Download
1 / 26
260 likes | 267 Views
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.
E N D
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 • 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
