key question: how do we represent texture?aalbu/computer_vision_2011/l23.texture_represe… · “a...
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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
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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?
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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”