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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 & 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

• Background

• Objectives

• Techniques

• Results & extended application

• Demo

• Seamless object cloning

• User interaction

• Time

• Expertise

• Reduce user-interaction

• Suppress unnatural look automatically

• Optimize boundary to achieve the above objectives

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

• Reduce user-interaction

• Suppress unnatural look automatically

• Optimize boundary

• 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

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

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

• Break the band with a cut

• Not closed now

• Perform usual shortest path algorithm on a yellow pixel

• Dijkstra O(NlogN)

• Perform on M yellow pixels

• O(MNlogN)

• With minimum length M

• Reduce probability of twisting path

• Not to pass the cut more than once

• Reduce running time (MNlogN)

• Seamless image completion

• A hole in an image S

• Another image D provided by user

• Semantically correct

• Auto complete the hole

• 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