Fuzzy knowledge based enhanced matting

Charles Z. Liu; Manolya Kavakli

doi:10.1109/ICIEA.2016.7603716

Fuzzy knowledge based enhanced matting

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

The goal of this paper is to address how to use human experience to develop an enhanced matting strategy. Based on a recursive α optimization framework, we present an adaptive fuzzy learning strategy for enhancement of matting. Taking into account the uncertainty of data, the proposed scheme successfully applies the expert human knowledge into matting. Experimental results are given to demonstrate the effect of the proposed method compared to some classical methods. The results indicate that the proposed adaptive learning algorithm handles uncertain pixels and perform stable matting.

Original language	English
Title of host publication	Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016
Publisher	IEEE
Pages	934-939
Number of pages	6
ISBN (Electronic)	9781509026050
DOIs	https://doi.org/10.1109/ICIEA.2016.7603716
Publication status	Published - 19 Oct 2016
Event	11th IEEE Conference on Industrial Electronics and Applications, ICIEA 2016 - Hefei, China Duration: 5 Jun 2016 → 7 Jun 2016

Publication series

Name	Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016

Conference

Conference	11th IEEE Conference on Industrial Electronics and Applications, ICIEA 2016
Country/Territory	China
City	Hefei
Period	5/06/16 → 7/06/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Access to Document

10.1109/ICIEA.2016.7603716

Cite this

@inproceedings{9c0cc46b64624af6954d27e11fe06b09,

title = "Fuzzy knowledge based enhanced matting",

abstract = "The goal of this paper is to address how to use human experience to develop an enhanced matting strategy. Based on a recursive α optimization framework, we present an adaptive fuzzy learning strategy for enhancement of matting. Taking into account the uncertainty of data, the proposed scheme successfully applies the expert human knowledge into matting. Experimental results are given to demonstrate the effect of the proposed method compared to some classical methods. The results indicate that the proposed adaptive learning algorithm handles uncertain pixels and perform stable matting.",

author = "Liu, {Charles Z.} and Manolya Kavakli",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 11th IEEE Conference on Industrial Electronics and Applications, ICIEA 2016 ; Conference date: 05-06-2016 Through 07-06-2016",

year = "2016",

month = oct,

day = "19",

doi = "10.1109/ICIEA.2016.7603716",

language = "English",

series = "Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016",

publisher = "IEEE",

pages = "934--939",

booktitle = "Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016",

address = "United States",

}

Liu, CZ & Kavakli, M 2016, Fuzzy knowledge based enhanced matting. in Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016., 7603716, Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016, IEEE, pp. 934-939, 11th IEEE Conference on Industrial Electronics and Applications, ICIEA 2016, Hefei, China, 5/06/16. https://doi.org/10.1109/ICIEA.2016.7603716

Fuzzy knowledge based enhanced matting. / Liu, Charles Z.; Kavakli, Manolya.
Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016. IEEE, 2016. p. 934-939 7603716 (Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Fuzzy knowledge based enhanced matting

AU - Liu, Charles Z.

AU - Kavakli, Manolya

PY - 2016/10/19

Y1 - 2016/10/19

N2 - The goal of this paper is to address how to use human experience to develop an enhanced matting strategy. Based on a recursive α optimization framework, we present an adaptive fuzzy learning strategy for enhancement of matting. Taking into account the uncertainty of data, the proposed scheme successfully applies the expert human knowledge into matting. Experimental results are given to demonstrate the effect of the proposed method compared to some classical methods. The results indicate that the proposed adaptive learning algorithm handles uncertain pixels and perform stable matting.

AB - The goal of this paper is to address how to use human experience to develop an enhanced matting strategy. Based on a recursive α optimization framework, we present an adaptive fuzzy learning strategy for enhancement of matting. Taking into account the uncertainty of data, the proposed scheme successfully applies the expert human knowledge into matting. Experimental results are given to demonstrate the effect of the proposed method compared to some classical methods. The results indicate that the proposed adaptive learning algorithm handles uncertain pixels and perform stable matting.

UR - http://www.scopus.com/inward/record.url?scp=84997418699&partnerID=8YFLogxK

U2 - 10.1109/ICIEA.2016.7603716

DO - 10.1109/ICIEA.2016.7603716

M3 - Conference publication

AN - SCOPUS:84997418699

T3 - Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016

SP - 934

EP - 939

BT - Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016

PB - IEEE

T2 - 11th IEEE Conference on Industrial Electronics and Applications, ICIEA 2016

Y2 - 5 June 2016 through 7 June 2016

ER -

Fuzzy knowledge based enhanced matting

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this