improving image matting using comprehensive sampling sets
DESCRIPTION
Improving Image Matting using Comprehensive Sampling Sets. CVPR2013 Oral. Outline. Introduction Approach Experiments Conclusions. Introduction. Accurate extraction of a foreground object from an image is known as alpha or digital matting. Introduction. Applications. Introduction. - PowerPoint PPT PresentationTRANSCRIPT
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Improving Image Matting using Comprehensive Sampling Sets
CVPR2013 Oral
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Outline
Introduction Approach Experiments Conclusions
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Introduction Accurate extraction of a foreground object from
an image is known as alpha or digital matting.
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Introduction Applications
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Introduction
Compositing Equation
Foreground color of pixel z
Observed color of pixel z
Background color of pixel z
Alpha value of pixel z
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Introduction
Range of α : [ 0, 1] α =1 , foreground. α =0 , background.
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Introduction
ill-posed problem Typically, matting approaches rely on constraints
Assumption on image statistics User constraints like Trimap
Known ForegroundKnown Background
Unknown Region
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Introduction
Current alpha matting approaches can be categorized into
1. alpha propagation based method
2. color sampling based method
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Introduction
Alpha propagation based method Assume that neighboring pixels are correlated under
some image statistics and use their affinities to propagate alpha values of known regions toward unknown ones.
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Introduction
Color sampling based method collect a set of known foreground and background samples to estimate
alpha values of unknown pixels.
The quality of the extracted matte is highly dependent on the selected samples. missing true samples problem
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Introduction
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Approach
Gathering comprehensive sample set Choosing candidate samples Handling overlapping color distributions Selection of best(F, B)pair Pre and Post-processing
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Approach
Gathering comprehensive sample set For each region, a two-level hierarchical
clustering is applied. first level, the samples are clustered with respect to
color second level , respect to spatial index of pixels.
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Approach
Gathering comprehensive sample set
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Approach
Choosing candidate samples Each pixel in the unknown region collects a set
of candidate samples that are in the form of a foreground-background pair
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Approach
Handling overlapping color distributions
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Approach
Selection of best(F, B)pair
K : chromatic distortionS : spatial statistics of the imageC : color statistics
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Approach
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Approach
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Approach
Cohen's d
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Approach
Pre-processing An unknown pixel z is considered as foreground if,
for a pixel q F,∈
Trimap Expanded Trimap
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Approach
Post-processing Eq. (2) is further refined to obtain a smooth matte by
considering correlation between neighboring pixels. Cost function [5] consisting of the data term a and a
confidence value f together with a smoothness term consisting of the matting Laplacian [10]
[10] A. Levin, D. Lischinski, and Y. Weiss. A closed-form solution to natural image matting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(1):228–242, 2007
[5] E. Gastal and M. Oliveira. Shared sampling for real time alpha matting. InProc. Eurographics , 2010, volume 29, pages 575–584, 2010.
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Experiments
www.alphamatting.com
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Experiments
www.alphamatting.com
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Experiments
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Experiments
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Experiments
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Conclusions
A new sampling based image matting method New sampling strategy to build a comprehensive set
of known samples. This set includes highly correlated boundary samples
as well as samples inside the F and B regions to capture all color variations and solve the problem of missing true samples.