Drag-and-drop Pasting

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# Drag-and-drop Pasting - PowerPoint PPT Presentation

Drag-and-drop Pasting. By Chui Sung Him, Gary Supervised by Prof. Chi-keung Tang. Outline. Background Objectives Techniques Results &amp; extended application Demo. Background. Seamless object cloning Traditional method User interaction Time Expertise. Objectives.

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Presentation Transcript

### Drag-and-drop Pasting

By Chui Sung Him, Gary

Supervised by Prof. Chi-keung Tang

Outline
• Background
• Objectives
• Techniques
• Results & extended application
• Demo
Background
• Seamless object cloning
• User interaction
• Time
• Expertise
Objectives
• Reduce user-interaction
• Suppress unnatural look automatically
• Optimize boundary to achieve the above objectives

f*

Ω

Ωobj

Techniques
• User provide rough region of interest (RoI)
• Contiaining object of interest (OoI)
• Drag-and-drop to the target
• Optimization problem
• Euler-Lagrange equation
• Poisson equation
Objectives
• Reduce user-interaction
• Suppress unnatural look automatically
• Optimize boundary
Techniques (Cont’d)
• User provides only rough RoI
• Assume v=∇g and let f’=f – g, reformulate optimization problem
• Poisson equation becomes Laplace equation
• Approach zero when (f*-g) = constant
• find an optimal boundary to satisfy this

f*

Ω

Ωobj

Techniques (Cont’d)
• To find the optimal boundary
• Inside the RoI
• Outside the OoI
• Define an energy function
• Total color variance
• Minimize it
Iterative minimization
• Initialize ∂Ω as boundary of RoI
• Given new ∂Ω, optimize E w.r.t.k
• Given new k, optimize E with new ∂Ω
• Shortest path problem
• Until convergence reached

f*

Ω

Ωobj

Shortest path problem?
• Cost of each pixel = its color variance w.r.t. new k
• Path to find in closed band Ω\Ωobj
• Not a usual shortest path
• A shortest closed-path problem
Shortest closed-path
• Break the band with a cut
• Not closed now
Shortest closed-path
• Perform usual shortest path algorithm on a yellow pixel
• Dijkstra O(NlogN)
Shortest closed-path
• Perform on M yellow pixels
• O(MNlogN)
Selecting the cut
• With minimum length M
• Reduce probability of twisting path
• Not to pass the cut more than once
• Reduce running time (MNlogN)
Extended Application
• Seamless image completion
• A hole in an image S
• Another image D provided by user
• Semantically correct
• Auto complete the hole
Seamless Image Completion
• D and Ssemantically agreed
• Color
• Scene objects
• Selecting region on D to complete the hole
• Sum of Squared Difference (SSD) of color
• Distance to the hole on S