naïve compositing
DESCRIPTION
naïve compositing. Poisson compositing. our result. Objectives. Seamless compositing. Robust to inaccurate selection Output Quality - Limit color bleeding Time-Performance - Efficient method. Related Work: Poisson Compositing. Pasting gradients instead of pixels. - PowerPoint PPT PresentationTRANSCRIPT
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naïve compositing
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Poisson compositing
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our result
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Objectives
• Robust to inaccurate selection
• Output Quality
- Limit color bleeding
• Time-Performance
- Efficient method
Seamless compositing
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Poisson Compositing Result
Pros:• Blends seamlessly• Linear computationCons:• Bleeding visible• Foreground to background bleeding
Related Work: Poisson CompositingPasting gradients instead of pixelsPérez 03, Georgiev 06, …
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Pros:• Reduced bleedingCons:• Nonlinear
- Computationally intensive
L1 Norm Result
Related Work: L1 NormIntroduced in shape from shadingReddy 09
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Move the boundaries to avoid bleeding
Related Work: Moving Boundaries
Pros:• Avoids bleedingCons:• We don’t want boundaries to change• Changed composition
Changing boundaries
Jia 06
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• Conceal bleeding in textured areas
• Better gradient field at boundary
• Efficient linear scheme
Contributions
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• Problem Description
• Hiding Residuals with Visual Masking
• Generating a low-curl boundary
• Results and Comparisons
Overview
Algorithm Overview
Selection
Background
Foreground
Algorithm Overview
Selection
Background
Foreground
grad
grad
Algorithm Overview
Selection
Background
Foreground
Targetgradient
fieldpastegrad
grad
Algorithm Overview
Selection
Background
Foreground
Result 14
Targetgradient
fieldpaste
integrate
grad
grad
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We seek image I with gradients close to target v.
target gradient field
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Standard Approach: Poisson compositing
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We seek image I with gradients close to target v.
target gradient field
output gradient field
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Least-squares:
Standard Approach: Poisson compositing
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target gradient field
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Our Approach
value that minimizes curl
output gradient field
Weighted least-square:
We seek image I with gradients close to target v.
weight
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• Problem Statement
• Hiding Residuals with Visual Masking
• Generating a low-curl boundary
• Results and Comparisons
Overview
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Our Strategy
• Use the weights to locate integration residuals where they are less visible
• Exploit perceptual effect: visual masking– human perception affected by texture.
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Visual Masking Demo
Can you spot all the dots?
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Visual Masking Demo
Texture hides low-frequency content
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Visual Masking Demo
Can you spot all the dots?
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Design of the Weights
smooth region:needs high weightto prevent bleeding
textured regions:low weight is okbecause bleedingless visible
weights (white is high)
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Estimating the Amount of Texture
RGB gradient field
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Estimating the Amount of Texture
RGB gradient field Noise controlling function
Small Gaussian convolution
Large Gaussian convolution
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Estimating the Amount of Texture
small Gaussian large Gaussian
gradient field g
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Estimating the Amount of Texture
Texture Map Output (white indicates more texture)
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• v depends only on foreground and background– does not depend on the unknown I– weights are constant in the optimization
• our energy is a classical least-squares optimization
Weight Formula
= 1 –
Weight output
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Hiding Residuals with Visual Masking
Poisson compositing With texture weights
Bleeding only in textured areas
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Hiding Residuals with Visual Masking
Poisson compositing With texture weights
Residuals
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Hiding Residuals with Visual Masking
Poisson compositing With texture weights
Reduced bleeding but not fully
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• Problem Statement
• Hiding Residuals with Visual Masking
• Generating a low-curl boundary
• Results and Comparisons
Overview
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• Target field v integrable iff curl(v)=0• Only boundary has non-zero curl– Consequence of
target field construction(see paper)
• Our strategy: reducing the curl
curl usingstandard strategy
Boundary Problems
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• Minimize
• Unfortunately, the result is not seamless:
output of naïve approach close-up
Naïve Approach
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• Unknowns: target field v along boundary• Weights depend on texture (detail in paper)
zero curl seamless compositingweight
Our Approach: Least Squares Trade-off
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• Problem Statement
• Hiding Residuals with Visual Masking
• Generating a low-curl boundary
• Results and Comparisons
Overview
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Results and Comparisons
Copy-and-paste
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Results and Comparisons
Poisson Reconstruction : Pérez 03, Georgiev 06, …
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Results and Comparisons
Maximum Gradient : Pérez 03
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Results and Comparisons
Diffusion : Agrawal 06
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Results and Comparisons
Lalonde 07
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Results and Comparisons
L1 Norm : Reddy 09
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Results and Comparisons
Our Result
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Results and Comparisons
Poisson
Max
Diffusion
Lalonde
L_1 Norm
Our Result
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Running Time (Seconds)
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Results and Comparisons Poisson
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Results and Comparisons Our Result
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Results and Comparisons Poisson
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Results and Comparisons Our Result
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• Several parameters
- all examples use the same parameters
• Discoloration may happen- happens in all gradient based operators
• Our model of visual masking is simple- good for performance
- more complex model could be used
Discussion
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• Robust compositing using visual masking
• Better gradient field at boundary
• Efficient linear scheme
Conclusion
Poisson Reconstruction Our Result
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Special Thanks
Todor GeorgievKavita BalaGeorge DrettakisAseem AgarwalaBill FreemanTim ChoBiliana KanevaMedhat H. IbrahimRavi Ramamoorthi
This material is based upon work supported by the National Science Foundation under Grant No. 0739255 and No. 0924968.
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• Robust compositing using visual masking
• Better gradient field at boundary
• Efficient linear scheme
Conclusion
Poisson Reconstruction Our Result