background estimation mehdi ghayoumi, md iftakharul islam, muslem al-saidi department of computer...
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![Page 1: Background Estimation Mehdi Ghayoumi, MD Iftakharul Islam, Muslem Al-Saidi Department of Computer Science Kent State University, Kent, OH 44242](https://reader035.vdocument.in/reader035/viewer/2022062314/56649da85503460f94a946c1/html5/thumbnails/1.jpg)
Background Estimation
Mehdi Ghayoumi, MD Iftakharul Islam, Muslem Al-SaidiDepartment of Computer Science
Kent State University,Kent, OH 44242.
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Objective• Fill in the area of an image based on existing background• User selects an area, which is then filled based on surrounding
pixels• Smooth transitions
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Introduction
• Object Removal
– Remove object(s) from image
– Fill the hole with information extracted from the surrounding area.
Filled region should look “realistic” to the human eyes
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Example
Source Image Target Final Image
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Greedy Approach• A Greedy Patch-based Image Inpainting Framework
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Diffusion-based Approach
The idea is to track perfectly the local geometry of the damaged
image and allowing diffusion only in the isophotes curves
direction.
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Exemplar Based Approach
Idea
1. Sample color values of the surrounding area
2. Generate textures with sampling result to fill the hole
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Criminisi’s Algorithm• Assign each pixel with a priority value• Give linear structures higher priorities
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Criminisi’s Algorithm
P(p) = C(p)D(p)
Confidence term
Data term
p
Iq pqC
pC
)(
)()(
pp nI
pD
)(
1. Compute the filling priority
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Criminisi’s Algorithm
• (a) The confidence term assigns high filling priority to out-pointing appendices (in green) and low priority
to in-pointing ones (in red), thus trying to achieve a smooth and roughly circular target boundary. (b) The
data term gives high priority to pixels on the continuation of image structures (in green) and has the effect
of favoring in-pointing appendices in the direction of incoming structures.
Effects of data and confidence terms
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Criminisi’s Algorithm
2. Search for the best matching patch
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Criminisi’s Algorithm
In this step, the algorithm fills the region corresponding to Ψp∩Ω by
replicating the corresponding region in the best matching patch Ψ ^q to the
target patch Ψp. Besides, the boundary of the target region δΩ has to be
renewed.
3. Copy the best matching patch information and refresh the
boundary of target region
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Criminisi’s Algorithm(cont.)• Structure Propagation by exemplar-based texture synthesis
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Criminisi’s Algorithm(cont.)
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Improved Criminisi’s Algorithm(cont.)
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Expected Results
Input Output
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Future Work
• Implementing Algorithms in JAVA• Make and install its Plugin in Imagej
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Future Work
• More accurate propagation of curve structures• Solve the problems
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References
• A. Criminisi, P. Perez, K. Toyama. Region filling and object removal by exemplar-based Inpainting, IEEE Transactions on Image Processing,2004.
• Christine Guillemot and Olivier Le Meur ,Image Inpainting, Signal Processing Magazin,IEEE,2014.
• Jing Wang and et all, Robust object removal with an exemplar-based image inpainting approach ,Neurocomputing, IEEE,2014.
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Thanks!