Improving IMAGE matting USING COMPREHENSIVE ... - GitHub
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Improving IMAGE matting USING COMPREHENSIVE ... - GitHub
Mar 25, 2014 - ... full and partial pixel coverage (alpha-channel) ... Choose best pair among all possible pairs ... con
Henri Rebecq – 25th March 2014
Plan
What is image matting?
Description of the studied algorithm
Implementation details & Results
What is image matting? Accurate foreground/background estimation in images Determining both full and partial pixel coverage (alpha-channel)
What is image matting?
Observed image = convex combination of foreground and background
7 unknowns 3 equations
Highly underconstrained problem
Main application
+
=
What is image matting?
How to reduce complexity? User guidance
BG ?
FG
Trimap
What is image matting?
How to reduce complexity? Assumptions on image statistics ○ Smooth zones ○ Correlation between nearby pixels…
Color sampling method
for each unknown pixel z sample nearby known Fz and Bz colors estimate matte using compositing equation:
Color sampling method
How to choose correctly (Fz,Bz)? Sample multiple candidate points (sample set) Choose best pair among all possible pairs
Generating sampling set Split FG and BG regions into subregions
Input image
User defined trimap
Generating sampling set Split FG and BG regions into subregions Perform two-level color and spatial clustering (GMM)
Generating sampling set Split FG and BG regions into subregions Perform two-level color and spatial clustering (GMM) Choose centroids as candidate samples
Why is this idea good?
Comprehensive sample set: each color distribution appears in the sample set
True samples are never missed
Computing alpha matte
for each unknown pixel z select corresponding subregion (the closer to the boundary the smaller region) try all (F,B) pairs and choose the one which best
“explains” pixel z
(which maximizes a certain objective function among all possible pairs) Compute alpha using compositing equation
Objective function (Fi,Bi) are not too far from Iz
(Fi,Bi) can explain color Iz well
favors well-separated color distributions
Color-based term Observed pixel = good convex combination of (Fi,Bi)
Spatial term Selected pair should be close to observed pixel
Overlapping term Cohen’s “d value”
Selected candidates should come from well-separated color distributions
inversely proportional to overlap between distributions
Pre/post processing Used to refine the alpha matte Pre-processing: known regions are expanded for pixels close to the boundary (wrt color and spatial distance) Post-processing: laplacian method to smooth alpha matte (regions with high confidence have higher smoothing weights)
Results
Everything implemented in C++ using OpenCV Region partitioning Two level hiarchical clustering
Paper not comprehensive enough: only a shallow description of the algorithm Number of subregions? Size of each subregion? Fixed number of subregions (4), width grows quadratically
Number of color/spatial clusters? (“number of
peaks in the histogram”…)
For each subregion, fixed number of color/spatial clusters
Multi-dimensional overlapping term? L2-norm of per-component Cohen’s d value
Thank you for your attention Demonstration if time permits
