Connected Attribute Filtering Based on Contour Smoothness

Georgios Ouzounis; Erik R. Urbach; M.H.F. Wilkinson

Connected Attribute Filtering Based on Contour Smoothness

Georgios Ouzounis, Erik R. Urbach, M.H.F. Wilkinson

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A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform.

Originele taal-2	English
Titel	11th International Conference "Pattern Recognition and Image Analysis
Subtitel	New Information Technologies" (PRIA-11-2013)
Redacteuren	Yu.I. Zhuravlev
Uitgeverij	The Russian Academie of Science
Pagina's	286-289
Volume	I
ISBN van geprinte versie	978-5-88940-130-8
Status	Published - 2013
Evenement	PRIA 2013 : 11th International Conference on Pattern Recognition and Image Analysis - Samara, Russian Federation Duur: 23-sep.-2013 → 28-sep.-2013

Conference

Conference	PRIA 2013
Land/Regio	Russian Federation
Stad	Samara
Periode	23/09/2013 → 28/09/2013

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14 Proceedings - Volume 1-IIFinal publisher's version, 19,6 MBLicentie: Taverne

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@inproceedings{0f97ce13d0944267846914f81d4295e0,

title = "Connected Attribute Filtering Based on Contour Smoothness",

abstract = "A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform.",

author = "Georgios Ouzounis and Urbach, {Erik R.} and M.H.F. Wilkinson",

year = "2013",

language = "English",

isbn = "978-5-88940-130-8",

volume = "I",

pages = "286--289",

editor = "Yu.I. Zhuravlev",

booktitle = "11th International Conference {"}Pattern Recognition and Image Analysis",

publisher = "The Russian Academie of Science",

note = "PRIA 2013 : 11th International Conference on Pattern Recognition and Image Analysis ; Conference date: 23-09-2013 Through 28-09-2013",

}

TY - GEN

T1 - Connected Attribute Filtering Based on Contour Smoothness

AU - Ouzounis, Georgios

AU - Urbach, Erik R.

AU - Wilkinson, M.H.F.

PY - 2013

Y1 - 2013

N2 - A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform.

AB - A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform.

M3 - Conference contribution

SN - 978-5-88940-130-8

VL - I

SP - 286

EP - 289

BT - 11th International Conference "Pattern Recognition and Image Analysis

A2 - Zhuravlev, Yu.I.

PB - The Russian Academie of Science

T2 - PRIA 2013

Y2 - 23 September 2013 through 28 September 2013

ER -

Connected Attribute Filtering Based on Contour Smoothness

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