texture - uvic.caaalbu/computer vision 2010/l26. texture.pdf · what is texture? no formal...
TRANSCRIPT
Texture
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Today
Key Question: How do we represent texture?
Topics Definition of texture Texture segmentation Texture analysis Texture synthesis Shape from texture (only statement of
the problem)
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What is texture?
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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Texture segmentation
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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Texture descriptors
Measures of smoothness, coarseness, and regularity
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Approaches for texture description
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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Statistical approaches
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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Limits of statistical moments of intensity histogram
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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Co-occurrence matrice
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Spectral approaches
Excellent for detecting periodic textures
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Texture synthesis
Alexei Efros and Thomas Leung: Texture synthesis by non-parametric sampling, ICCV 1999 (mandatory reading)
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”