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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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Drag and drop pasting

Drag-and-drop Pasting

By Chui Sung Him, Gary

Supervised by Prof. Chi-keung Tang


Outline
Outline

  • Background

  • Objectives

  • Techniques

  • Results & extended application

  • Demo


Background
Background

  • Seamless object cloning

  • Traditional method

    • User interaction

    • Time

    • Expertise


Objectives
Objectives

  • Reduce user-interaction

  • Suppress unnatural look automatically

  • Optimize boundary to achieve the above objectives


Techniques

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



Objectives1
Objectives

  • Reduce user-interaction

  • Suppress unnatural look automatically

  • Optimize boundary


Techniques cont d
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


Techniques cont d1

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


Shortest path problem

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
Shortest closed-path

  • Break the band with a cut

    • Not closed now


Shortest closed path1
Shortest closed-path

  • Perform usual shortest path algorithm on a yellow pixel

    • Dijkstra O(NlogN)


Shortest closed path2
Shortest closed-path

  • Perform on M yellow pixels

    • O(MNlogN)


Selecting the cut
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
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
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







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