Seamless Object Cloning: Optimizing User Interaction in Image Processing
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This study presents a novel approach to seamless object cloning, focusing on reducing user interaction while maintaining natural aesthetics. The process involves users providing a rough region of interest (RoI), from which an optimization problem is formulated using Euler-Lagrange and Poisson equations. The aim is to automatically suppress unnatural appearances and improve boundary optimization. Results indicate significant advancements in seamless image completion and object cloning techniques, allowing for better integration in various applications, including automatic image completion and enhancing user experiences.
Seamless Object Cloning: Optimizing User Interaction in Image Processing
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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 • 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
