The paper describes a new image processing for a non-photorealistic
rendering. The algorithm is based on a random generation of gray tones and
competing statistical requirements. The gray tone value of each pixel in the
starting image is replaced selecting among randomly generated tone values,
according to the statistics of nearest-neighbor and next-nearest-neighbor
pixels. Two competing conditions for replacing the tone values - one position
on the local mean value the other on the local variance - produce a peculiar
pattern on the image. This pattern has a labyrinthine tiling aspect. For
certain subjects, the pattern enhances the look of the image.Comment: 9 pages, 5 figure

Montrucchio, B.

Sparavigna, A.

English

arXiv

The paper describes a new image processing for a non-photorealistic rendering. The algorithm is based on a random generation of gray tones and competing statistical requirements. The gray tone value of each pixel in the starting image is replaced selecting among randomly generated tone values, according to the statistics of nearest-neighbor and next-nearest-neighbor
pixels. Two competing conditions for replacing the tone values - one position on the local mean value the other on the local variance - produce a peculiar pattern on the image. This pattern has a labyrinthine tiling aspect. For certain subjects, the pattern enhances the look of the image

Sparavigna A. C.

Montrucchio B.

PORTO@iris (Publications Open Repository TOrino - Politecnico di Torino)

04 August 2020POLITECNICO DI TORINORepository ISTITUZIONALENon-photorealistic image rendering with a labyrinthine tiling / Sparavigna, A. C.; Montrucchio, B.. - ELETTRONICO. -(2006).OriginalNon-photorealistic image rendering with a labyrinthine tilingPublisher:PublishedDOI:Terms of use:openAccessPublisher copyright(Article begins on next page)This article is made available under terms and conditions as specified in the  corresponding bibliographic description inthe repositoryAvailability:This version is available at: 11583/2714808 since: 2018-10-09T12:10:05ZarXivNon-photorealistic image rendering with a labyrinthine tilingA. Sparavigna1 nd B. Montrucchio21Physics Department, Politecnico di Torino2Control and Computer Engineering Department, Politecnico di TorinoCorso Duca degli Abruzzi 24Torino, I-10129,  Italy(September 15, 2006)The paper describes a new image processing for a non-photorealistic rendering. The algorithm is based on arandom  generation of  gray tones and competing statistical requirements. The gray tone value of each pixel inthe starting image is replaced selecting among randomly generated tone values, according to  the statistics ofnearest-neighbor and next-nearest-neighbor pixels. Two competing conditions for replacing the  tone values -one position on the local mean value  the other on the local variance - produce a peculiar  pattern on the image.This pattern has a labyrinthine tiling aspect. For certain subjects, the pattern  enhances the look of the image.Keywords: Image processing. Non-photorealistic processing. Image-based rendering.1. INTRODUCTIONPhotorealistic processing of images  is not always the best  vehicle for communicate information, because  thesuccess of  the applied processing depends on the intention behind the communication.  This processing is oftenrequired for scientific and forensic imagery, where photorealistic processing  algorithms can remove noise  orenhance object outlines in the image scene. However, itis not obv ous that a photorealistic processing is alwaysto be preferred. Let us consider for instance the hand-drawing, clearly a non-photographic imagery.  The ideathat these illustrations can be better to explain a scene than photographic plates is quite natural. Hand-drawinghelps to illustrate complex phenomena, omitting details and proposing only fundamental objects, sometimeswith a  symbolic representation.The goal of the non-photorealistic rendering (NPR) (for a recent literature survey1-4) is the dev lopment ofalgorithms for generating  or processing images that embody the following qualities: emphasis of selectedfeatures, suppression of unimportant details,  use of stylization to suggest emotional structures. Some NPRalgorithms are devoted to produce pseudo hand-drawing images5. Other techniques are giving interestingaesthetic results6  working with algorithms for visualizing the vector fields.  The vector field visualization, usedalso for scientific purposes7,8, is mainly based on line integral convolutions (LICs).  LIC and related techniquesuse stylization to suggest the structure of the vector field, as an hand-drawing stylization of the field can do.We show here an image processing based on a statistical approach to obtain a non-photorealistic  rendering,different from vector field  visualization, or from other techniques such as mosaic or tessellation textures. It isbased on the use of  statistical parameters such as mean value and variance and gives on the processed image alabyrinth  tiled  texture, which is   not  governed  by casualty  but  able to  follow  the   outlines  of    theobjects in the image scene. The stylization we propose  is then interesting non only for aesthetic purposed butfor the enhancement of visibility of certain features of  images.2. LABYRINTH-LIKE TILINGThe rendering we propose  is a random pixel tone generation performed with two competing statisticalrequirements. This is giving a peculiar pattern on the image, producing a tiling of the image with a labyrinth-like texture.  As in many physical and chemistry phenomena, where  competing conditions are acting on thesystem,  complex patterns are displayed9-11. It is quite usual to encounter stripes, fingerprints  or labyrinths inthe optical investigations of solid surfaces or of thin liquid crystal films.  Competing conditions give two ormore possible local orientations or configurations of the material, revealed by  complex patterns in microscopyobservations.Before discussing  the algorithm, let us show the result of the image processing, to illustrate the pattern formedby the algorithm, on a photographic image. In the Figure 1, the source image and its rendering are compared.The texture has a labyrinth structure, which is not arbitrary but is following the contours of the objects, in thiscase of eyes, eyebrows, chin, etc.  In Figure 2 another example of rendering on the portrait of BenjaminFranklin.The algorithm is based on a statistics manipulations of the image. The statistical approach means that decisionsand choices of the algorithm are based on statistical parameters such as mean values, variances and highermoments12-14. Before discussing the algorithm, let us observe  that an image processing algorithm based simplyon the global mean value and variance of the image, that is on the mean value and variance evaluated on all thepixels forming the image, can be misleading. Each part of the scene possesses a local mean value and variance.These local values can be rather different.For each pixel in the image then, we use a local mean value and variance defined in the following way:( ) ( )( ) ( ) ( )[ ]åå-==..........,,,,,nnnnn2nnnnnyxMyxgN1 yx V; yxgN1yx M     (1)where (x,y) are the co-ordinates of the pixel under consideration in the image frame. Function g(x,y) is the graytone of the pixel and then  a function of the pixel position. Additions are done on the nearest neighbor (n.n.)and next nearest neighbor (n.n.n.) pixels of the place  (x,y). N  is the number of  nearest neighbor and nextnearest neighbor pixels. Starting for the left upper corner of the image we  check all the pixels of the image. Wecan choose to keep the gray tone value g(x,y) of the pixel  (x,y)  under test if :( ) ( )( ) t£-=ÂyxMyxMyxg 1 ,|,,|    (2)where t   is a  fixed threshold value. Eq.2 is comparing the pixel gray tone with the local mean value of theneighbouring pixels. For our image rendering, we choose a value threshold value of  12%.If the ratio  1Â exceeds the fixed threshold, the value of the gray tone is  changed with a new value evaluatedin the following way. The values of the gray tones  range from 0 to 255: let us subdivide this range in three (forexample) approximately equally spaced ranges, with lower and upper values,  iLinf  and iLsup, with indexi ranging from 1 to 3. For neighbor intervals:  1ii LL += infsup . For the first interval, it is better to take as 1L inf  alow  value different from zero. The gray tone g(x,y) of  the pixel is belonging to one of these ranges.  The newgray tone g*(x,y) of the pixel is given according to the range by means of the following expression:( ) ( )iii TTRTyxg infsupinf,* -+=   (3)where  R  is a random number going from 0 to 1. ii TT supinf ,  are gray tones which can be coincident with thevalues ii LL supinf ,  or different  to enhance the rendering of the image. Position in Eq.2 is performed again and ifEq. 2 is true, the new value g*(x,y)  substitutes the old value g(x,y) of the gray tone. If it is not so, anotherrandom number is generated and the procedure continues  till condition (2) is satisfied or if the number ofiterations exceeds a fixed value. Let us note that since the value of gray tone of the pixel is changed, the localenvironment of the pixel is also changed:  the mean value M(x,y) and variance V(x,y) of Eq.(1) acquire newvalues as the processing evolves in  checking all  pixels of the image.To obtain the tiling pattern in Fig.1 and 2, a competing condition must be inserted in the algorithm. In fact, thepixel tone substitution that we have previously described gives a  local smoothing of the image, removingrandom noise. The substitution g(x,y) ® g*(x,y) alone is then not able to create the labyrinthine pattern. Wemust insert a condition competing against this smoothing, a condition then able to strongly enhance the localvariance of pixel tones.  This is accomplished with the following control.The substitution g(x,y) ® g*(x,y) is not applied if  we find the following condition:( )( ) V>=Â yxMNyxV2 ,/,(4)verified at the pixel (x,y). A threshold V of  50%  is used for patterns in Fig.1 and 2. A high value of the ratio 2Â means that  the pixel (x,y) is belonging to a region where the gray tone function has a strong localgradient.The role of the value of the thresholdV   in giving the tiling is illustrated in Figure 3, where the results on thepattern for a slight change in the values of V  are  shown: on the left the threshold is fixed at 47% and on theright at 53%.  In the middle, the threshold value is of 50%. The figure display that a different value of thethreshold parameter gives a different tiling on the image. Trials on several images demonstrate  that there isonly a small range of values for the  threshold V  w ere  the labyrinth pattern is displayed on the processedimage.3. ENHANCING IMAGES.The ratio  2Â in condition (4) depends on the local gradient of the gray tone function g(,y). When the pixel isvery near or is on the  contour of an object in the image, the ratio   2Â incre ses. To verify that position (4)  isacting in enhancing the edges, the algorithm  was applied several times to the image. Fig.4 is a sequence ofimages obtained with this iteration procedure.  The last image of  the sequence (Fig. 4 (d)) looks like an imageresulting from the application of an edge detection filter.The stylization  here proposed is able to give  in certain circumstances,  a better appreciation of a scene. Anexample of the algorithm applied to a landscape (Fig.5), can be useful to stress this possibility. In Fig.5, thedetails of the small branches of the tree are  suppressed but the shape of the main branches is strongly enhancedby the labyrinth  tiling. In this case then, the morphology of the tree is put in evidence by the rendering: itseems that the growth of the branches in the image is following the labyrinthine pattern. The intrinsic geometricstructure (the tree) in the image is favored by the stylization of the rendering algorithm.15  This is aconsequence of  position  (4): as in the sequence of   Fig.4, we have a slight  enhance of  the edges.As we have argued at the beginning, one of the goals in non realistic rendering is to stimulate sensations, with acertain enhancing of patterns in the image scene. If  the  rendering algorithm  turns out to be good  for thispurpose is, to a certain extent,   a rather subjective conclusion. We are now working to evaluate in aquantitative manner  the features of the labyrinthine tiling.REFERENCES[1] F. Durand, An Invitation to Discuss Computer Depiction, Proc. Int. Symp. On Non-photorealisticAnimation and Rendering, NPAR02, ACM Press, pp. 111-124 (2002).[2] M. Tory and T Moller,  IEEE Trans. Visualization and Computer Graphics 10, 72-84 (2004).[3] T. Strothotte and S. Schlechtweg, Non-Photorealistic Computer Graphics: Modeling, Rendering, andAnimation, Morgan Kaufman Publisher, San Francisco  (2002).[4] C. Reynolds, Stylized Depiction in Computer Graphics, an Internet report (2006)http://www.red3d.com/cwr/npr/, 2006.[5] I. Buck, A. Finkelstein, C. Jacobs, A. Klein, D. H. Salesin, J. Seims, R. Szeliski, and K. Toyama,Performance-driven hand-drawn animation, Proc.  Int. Symposium on Non-photorealistic animation andrendering, NPAR00, ACM Press, pp.101-108 (2000).[6] C-M. Wang and J-S. Lee,  J. Information Science and Engineering 20, 923-948 (2004).[7] A.Sparavigna, A.Sanna, B.Montrucchio and A.Strigazzi,  Liq. Cryst. 26, 1467-1478 (1999).[8] B. Montrucchio,  P. Montuschi, A. Sanna, and A. Sparavigna,  Computers & Graphics 25, 847-855 (2001).[9] J.P. Gollub and J.S. Langer,  Rev.  Mod. Phys. 71,  s396-s403 (1999).[10] C. Bowman and A. C. Newell, Rev.  Mod. Phys. 70,  289-301 (1998).[11] A. J. Koch and H. Meinhardt,  Rev.  Mod. Phys.  66, 1481-1507 (1994).[12] B.Montrucchio, A.Sparavigna and A.Strigazzi, Liq. Cryst. 24, 841-852 (1998).[13] B.Montrucchio, A.Sparavigna, S.I.Torgova and A.Strigazzi,  Liq. Cryst. 25, 613-620 (1998).[14] A.Sanna, B.Montrucchio and A.Sparavigna,  Pattern Rec. Lett. 20, 183-190 (1999).[15] The reader can find  images with a high resolutions at the web page staff.polito.it/amelia.sparavigna/rendering.FIGURE CAPTIONSFIG.1: Example of labyrinthine tiling on a photographic image.FIG.2: Labyrinthine tiling on the portrait of Benjamin Franklin.FIG.3: The role of the threshold value on the pattern. From left to right the threshold value is 47%, 50% and53%.FIG.4: An iterative sequence of the algorithm. The last image (d) looks like the image that can be obtained withan edge detection filter.FIG.5: A example of the rendering of a landscape. Details of the small  branches of the tree are  suppressed butthe shape of the main branches is strongly enhanced by the labyrinth tiling (source image: Norman Walsh2006). To appreciate the rendering see the figure with a better resolution at staff.polito.it/amelia.sparavigna/rendering.FIGURE 1  FIGURE 2FIGURE 3FIGURE 4FIGURE 5

Non-photorealistic image rendering with a labyrinthine tiling

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