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Journal ArticleDOI

Easy Matting ‐ A Stroke Based Approach for Continuous Image Matting

TLDR
This work proposes an iterative energy minimization framework for interactive image matting and demonstrates that the energy‐driven scheme can be extended to video matting, with which the spatio‐temporal smoothness is faithfully preserved.
Abstract
We propose an iterative energy minimization framework for interactive image matting. Our approach is easy in the sense that it is fast and requires only few user-specified strokes for marking the foreground and background. Beginning with the known region, we model the unknown region as a Markov Random Field (MRF) and formulate its energy in each iteration as the combination of one data term and one smoothness term. By automatically adjusting the weights of both terms during the iterations, the first-order continuous and feature-preserving result is rapidly obtained with several iterations. The energy optimization can be further performed in selected local regions for refined results. We demonstrate that our energy-driven scheme can be extended to video matting, with which the spatio-temporal smoothness is faithfully preserved. We show that the proposed approach outperforms previous methods in terms of both the quality and performance for quite challenging examples.

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Citations
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Proceedings ArticleDOI

A Closed Form Solution to Natural Image Matting

TL;DR: A closed-form solution to natural image matting that allows us to find the globally optimal alpha matte by solving a sparse linear system of equations and predicts the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms.
Journal ArticleDOI

KNN Matting

TL;DR: The matting technique, aptly called KNN matting, capitalizes on the nonlocal principle by using K nearest neighbors (KNN) in matching nonlocal neighborhoods, and contributes a simple and fast algorithm giving competitive results with sparse user markups.
Journal ArticleDOI

Image and video matting: a survey

TL;DR: This survey provides a comprehensive review of existing image and video matting algorithms and systems, with an emphasis on the advanced techniques that have been recently proposed.
Proceedings ArticleDOI

A perceptually motivated online benchmark for image matting

TL;DR: This paper evaluated several matting methods with their benchmark and show that their performance varies depending on the error function, and reveals problems of existing algorithms, not reflected in previously reported results.
Proceedings Article

Spectral Matting

TL;DR: In this article, a set of fundamental fuzzy matting components from the smallest eigenvectors of a suitably defined Laplacian matrix is automatically computed, which can then be used as building blocks to easily construct semantically meaningful foreground mattes.
References
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Journal ArticleDOI

Fast approximate energy minimization via graph cuts

TL;DR: This work presents two algorithms based on graph cuts that efficiently find a local minimum with respect to two types of large moves, namely expansion moves and swap moves that allow important cases of discontinuity preserving energies.
Journal ArticleDOI

"GrabCut": interactive foreground extraction using iterated graph cuts

TL;DR: A more powerful, iterative version of the optimisation of the graph-cut approach is developed and the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result.
Proceedings ArticleDOI

Fast approximate energy minimization via graph cuts

TL;DR: This paper proposes two algorithms that use graph cuts to compute a local minimum even when very large moves are allowed, and generates a labeling such that there is no expansion move that decreases the energy.
Journal ArticleDOI

Lazy snapping

TL;DR: Usability studies indicate that Lazy Snapping provides a better user experience and produces better segmentation results than the state-of-the-art interactive image cutout tool, Magnetic Lasso in Adobe Photoshop.
Proceedings ArticleDOI

A Bayesian approach to digital matting

TL;DR: This paper proposes a new Bayesian framework for solving the matting problem, i.e. extracting a foreground element from a background image by estimating an opacity for each pixel of the foreground element by using a maximum-likelihood criterion.