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A framework to select edge detection method using multi-criteria decision making

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Standard

A framework to select edge detection method using multi-criteria decision making. / Naeem, Sidra; Siraj, Sajid.

2013 IEEE international conference on Systems, man, and cybernetics: SMC 2013. Picataway : IEEE, 2013. p. 730-735.

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Harvard

Naeem, S & Siraj, S 2013, A framework to select edge detection method using multi-criteria decision making. in 2013 IEEE international conference on Systems, man, and cybernetics: SMC 2013. IEEE, Picataway, pp. 730-735, 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013), Manchester, United Kingdom, 13/10/13. https://doi.org/10.1109/SMC.2013.129

APA

Naeem, S., & Siraj, S. (2013). A framework to select edge detection method using multi-criteria decision making. In 2013 IEEE international conference on Systems, man, and cybernetics: SMC 2013 (pp. 730-735). IEEE. https://doi.org/10.1109/SMC.2013.129

Vancouver

Naeem S, Siraj S. A framework to select edge detection method using multi-criteria decision making. In 2013 IEEE international conference on Systems, man, and cybernetics: SMC 2013. Picataway: IEEE. 2013. p. 730-735 https://doi.org/10.1109/SMC.2013.129

Author

Naeem, Sidra ; Siraj, Sajid. / A framework to select edge detection method using multi-criteria decision making. 2013 IEEE international conference on Systems, man, and cybernetics: SMC 2013. Picataway : IEEE, 2013. pp. 730-735

Bibtex

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title = "A framework to select edge detection method using multi-criteria decision making",
abstract = "In the fields of computer vision and image processing, edge detection refers to the identification and localization of significant changes in a digital image. This article presents a survey of widely-used edge detection techniques including linear approaches, morpohlogical operations, multi-resolution analysis, and machine learning methods. Since there exists no single method that is applicable in all situations, different methods are deployed for different applications. A hierarchical framework based on multiple criteria has been proposed here that can facilitate the process of selecting the most appropriate edge detection method in a given scenario. Use of the proposed framework has been explained through an example of medical images. Finally, possible areas for further exploration have been highlighted. collapse",
keywords = "Edge detection,, boundary,, filters",
author = "Sidra Naeem and Sajid Siraj",
year = "2013",
doi = "10.1109/SMC.2013.129",
language = "English",
isbn = "978076955548",
pages = "730--735",
booktitle = "2013 IEEE international conference on Systems, man, and cybernetics",
publisher = "IEEE",
note = "2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013) ; Conference date: 13-10-2013 Through 16-10-2013",

}

RIS

TY - CHAP

T1 - A framework to select edge detection method using multi-criteria decision making

AU - Naeem, Sidra

AU - Siraj, Sajid

PY - 2013

Y1 - 2013

N2 - In the fields of computer vision and image processing, edge detection refers to the identification and localization of significant changes in a digital image. This article presents a survey of widely-used edge detection techniques including linear approaches, morpohlogical operations, multi-resolution analysis, and machine learning methods. Since there exists no single method that is applicable in all situations, different methods are deployed for different applications. A hierarchical framework based on multiple criteria has been proposed here that can facilitate the process of selecting the most appropriate edge detection method in a given scenario. Use of the proposed framework has been explained through an example of medical images. Finally, possible areas for further exploration have been highlighted. collapse

AB - In the fields of computer vision and image processing, edge detection refers to the identification and localization of significant changes in a digital image. This article presents a survey of widely-used edge detection techniques including linear approaches, morpohlogical operations, multi-resolution analysis, and machine learning methods. Since there exists no single method that is applicable in all situations, different methods are deployed for different applications. A hierarchical framework based on multiple criteria has been proposed here that can facilitate the process of selecting the most appropriate edge detection method in a given scenario. Use of the proposed framework has been explained through an example of medical images. Finally, possible areas for further exploration have been highlighted. collapse

KW - Edge detection,

KW - boundary,

KW - filters

U2 - 10.1109/SMC.2013.129

DO - 10.1109/SMC.2013.129

M3 - Chapter (peer-reviewed)

SN - 978076955548

SP - 730

EP - 735

BT - 2013 IEEE international conference on Systems, man, and cybernetics

PB - IEEE

CY - Picataway

T2 - 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013)

Y2 - 13 October 2013 through 16 October 2013

ER -

ID: 673964