Key Question: How do we represent texture?   Reading: Sonka 15.1 and Alexei Efros and Thomas Leung: Texture synthesis by non-parametric

sampling, ICCV 1999 (mandatory reading for ELEC 536) Topics ◦  Definition of texture ◦  Texture segmentation ◦  Texture analysis ◦  Texture synthesis ◦  Shape from texture (only statement of the problem)

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  No formal definition exists   Sonka, Hlavac and Boyle: ◦  “something consisting of mutually related elements”

  Trucco and Verri: ◦  “A surface texture is created by the regular repetition of an element or pattern,

called surface textel, on a surface”

◦  “An image texture is the image of a surface texture, itself a repetition of image texels, the shape of which is distorted by the projection across the image”

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  Question: Is texture the property of a point or of a region?   We need a region to have a texture!   This is a “chicken and egg” problem. ◦  Texture segmentation can be done can be done by detecting boundaries of a

region characterized by similar texture

◦  Texture boundaries can be detected using standard edge detection techniques (applied to the texture measures determined at each point)

  We typically use a local window to estimate texture properties and assign those texture properties as point properties of the windows’ center row and column

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  Measures of smoothness, coarseness, and regularity

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  Statistical: ◦  Describe texture as smooth, coarse, grainy etc.

◦  Scale dependent!

  Structural: ◦  Deal with the arrangement of image primitives. Example: regularly spaced parallel

lines

◦  Tone and structure of a texture   Tone=based on pixel intensity properties in a primitive   Structure: the spatial relationship between the primitives

  Spectral techniques ◦  Good for analyzing periodic or quasi-periodic textures

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  Use the statistical moments of the intensity histogram of an image or region ◦  Order 1: Mean ◦  Order 2: Variance   Normalized smoothness descriptor ◦  Order 3: Skewness (symmetry of the histogram) ◦  Order 4: Kurtosis (flatness of the histogram) ◦  Additional measures: uniformity, enthropy

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 Carry no information about the relative position of pixels with respect to each other

 Don’t tell us anything about ‘texels’

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 Excellent for detecting periodic textures

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  Alexei Efros and Thomas Leung: Texture synthesis by non-parametric sampling, ICCV 1999 (mandatory reading for ELEC 536)

 http://graphics.cs.cmu.edu/people/efros/research/NPS/efros-iccv99.ppt

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Goal  of  Texture  Synthesis  

•  Given  a  finite  sample  of  some  texture,  the  goal  is  to  synthesize  other  samples  from  that  same  texture.    –  The  sample  needs  to  be  "large  enough"  

True (infinite) texture

SYNTHESIS

generated image

input image

The  Challenge  

•  Texture  analysis:  how  to  capture  the  essence  of  texture?    

•  Need  to  model  the  whole  spectrum:  from  repeated  to  stochasDc  texture  

•  This  problem  is  at  intersecDon  of  vision,  graphics,  staDsDcs,  and  image  compression  

repeated

stochastic

Both?

Shape  from  texture  

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“An image texture is the image of a surface texture, itself a repetition of image texels, the shape of which is distorted by the projection across the image”