The W-penalty and its application to alpha matting with sparse labels

Stephen Tierney; Junbin Gao; Yi Guo

doi:10.1109/DICTA.2014.7008132

The W-penalty and its application to alpha matting with sparse labels

Stephen Tierney, Junbin Gao, Yi Guo

Western Sydney University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

Abstract

![CDATA[Alpha matting is an ill-posed problem, as such the user must supply dense partial labels for an acceptable solution to be reached. Unfortunately this labelling can be time consuming. In this paper we introduce the w-penalty function, which when incorporated into existing matting techniques allows users to supply extremely sparse input. The formulated objective function encourages driving matte values to 0 and 1. The experiments demonstrate the proposed model outperforms the state-of-the-art KNN matting algorithm. MATLAB code for our proposed method is freely available in the MatteKit package.]]

Original language	English
Title of host publication	Proceedings 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014, 25-27 November 2014, Wollongong, N.S.W.
Publisher	IEEE
Number of pages	7
ISBN (Print)	9781479954094
DOIs	https://doi.org/10.1109/DICTA.2014.7008132
Publication status	Published - 2014
Event	DICTA (Conference) - Duration: 25 Nov 2014 → …

Conference

Conference	DICTA (Conference)
Period	25/11/14 → …

Keywords

algorithms
alpha matting
image processing

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10.1109/DICTA.2014.7008132

Cite this

@inproceedings{43001016e9b54961bbb64c83c5ed9801,

title = "The W-penalty and its application to alpha matting with sparse labels",

abstract = "![CDATA[Alpha matting is an ill-posed problem, as such the user must supply dense partial labels for an acceptable solution to be reached. Unfortunately this labelling can be time consuming. In this paper we introduce the w-penalty function, which when incorporated into existing matting techniques allows users to supply extremely sparse input. The formulated objective function encourages driving matte values to 0 and 1. The experiments demonstrate the proposed model outperforms the state-of-the-art KNN matting algorithm. MATLAB code for our proposed method is freely available in the MatteKit package.]]",

keywords = "algorithms, alpha matting, image processing",

author = "Stephen Tierney and Junbin Gao and Yi Guo",

year = "2014",

doi = "10.1109/DICTA.2014.7008132",

language = "English",

isbn = "9781479954094",

booktitle = "Proceedings 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014, 25-27 November 2014, Wollongong, N.S.W.",

publisher = "IEEE",

note = "DICTA (Conference) ; Conference date: 25-11-2014",

}

TY - GEN

T1 - The W-penalty and its application to alpha matting with sparse labels

AU - Tierney, Stephen

AU - Gao, Junbin

AU - Guo, Yi

PY - 2014

Y1 - 2014

N2 - ![CDATA[Alpha matting is an ill-posed problem, as such the user must supply dense partial labels for an acceptable solution to be reached. Unfortunately this labelling can be time consuming. In this paper we introduce the w-penalty function, which when incorporated into existing matting techniques allows users to supply extremely sparse input. The formulated objective function encourages driving matte values to 0 and 1. The experiments demonstrate the proposed model outperforms the state-of-the-art KNN matting algorithm. MATLAB code for our proposed method is freely available in the MatteKit package.]]

AB - ![CDATA[Alpha matting is an ill-posed problem, as such the user must supply dense partial labels for an acceptable solution to be reached. Unfortunately this labelling can be time consuming. In this paper we introduce the w-penalty function, which when incorporated into existing matting techniques allows users to supply extremely sparse input. The formulated objective function encourages driving matte values to 0 and 1. The experiments demonstrate the proposed model outperforms the state-of-the-art KNN matting algorithm. MATLAB code for our proposed method is freely available in the MatteKit package.]]

KW - algorithms

KW - alpha matting

KW - image processing

UR - http://handle.uws.edu.au:8081/1959.7/uws:35213

UR - https://ssl.informatics.uow.edu.au/dicta2014/

U2 - 10.1109/DICTA.2014.7008132

DO - 10.1109/DICTA.2014.7008132

M3 - Conference Paper

SN - 9781479954094

BT - Proceedings 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014, 25-27 November 2014, Wollongong, N.S.W.

PB - IEEE

T2 - DICTA (Conference)

Y2 - 25 November 2014

ER -

The W-penalty and its application to alpha matting with sparse labels

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Conference

Keywords

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