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

^*Bu çalışma için yazışmadan sorumlu yazar

Bilgisayar Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Computer Analysis of Images and Patterns - 17th International Conference, CAIP 2017, Proceedings
Editörler	Anders Heyden, Michael Felsberg, Norbert Kruger
Yayınlayan	Springer Verlag
Sayfalar	3-13
Sayfa sayısı	11
ISBN (Basılı)	9783319646978
DOI'lar	https://doi.org/10.1007/978-3-319-64698-5_1
Yayın durumu	Yayınlandı - 2017
Etkinlik	17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017 - Ystad, Sweden Süre: 22 Ağu 2017 → 24 Ağu 2017

Yayın serisi

Adı	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Hacim	10425 LNCS
ISSN (Basılı)	0302-9743
ISSN (Elektronik)	1611-3349

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017
Ülke/Bölge	Sweden
Şehir	Ystad
Periyot	22/08/17 → 24/08/17

Bibliyografik not

Publisher Copyright:
© Springer International Publishing AG 2017.

Finansman

Acknowledgment. This study is partially supported by The Scientific and Technological Research Council of Turkey with the grant number 115R285.

Finansörler	Finansör numarası
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu	115R285

Belgeye Erişim

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

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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); Hac. 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. editör / 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)).

@inproceedings{e525de6b14cf48d88833100a254b9cbf,

title = "A new scoring method for directional dominance in images",

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.",

keywords = "Directed total variation, Directional dominance score, Total variation",

author = "Akkoca-Gazioglu, {Bilge Suheyla} and Mustafa Kamasak",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017 ; Conference date: 22-08-2017 Through 24-08-2017",

year = "2017",

doi = "10.1007/978-3-319-64698-5_1",

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isbn = "9783319646978",

series = "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 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), hac. 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); Hac. 10425 LNCS).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

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.

PY - 2017

Y1 - 2017

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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KW - Directional dominance score

KW - Total variation

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SN - 9783319646978

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

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BT - Computer Analysis of Images and Patterns - 17th International Conference, CAIP 2017, Proceedings

A2 - Heyden, Anders

A2 - Felsberg, Michael

A2 - Kruger, Norbert

PB - Springer Verlag

T2 - 17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017

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ER -

Akkoca-Gazioglu BS, Kamasak M. A new scoring method for directional dominance in images. In Heyden A, Felsberg M, Kruger N, editörler, 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

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap