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WO2001027865A1 - Reconnaissance de dessin - Google Patents

Reconnaissance de dessin Download PDF

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Publication number
WO2001027865A1
WO2001027865A1 PCT/GB2000/003839 GB0003839W WO0127865A1 WO 2001027865 A1 WO2001027865 A1 WO 2001027865A1 GB 0003839 W GB0003839 W GB 0003839W WO 0127865 A1 WO0127865 A1 WO 0127865A1
Authority
WO
WIPO (PCT)
Prior art keywords
image
block
pixels
likelihood
generating
Prior art date
Application number
PCT/GB2000/003839
Other languages
English (en)
Inventor
Mark Pawleski
Original Assignee
British Telecommunications Public Limited Company
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 British Telecommunications Public Limited Company filed Critical British Telecommunications Public Limited Company
Priority to CA002385714A priority Critical patent/CA2385714A1/fr
Priority to EP00964531A priority patent/EP1224610A1/fr
Priority to AU75457/00A priority patent/AU7545700A/en
Publication of WO2001027865A1 publication Critical patent/WO2001027865A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/40—Document-oriented image-based pattern recognition
    • G06V30/41—Analysis of document content
    • G06V30/413—Classification of content, e.g. text, photographs or tables

Definitions

  • This invention relates to a method of and apparatus for determining whether an image, for example a frame of a video signal, represents a cartoon.
  • Image interpretation and classification can be done either by the service provider or by the service receiver. For example, if it is possible to determine whether a signal represents a cartoon or not then it is possible for parents to stop children from downloading pictures from the Internet or from watching TV programs other than cartoons. Other types of classifiers could prove useful, for example, classification of pornographic images or recognition of particular people.
  • a method for classifying whether an image represents a cartoon comprising the step of generating a likelihood in dependence on the presence of low luminosity outlines in the image.
  • a data carrier loadable into a computer and carrying instructions for causing the computer to carry out said method.
  • the method further comprises the step of analysing the image to provide one or more parameters wherein one parameter relates to the luminosity of the image; and the generating step determines the generated likelihood in dependence upon the value of said one parameter.
  • the image comprises a plurality of pixels and the analysing step includes the sub-step of vector quantising the image so that each pixel corresponds to one of a plurality of codes.
  • the analysing step further comprises the sub-step of calculating the percentage of pixels corresponding to a one of the plurality of codes and preferably said one of the plurality of codes is a code which corresponds to pixels of low luminosity.
  • the vector quantising sub-step comprises sub-steps of dividing the image into a plurality of blocks, each block comprising a subset of pixels in the image; and independently vector quantising each block.
  • the generating step comprises the sub-step of generating a block likelihood value for each of a plurality of blocks, the block likelihood representing the probability that the pixels in that block represent an image comprising one or more outlines and preferably the generating step comprises the sub-step of combining a plurality of block likelihood values to provide the likelihood value for the image.
  • apparatus for classifying whether an image represents a cartoon comprising generating means for generating a likelihood in dependence on the presence of low luminosity outlines in the image.
  • a data carrier loadable into a computer and carrying instructions for enabling the computer to provide said apparatus.
  • the apparatus further comprises means for analysing the image to provide one or more parameters wherein one parameter relates to the luminosity of the image; and the generating means receives in operation said one parameter and determines the generated likelihood in dependence upon the value of said one parameter.
  • the analysing means comprises a vector quantiser which receives in operation a plurality of pixels comprising the image and outputs a plurality of codes each output code corresponding to each of the received pixels.
  • the analysing means further comprises means for calculating the percentage of pixels corresponding to a one of the plurality of codes and said one of the plurality of codes is a code which corresponds to pixels of low luminosity.
  • the vector quantiser further comprises means for dividing the image into a plurality of blocks, each block comprising a subset of pixels in the image; and means for independently vector quantising each block.
  • the generating means comprises means for generating a block likelihood value for each of a plurality of blocks, the block likelihood value representing the probability that the pixels in that block represent an image comprising one or more outlines and the generating means further comprises means for combining a plurality of block likelihood values to provide a likelihood value for the image.
  • Figure 1 is a schematic representation of a computer loaded with software embodying the present invention
  • Figure 2 shows red, blue, green and luminance components for a cartoon
  • Figure 3 shows red, blue, green and luminance components for a photograph
  • Figure 4 shows red, blue, green and luminance components for a complex cartoon
  • Figure 5 is a functional block diagram of the program elements that comprise the software indicated in Figure 1 ;
  • Figure 6 is a flow chart showing the method steps performed in one embodiment of the invention by the software illustrated in Figure 5;
  • Figure 7 is a flow chart showing the vector quantising step of the method illustrated in
  • Figure 6 Figure 8 is a flow chart showing the production of a low luminosity signal
  • Figure 9 shows images for each level of vector quantisation for a cartoon and a photograph.
  • Figure 10 is a flow chart showing the determination step of the method illustrated in Figure
  • Figure 1 illustrates a conventional computer 101 , such as a Personal Computer, generally referred to as a PC, running a conventional operating system 103, such as Windows (a)
  • the computer 101 also includes an image classification program 109 that enables a signal representing an image to be classified according to whether the image represents a cartoon.
  • the computer 101 is also connected to a conventional disc storage unit 111 for storing data and programs, a keyboard 113 and mouse 115 for allowing user input and a printer 117 and display unit 119 for providing output from the computer 101.
  • the computer 101 also has access to external networks (not shown) via a network card 121.
  • FIG. 2a shows a cartoon (represented in a grey scale in the figure).
  • Figure 2b is a histogram showing the number of pixels with particular values for the red component
  • Figures 2c, 2d and 2e show similar histograms for the green, blue and luminance components.
  • Figure 3 shows a similar set of histograms for an image which is not a cartoon. The distribution for each component shows no such spikes.
  • an input signal representing an image for example a frame of video data, comprising a plurality of pixels is received.
  • the received signal is converted into a luminosity signal, which represents a grey scale version of the image, by calculating a luminosity value (L) for each pixel.
  • the received signal has components representing a value in the range 0 to 255 for a red component (R) a blue component (B) and a green component (G) for each of the plurality of pixels which comprise the frame of video data.
  • the luminosity value is calculated at step 20 using the equation
  • each block signal represents an area of the same size as the area represented by each other block signal (although the size may differ slightly due to quantisation effects) However, the areas represented by the block signals could equally well be different sizes from each other.
  • each block signal is vector quantised into a predetermined number of levels.
  • a code (for example an integer in the range 1 to the predetermined number of levels) being used to represent each level.
  • the vector quantised signals are used to provide a low luminosity signal comprising the vector quantised signals which represent the darkest level for each block.
  • the lowest luminosity signal is used to determine whether the received signal represents a cartoon. It is not necessary to split the luminosity signal into a plurality of block signals prior to vector quantisation. However, the determination at step 60 is more accurate if the received signal is split into signals representing smaller blocks of the frame.
  • each pixel value is assigned to a code. Initially there is a single code used to represent each pixel value.
  • the mean and the standard deviation of the pixel values which the or each code currently represents are calculated. The mean for the or each code is then associated with that code.
  • the code which represents pixel values having the greatest standard deviation is determined.
  • a new value to be associated with that code is then calculated at step 45 as the mean for that code minus half the standard deviation for that code. If the new value is calculated to be less than zero then the new value is set to zero.
  • a new value to be associated with a new code is calculated as the mean plus half said standard deviation.
  • one of the plurality of codes is assigned to each pixel value.
  • a code is used to represent a pixel value if the value which that code is associated with is 'closer' to the pixel value than any of the other codes.
  • a luminosity value is used, so it is a simple matter to measure the distance between the luminosity value and the value associated with a code, by calculating the difference between the two values.
  • a distance may be calculated using, for example, the 'city-block' distance or the least squares distance.
  • a check is performed to check whether the number of codes corresponding to the predetermined number of levels have been created. If not, the steps 43 to 47 are repeated.
  • the steps 43 to 47 are repeated.
  • four codes are created for each block, although the number of codes (and hence the predetermined number of levels) does not need to be the same for each block.
  • the vector quantising step operates in an analogous manner to that described above.
  • each block is taken in turn.
  • the luminance value for each pixel is set to be equal to the value associated with the code which is used to represent that pixel.
  • a signal is generated with the luminance value for each pixel set to white for each pixel which is not represented by the code associated with the lowest luminosity value for that block.
  • a similar signal is generated (for display purposes) for each one of the codes, in order to generate images for each vector quantisation level.
  • the image generated from the signals for the lowest luminosity value will be referred to as a level 0 image, the image generated from the signals for the next highest luminosity value will be referred to as a level 1 image, etc.
  • Figure 9 shows cartoon image 71 and photographic image 81 , together with level 0 images 72 and 82, level 1 images 73, and 83, level 2 images 74 and 84, and level 3 images 75 and 85.
  • the level 0 image 72 generated from the lowest luminosity signal for each block for the cartoon image 71 differs from the corresponding level 0 image 82 generated from a signal representing the photographic image 81.
  • the level 0 image 72 clearly comprises a plurality of outlines whereas the level 0 image 82 does not. This is because even complex cartoons have outlines delineating the areas of one colour from the areas of another colour, even when the areas of colour are carefully shaded. Photographic images do not have such outlines. Small areas of the level 0 image 82 may be mistakenly judged to contain outlines, however the majority of the image 82 does not contain outlines.
  • the signal representing a frame of video data is separated into block signals representing smaller areas of the frame. These need not be the same size areas as were produced at step 30 of Figure 6. Again, each area represented by a signal need not necessarily be the same size as each other area represented by a signal.
  • the number of dark pixels for each block is determined.
  • the number of blocks which are likely to contain outlines is determined by testing whether the percentage of dark pixels in a block less than a predetermined dark-threshold.
  • a test is performed as to whether the number of blocks which are likely to contain outlines divided by the total number of blocks is greater than a predetermined outline-threshold.
  • the test for whether a block contains outlines is fairly simple. It would be possible to replace steps 62 and 63 by a more sophisticated algorithm which detects, for example, narrow bands of dark pixels, or an algorithm for detecting substantially parallel edges where pixels change from dark to light, or vice versa, or to implement a classifier using a neural network.
  • an image classification program 109 comprises a grey scale converter 130 which performs steps 10 and 20 of Figure 6, an analysing means 140 which performs steps 30, 40 and 50 of Figure 6, and a likelihood generator 150 which performs step 60 of Figure 6.
  • the analysing means 140 comprises a vector quantiser 142 which performs steps 30 and 40 of Figure 6 and a luminosity parameter generator 144 which performs step 50 of Figure 6.
  • the likelihood generator 150 comprises a low luminosity block signal generator 152, a block likelihood generator 154 and a likelihood combiner 156.
  • the vector quantiser 142 comprises a block signal generator 146 and a block signal vector quantiser 148.
  • the luminosity parameter generator 144 comprises a low luminosity signal generator 132 and a percentage of low luminosity signals calculator 134.
  • the image classification program 109 can be contained on various transmission and/or storage mediums such as a floppy disc, CD- ROM, or magnetic tape so that the program can be loaded onto one or more general purpose computers or could be downloaded over a computer network using a suitable transmission medium.

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  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Image Analysis (AREA)

Abstract

Cette invention a trait à une technique d'analyse d'image, par exemple, à partir d'un signal vidéo, aux fins de la recherche des contours, afin de déterminer si cette image censée représenter un dessin satisfait au concept selon lequel les dessins sont souvent caractérisés par la présence de contours sombres autour des objets. Dans une version améliorée, le signal est subdivisé en signaux représentant des secteurs de taille réduite de l'image avant que ne soit effectuée l'analyse de chaque signal aux fins de la recherche de contours sombres.
PCT/GB2000/003839 1999-10-08 2000-10-05 Reconnaissance de dessin WO2001027865A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
CA002385714A CA2385714A1 (fr) 1999-10-08 2000-10-05 Reconnaissance de dessin
EP00964531A EP1224610A1 (fr) 1999-10-08 2000-10-05 Reconnaissance de dessin
AU75457/00A AU7545700A (en) 1999-10-08 2000-10-05 Cartoon recognition

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP99307971.4 1999-10-08
EP99307971 1999-10-08

Publications (1)

Publication Number Publication Date
WO2001027865A1 true WO2001027865A1 (fr) 2001-04-19

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PCT/GB2000/003839 WO2001027865A1 (fr) 1999-10-08 2000-10-05 Reconnaissance de dessin

Country Status (4)

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EP (1) EP1224610A1 (fr)
AU (1) AU7545700A (fr)
CA (1) CA2385714A1 (fr)
WO (1) WO2001027865A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003010715A2 (fr) 2001-07-20 2003-02-06 Koninklijke Philips Electronics N.V. Procede et systeme pour le reperage de dessin anime dans un flux de donnees video
WO2017166597A1 (fr) * 2016-03-31 2017-10-05 乐视控股（北京）有限公司 Procédé et appareil de reconnaissance de vidéo de dessin animé, et dispositif électronique

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5872864A (en) * 1992-09-25 1999-02-16 Olympus Optical Co., Ltd. Image processing apparatus for performing adaptive data processing in accordance with kind of image

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5872864A (en) * 1992-09-25 1999-02-16 Olympus Optical Co., Ltd. Image processing apparatus for performing adaptive data processing in accordance with kind of image

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003010715A2 (fr) 2001-07-20 2003-02-06 Koninklijke Philips Electronics N.V. Procede et systeme pour le reperage de dessin anime dans un flux de donnees video
WO2003010715A3 (fr) * 2001-07-20 2003-11-27 Koninkl Philips Electronics Nv Procede et systeme pour le reperage de dessin anime dans un flux de donnees video
WO2017166597A1 (fr) * 2016-03-31 2017-10-05 乐视控股（北京）有限公司 Procédé et appareil de reconnaissance de vidéo de dessin animé, et dispositif électronique

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Publication number Publication date
AU7545700A (en) 2001-04-23
CA2385714A1 (fr) 2001-04-19
EP1224610A1 (fr) 2002-07-24

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