1/fr
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/7715—Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
    • G06K7/10—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition

Definitions

  • the present invention relates to apparatus and methods for detecting the origin and authorship of images.
  • Rembrandt is such a significant example.
  • a high technical standard based investigation apparatus which is used for checking the works of art.
  • UV-lighted materials fluoresce in different colors and thus provide information about the material underlying the work of art. From this conclusions about the respective artist can be deduced.
  • Further methods for the determination of forgeries are the thermoluminescence analysis, elaborate chemical analyzes or the examination of the works of art under a microscope. However, these methods are very complex and must, moreover, be carried out in suitable locations, usually laboratories, that is to say places which are not caused by the radiation to cause any damage to the health of the personnel, or climatically controlled premises which condition the image Take care.
  • art experts can often determine the originator of a work of art, especially a picture, with their trained eye alone. They have acquired the ability to associate an artist as the author with a particular image in the course of their training and working activities.
  • the brushstroke is generally the stroke in painting. He can express the importance of each area of the image for each artist. For example, in an image, the lines on the face can be made finer than the style of the lines that the artist used in his clothes. Hietan one can recognize that the artist has placed particular value on the design of the facial expression or the face itself. Det brushwork is thus the personal signature of an artist.
  • a work of art especially an image, can be identified with the help of this personal signature of the artist, ie the author, and then assigned to the correct author. This is currently only possible with the help of a visual inspection of the image by an art expert.
  • the art expert must study the image carefully and carefully examine. Such a process is time consuming and usually very expensive, as the art experts are highly skilled experts.
  • expert knowledge is retrieved by the art expert via a database. It depends only on the access speed to this database and the algorithm used to search in the database, as well as the size of the records to be examined and of course the amount of relevant records in the database, how quickly a result can be presented here. Determination of characteristic features in the data record in the context of this application is to be understood as recognition of characteristic features in the data record (and then, of course, finally in the image to be examined) or as identification of characteristic features in the data record.
  • the transfer of the image or parts of the image into at least one data set by means of a digitizing means in most cases means scanning the image with a scanner. But it is also possible to use a corresponding digital camera. This has the advantage that the digitization with the help of the camera can be made while traveling. This provides a simple and quick way to gain digitized data while ensuring great mobility, especially without transporting the artwork, ie directly at the exhibition site.
  • a scanner also constitutes such a data acquisition device which scans or measures an object, in particular an image, in a systematic and regular manner. In this case, a large number of individual measurements are used to generate an overall image of the object to be scanned or of parts thereof. For 3D artwork, a 3D scanner can be used.
  • the analysis to be carried out according to the inventive method the data sets obtained by scanning, and the determination of the characteristic features, which naturally exist in their digital form in the data record, are carried out with the help of the templates of the characteristic features stored in the database. These patterns are compared with the image to be examined, whether these patterns are found, either in identical form or with a great similarity in the image to be examined.
  • characteristic features in particular points or lines or dot or line groups or patterns, in the image to be inspected requires the ability to recognize regularities, repetitions, similarities or laws in a set of data, recognizing similarities has the greatest chance of success.
  • Such a capability can be provided by the methods of pattern recognition.
  • Other characteristic features can be edges, color edges, the course of colors, ie the color gradient and the brushstroke itself.
  • the scanned and inspected image is subjected to image analysis, which is described below including its preparatory steps. This analysis greatly facilitates the subsequent comparison with the patterns available in the database.
  • the image to be inspected is subjected to a preparation step.
  • the image is normalized so that it can later be compared with the patterns to be compared. If necessary, interfering impurities can also be suppressed by the use of filters. It is also possible to convert the colors of the image to shades of gray, if this is helpful in the following steps. Hietnach the image can be divided into segments, so homogeneous areas, ie areas with, for example, the same texture or the same color, can be combined to save space.
  • the feature extraction is performed.
  • the methods for obtaining characteristics are mostly methods that are obtained at best by intuition or based on many years of experience of a specialist.
  • Important features can be processed into feature vectors.
  • Features in the images may be straight lines, circular arcs, circles, ellipses or other geometrically writable groups. From several of these features or even from a feature alone, the characteristic feature, which can be assigned to the author exist.
  • the extracted features and patterns are classified in a further step, so subjected to a classification process.
  • the classification method also called the classifier, divides the extracted features and patterns into classes.
  • Classification procedures are already known in the art. There, manual, automatic, numerical and non-numerical statistical and distribution-free or even dimensioned and learning methods are known. There are also other methods known, so that the list given above is not intended to be limiting.
  • the classification can be made, for example, with the aid of a Bayes classifier. This classifier assigns each feature exactly to the class to which it is most likely to belong.
  • the features thus identified and analyzed are compared with the characteristic features present in the database. If one or even more matches are achieved here, then in addition to the name of the author, an assigned data record with further information about the author of the picture can also be output.
  • this is characterized in that the resolution of the digitizing means, preferably a scanner, can be freely adjusted.
  • the Hough method is also used to analyze and determine the characteristic features in the image to be tested or in parts of the image to be tested.
  • the Hough method is a method which is based on the transformation of the same name, namely the Hough transformation. This is suitable for detecting straight lines, ellipses and other geometric objects. From these objects, the characteristic features that are to be found or composed are composed.
  • the Hough method is still a very robust method with which the structures of lines can still be identified in a noisy picture, ie a picture in which the geometric objects can not be clearly identified. So not only complete lines, but also line segments or other segments and parts of the geometric objects can be determined.
  • this is characterized in that the method in a further step determines reference features of the characteristic features or of parts of the characteristic features contained in the dataset, the reference features of the characteristic features either already in the database are stored or while running Procedure are generated.
  • the reference feature to a characteristic feature in the sense of this application is generated by a change in the characteristic feature of just this. For the human eye, only slight differences between the reference feature and the characteristic feature can be seen.
  • the characteristic features or parts of the characteristic features are simply enlarged or reduced, and it may well be the case that more than one characteristic feature or part thereof is processed. It is even possible, on the one hand, to enlarge one part of the characteristic feature, but on the other to reduce the size of another part.
  • the reference features are generated by stretching or compressing the respective characteristic feature or at least part of the characteristic feature.
  • the characteristic feature is more distorted.
  • such a manipulation can certainly lead to a positive result, if the characteristic feature had forced a compression or extension in the creation of the work due to the present image geometry of this image to be tested.
  • these manipulations of Characteristic features or parts of these that one part can be compressed, another part but stretched.
  • At least one reference feature is generated by changing a line curvature.
  • the reference feature is generated by changing an angle between at least two lines of the respective characteristic feature.
  • the present inventive method can also be applied only to parts of images, so image sections. In this case, only parts of these images that need to be checked are digitized. This may be the case when a particular "characteristic feature” catches the eye, but the viewer is not sure whether this particular "characteristic feature” is an original or a forgery to deceive the viewer.
  • Such a method can, for example, run on a device which has at least one digitizing means, preferably a scanner, a normalization module, a segmentation module, a classifier module and a database module. Furthermore, at least one storage medium and at least one data processing unit for such a device are conceivable.
  • FIG. 1 schematic sequence of the method
  • Fig. 3 is a schematic representation of a color edge
  • FIG. 4 schematic representation of a color gradient Fig. 5 Black / white copy of an original with five marked image areas
  • 5a to 5e show the marked image areas of FIG. 5
  • FIGS. 6a to 6e show the marked image areas of FIG. 6
  • Fig. 1 shows the schematic sequence of the method according to the invention, as well as the use of some of the necessary modules.
  • a digitizing means in this case a high-resolution scanner.
  • the scanned image is probably a miniature artwork by the very important artist XY, who painted this image on the back of an antique matchbox.
  • the work has suffered considerably and the fact that the matchbox was previously used as intended was not conducive to the quality of the back. For this reason we worked with a very high resolution.
  • the individual, barely recognizable to the naked eye image objects were digitized so that as little details are lost.
  • the high resolution is also used to better detect and suppress possible ambiguities, such as contamination of the pad, in the subsequent process.
  • the high resolution generates a large amount of data, the small size of the image compensates for this disadvantage.
  • today's technology is quite capable of efficiently storing and managing very large amounts of data.
  • the image is normalized with the aid of a normalization module 2. This serves for better comparability of the respective found characteristic features with the patterns present in the database 5. Since the patterns present in the database 5 are also normalized, so at least the size ratios of the individual characteristic features are similar. An artist will always carry out his characterizing movements, which manifest in the brushstroke, in roughly the same manner, so that in most cases the structure and magnitude of the result of these movements will be similar.
  • the image is divided into segments in the following step. This is useful in the present case, since the image consists mainly of four features, namely stroke, circle, heart and sun.
  • segmentation module 3 The module which performs these steps is called segmentation module 3.
  • the stroke is assigned to the class of ⁇ strokes ⁇ , the circle of the class of ⁇ circles ⁇ .
  • the heart and the sun may not be stored as classes in the database and thus can not be found.
  • the characteristics are assigned to a wrong class.
  • the heart can be assigned to the class of ⁇ triangles ⁇ or the class of ⁇ deformed triangles ⁇ .
  • the object heart is a characteristic feature of the artist to be found, it is also deposited in its entirety as a class of ⁇ hearts ⁇ in the database and then, in all likelihood, in the same, albeit false class, namely the class ⁇ deformed triangles ⁇ .
  • the characteristic features present in the database were also classified by the classifier 4, so that in the case of identity or great similarity the classifier 4 as a rule carries out the same classification. If only parts of the object heart are to be identified as a feature, then these are classified into the respective classes.
  • a heart can be subdivided into two ellipsoidal segments with an adjoining straight line. As can be easily seen, the sun can be divided into a circle and a number of triangles adjoining it.
  • the database 5 with the present patterns of the characteristic features may be any commercially available database. Only the connection to the respective modules, which provide the features to be compared, is important.
  • the database 5 present in this example includes, among other things, the following four characteristic features, namely stroke, star, double arrow, and heart.
  • the heart from the database is almost identical to the present heart, which can be found in the image to be examined.
  • the sun of the image to be examined also shows great agreement with the sun, which was found in the database.
  • the object sun (not shown here) is further broken down into its details in an intermediate step and then compared with the characteristic features of the database 5.
  • the sun's association of the database with the sun can be verified on the image under test.
  • the database 5 There is no equivalent in the database 5 for the object circle. Since all found characteristic features of the database 5 indicate the same originator, the result was unique in this present example case. It was indeed the artist X Y.
  • the result output 6 is supplemented by further, also in the database 5 information on the respective artist, so that the request not only determines the artist, but also more information about the artist's work and effect and outputs via an output module, such as a screen or printed on paper outputs.
  • a hook can consist of two touching straight lines.
  • a straight line can be given mathematically by its perpendicular distance r to the origin of the coordinates as well as by the angle ⁇ between the corresponding connecting path and a coordinate axis.
  • the division of the image into pixels now represents a suitable coordinate system for this purpose, the coordinate jump should lie in the lower left corner, the horizontal with the values x (i) and the vertical with the values y (i) is called (with i as a passing natural number).
  • the section shown in Figure 2 has 13 pixels in the horizontal direction, thus i runs from 1 to 13, that is, x (1), x (2), ..., x (13). In the vertical, 12 pixels are visible, so i runs from 1 to 12, so y (l), y (2), ..., y (12).
  • the easy for the human eye identifiable straight line passes over the pixels with the value pairs (x (2), y (10) ⁇ , ⁇ x (3), y (9) ⁇ , ⁇ x (4), y (8) ⁇ , (x (5) , y (7) ⁇ , (x (6), y (6) ⁇ , ⁇ x (7), y (5) ⁇ , ⁇ x (8), y (4) ⁇ , ⁇ x (9), y (3) ⁇ , then the line turns in a different direction In the clipping additional pixels are colored, thus marked with an X.
  • This line can also be represented by a series of value pairs (r, ⁇ ) For all value combinations (r, ⁇ ), whether the pixels lying there all correspond to one color or not.If this color is different from the color of the environment of these pixels, the line is visible and represents a straight line in the image for the viewer. All other geometrical shapes obey other mathematical formulas, but can still be found and determined using the same method adjacent pixels are also recognized as a straight line or part of a straight line. For two directly adjoining and touching straight lines can be recognized by a viewer as a wider straight line. The more of these straight lines lie next to each other without a gap, the thicker the line is perceived as a line in the picture by the viewer.
  • Fig. 3 shows a schematic representation of a color edge, wherein the shades of white and black each fill a range.
  • the black-colored color range starts at the value xl and ends at x2, the color edge.
  • the white colored area begins at x2 and ends at x3.
  • the value curve is also shown schematically in the RGB space, whereby only the R value (marked in bold in the value triple (R 3 B 5 G)) is plotted against the length of the color areas, which are marked as x values.
  • the white area is assigned the RGB code (255,255,255) and the black area is the RGB code (0,0,0).
  • the curve is relatively simple.
  • the black color range has the RGB value (0,0,0) over its entire length, here in this range the curve runs constantly and steadily. At the color edge at the length value x2, there is a point of discontinuity. Throughout the white area, the curve has the RGB value (255,255,255), so it's constant and steady again.
  • Such a curve can be stored as a characteristic feature, thus stored as a record in the database and stored.
  • fault tolerances can be included as additional information in the data set, so that smaller deviations of the features in the image to be tested are recognized by the characteristic features stored in the database.
  • Fig. 4 shows a schematic representation of a color gradient. The color gradient is shown here from left to right, from white to black. He goes through the grays continuously.
  • the value curve in the RGB space is also shown schematically, whereby only the R value (marked in bold in the value triple (R, G, B)) is plotted against the length x. Again, the RGB value goes through the values from (0,0,0) to (255,255,255).
  • the curve in this case is a straight line with a linear slope, according to the formula:
  • R a * x, where a is the slope of the curve.
  • Such a curve can also be stored as a characteristic feature, and thus as a record in the database and stored.
  • error tolerances can also be included as additional information in the data record in this case, so that smaller deviations of the features in the image to be checked from the characteristic features stored in the database are detected.
  • FIGS. 5 and 6 show a selection of a plurality of sections from three images, wherein the difference between original and presumed imitation is to be made clear by means of an exemplary explanation.
  • Fig. 5a is a picture section (marking: 2009 0317-3-001) in the lower right quadrant of the picture, which in the colors orange, white, gray, HIa, black and ocher, as well as individual intermediate stages of these shades, a stylized tube shows.
  • the color transition from the dark area to the bright area ending at the black horizontal line is particularly characteristic of the artist's technique and thus represents a characteristic feature that is incorporated into the database.
  • the color gradient seen from top to bottom, changes from a dark hue (black) continuously into a light hue (yellow-white). Designing a color gradient just as carefully requires not only a special craft and artistic talent, but also a sophisticated technique.
  • Such a continuous transition can be accurately represented, for example, by an equally continuous function in the RGB color space and is thus easy to digitize.
  • Fig. 5b shows a completely black image section, which is also located in the lower right quadrant, but to the left of the first image section and towards the center.
  • the brushstroke which is so characteristic of the artist, becomes visible, which is reflected here in the uniform color density and color. This too can be mathematically described by a function.
  • Fig. 5c of this image section again illustrates an example of how the artist designed a gradient.
  • the image section is located in the lower left quadrant of the image and shows the shadow play on the surface of a pipe. Again, this is true for IU
  • the fourth image section is located in the left upper quadrant of the image and shows the artistic treatment of the color edges that separate different color areas.
  • the respective color areas are subdivided by a color edge which discretely changes to the hue of the respective color area.
  • the color areas themselves have the same color throughout, as can already be seen in FIG. 5b.
  • the RGB value of the first color range and the RGB value of the second color range are approximately constant within their color ranges. At the color edge, however, the RGB bet changes abruptly, the mathematical function described here has a discontinuity at this point.
  • Fig. 5e The fifth picture detail from the upper right quadrant of the picture again shows the shading on a tube and clearly shows the way in which the artist creates a shadow with the aid of a gradient from the light into the dark.
  • Fig. 6 now shows a copy of a presumed imitation of a work by the artist JG Müller, which is to be checked for authenticity.
  • JG Müller For this purpose, for example, also scanned five image areas and then compared using the inventive method with the deposited in the database characteristics of the artist. It is natural It is also quite possible to automate this process by treating the image either as a whole or subdividing it into arbitrarily selected segments using a program. These segments are then individually examined for possible characteristic features using the invention.
  • Fig. 6a shows the first marked image area (2009 03 17 -2-001) of the alleged imitation.
  • the viewer perceives a color edge in the image area, which separates a yellow color area from a yellow-green color area. It turns out, however, that the color edge does not separate the two color ranges from each other, as the artist J. G. Müller realized in his pictures.
  • the edge itself has a darker hue than the subsequent color range, wherein the subsequent green color strip in itself also has no uniform color.
  • a comparison with the data records stored in the database does not give a positive agreement with the data records available there.
  • the color edge as the characteristic feature, separates two color areas which have the same color tone throughout. This can not be described by a function having the selection criteria described above.
  • Fig. 6b shows the shadow of a tube. This section is very similar to Figure 5c, but the shades do not change slowly, but their transitions are more abrupt. This can then by no means be described as an approximately linear curve in the RGB color space. Thus, no characteristic feature can be found in the database which can be assigned to this image detail.
  • Fig. 6c and Fig. 6d shows the representation of color edges. If one compares this presentation shown here with the representation in the image detail Fig. 5a, it is easy to see that, even in this case, the feature of color edge matching does not correspond to the feature stored in the database.
  • Fig. 6e shows the representation of a color gradient. Again, the above applies.
  • Figures 7 and 7a show a copy of a work by the artist Max Clarenbach, born in 1880 in Neuss and died in 1952 in Wittlaer. Max Clarenbach was a German painter and co-founder of the Sonderbund in Dusseldorf. His nuance-rich style of painting was determined mainly by the Impressionists.
  • This copy and the accompanying detail is a landscape image showing a snowy landscape along a river. This picture clearly illustrates how an artist can be identified by his brushwork and brushstroke. Here you can clearly see the constantly repeating semi-circular, sweeping brushwork, which probably runs from left to right and therefore ends with a color patch on the left side.
  • FIG. 8 shows a presumed imitation of the painting style of the artist M. Clarenbach. Looking more closely at the sky here, one sees beyond any doubt that the creator of this painting has guided the brush differently, not always from left to right, but also from top to bottom. Also, the individual brushstrokes do not have the characteristic arcs and the color cast produced thereby.
  • FIG. 9 again shows a possible comparison between the possible characteristic features color edge, color gradient and brush stroke.

Abstract

L'invention concerne un procédé permettant d'identifier l'auteur d'un tableau et comprenant au moins les étapes suivantes: transposition du tableau à étudier ou de parties du même tableau en un ensemble de données à l'aide d'un moyen de numérisation, en particulier d'un scanner, analyse du ou des ensembles de données et définition d'éléments caractéristiques ou parties d'éléments caractéristiques, en particulier de points ou de lignes ou de groupes de points ou lignes ou bien de motifs, contenus dans l'ensemble de données sous forme numérisée, les éléments caractéristiques à définir étant déposés dans une banque de données, la banque de données associant à chacun de ces éléments caractéristiques un ensemble de données associé supplémentaire.
EP10728585A 2009-05-20 2010-05-17 Dispositif et procédé permettant d'identifier l'auteur d'une oeuvre d'art Ceased EP2433245A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
DE102009022147 2009-05-20
DE102009041757A DE102009041757A1 (de) 2009-05-20 2009-06-04 Vorrichtung und Verfahren zum Herkunftsnachweis und Urheberschaft von Bildern
DE102009023756A DE102009023756B4 (de) 2009-05-20 2009-06-04 Verfahren zum Herkunftsnachweis und zur Urheberschaft von Bildern
PCT/DE2010/000534 WO2010133204A1 (fr) 2009-05-20 2010-05-17 Dispositif et procédé permettant d'identifier l'auteur d'une oeuvre d'art

Publications (1)

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EP2433245A1 true EP2433245A1 (fr) 2012-03-28

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EP10728585A Ceased EP2433245A1 (fr) 2009-05-20 2010-05-17 Dispositif et procédé permettant d'identifier l'auteur d'une oeuvre d'art

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Country Link
US (1) US8930302B2 (fr)
EP (1) EP2433245A1 (fr)
JP (1) JP2012527665A (fr)
KR (1) KR20120024799A (fr)
CN (1) CN102439605A (fr)
BR (1) BRPI1010963A2 (fr)
CA (1) CA2761382C (fr)
DE (3) DE102009023756B4 (fr)
RU (1) RU2541917C2 (fr)
WO (1) WO2010133204A1 (fr)

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US8930302B2 (en) 2015-01-06
RU2541917C2 (ru) 2015-02-20
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KR20120024799A (ko) 2012-03-14
DE102009041757A1 (de) 2010-12-09
DE102009023756A1 (de) 2011-01-05
BRPI1010963A2 (pt) 2019-04-02
US20120072454A1 (en) 2012-03-22
CN102439605A (zh) 2012-05-02
JP2012527665A (ja) 2012-11-08
CA2761382C (fr) 2016-01-12
CA2761382A1 (fr) 2010-11-25
WO2010133204A1 (fr) 2010-11-25
RU2011149157A (ru) 2013-06-27

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