A new scoring method for directional dominance in images

Bilge Suheyla Akkoca-Gazioglu; Mustafa Kamasak

doi:10.1007/978-3-319-64698-5_1

A new scoring method for directional dominance in images

Bilge Suheyla Akkoca-Gazioglu^*, Mustafa Kamasak

^*Corresponding author for this work

Department of Computer Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We aim to develop a scoring method for expressing directional dominance in the images. It is predicted that this score will give an information of how much improvement in system performance can be achieved when using a directional total variation (DTV)-based regularization instead of total variation (TV). For this purpose, a dataset consists of 85 images taken from the noise reduction datasets is used. The DTV values are calculated by using different sensitivities in the direction of the directional dominance of these images. The slope of these values is determined as the directional dominance score of the image. To verify this score, the noise reduction performances are examined by using direction invariant TV and DTV regulators of images. As a result, we observe that the directional dominance score and the improvement rate in noise reduction performance are correlated. Therefore, the resulting score can be used to estimate the performance of DTV method.

Original language	English
Title of host publication	Computer Analysis of Images and Patterns - 17th International Conference, CAIP 2017, Proceedings
Editors	Anders Heyden, Michael Felsberg, Norbert Kruger
Publisher	Springer Verlag
Pages	3-13
Number of pages	11
ISBN (Print)	9783319646978
DOIs	https://doi.org/10.1007/978-3-319-64698-5_1
Publication status	Published - 2017
Event	17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017 - Ystad, Sweden Duration: 22 Aug 2017 → 24 Aug 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10425 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017
Country/Territory	Sweden
City	Ystad
Period	22/08/17 → 24/08/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Keywords

Directed total variation
Directional dominance score
Total variation

Access to Document

10.1007/978-3-319-64698-5_1

Cite this

Akkoca-Gazioglu, B. S., & Kamasak, M. (2017). A new scoring method for directional dominance in images. In A. Heyden, M. Felsberg, & N. Kruger (Eds.), Computer Analysis of Images and Patterns - 17th International Conference, CAIP 2017, Proceedings (pp. 3-13). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10425 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-64698-5_1

Akkoca-Gazioglu, Bilge Suheyla ; Kamasak, Mustafa. / A new scoring method for directional dominance in images. Computer Analysis of Images and Patterns - 17th International Conference, CAIP 2017, Proceedings. editor / Anders Heyden ; Michael Felsberg ; Norbert Kruger. Springer Verlag, 2017. pp. 3-13 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We aim to develop a scoring method for expressing directional dominance in the images. It is predicted that this score will give an information of how much improvement in system performance can be achieved when using a directional total variation (DTV)-based regularization instead of total variation (TV). For this purpose, a dataset consists of 85 images taken from the noise reduction datasets is used. The DTV values are calculated by using different sensitivities in the direction of the directional dominance of these images. The slope of these values is determined as the directional dominance score of the image. To verify this score, the noise reduction performances are examined by using direction invariant TV and DTV regulators of images. As a result, we observe that the directional dominance score and the improvement rate in noise reduction performance are correlated. Therefore, the resulting score can be used to estimate the performance of DTV method.",

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Akkoca-Gazioglu, BS & Kamasak, M 2017, A new scoring method for directional dominance in images. in A Heyden, M Felsberg & N Kruger (eds), Computer Analysis of Images and Patterns - 17th International Conference, CAIP 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10425 LNCS, Springer Verlag, pp. 3-13, 17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017, Ystad, Sweden, 22/08/17. https://doi.org/10.1007/978-3-319-64698-5_1

A new scoring method for directional dominance in images. / Akkoca-Gazioglu, Bilge Suheyla; Kamasak, Mustafa.
Computer Analysis of Images and Patterns - 17th International Conference, CAIP 2017, Proceedings. ed. / Anders Heyden; Michael Felsberg; Norbert Kruger. Springer Verlag, 2017. p. 3-13 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10425 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A new scoring method for directional dominance in images

AU - Akkoca-Gazioglu, Bilge Suheyla

AU - Kamasak, Mustafa

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

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N2 - We aim to develop a scoring method for expressing directional dominance in the images. It is predicted that this score will give an information of how much improvement in system performance can be achieved when using a directional total variation (DTV)-based regularization instead of total variation (TV). For this purpose, a dataset consists of 85 images taken from the noise reduction datasets is used. The DTV values are calculated by using different sensitivities in the direction of the directional dominance of these images. The slope of these values is determined as the directional dominance score of the image. To verify this score, the noise reduction performances are examined by using direction invariant TV and DTV regulators of images. As a result, we observe that the directional dominance score and the improvement rate in noise reduction performance are correlated. Therefore, the resulting score can be used to estimate the performance of DTV method.

AB - We aim to develop a scoring method for expressing directional dominance in the images. It is predicted that this score will give an information of how much improvement in system performance can be achieved when using a directional total variation (DTV)-based regularization instead of total variation (TV). For this purpose, a dataset consists of 85 images taken from the noise reduction datasets is used. The DTV values are calculated by using different sensitivities in the direction of the directional dominance of these images. The slope of these values is determined as the directional dominance score of the image. To verify this score, the noise reduction performances are examined by using direction invariant TV and DTV regulators of images. As a result, we observe that the directional dominance score and the improvement rate in noise reduction performance are correlated. Therefore, the resulting score can be used to estimate the performance of DTV method.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Akkoca-Gazioglu BS, Kamasak M. A new scoring method for directional dominance in images. In Heyden A, Felsberg M, Kruger N, editors, Computer Analysis of Images and Patterns - 17th International Conference, CAIP 2017, Proceedings. Springer Verlag. 2017. p. 3-13. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-64698-5_1

A new scoring method for directional dominance in images

Abstract

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Bibliographical note

Keywords

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Cite this