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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
slide10
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
ad