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Hyperconnected Attribute Filters Based on k-Flat Zones
February 2011 (vol. 33 no. 2)
pp. 224-239
ASCII Text
x 
 
Georgios K. Ouzounis, Michael H.F. Wilkinson, "Hyperconnected Attribute Filters Based on k-Flat Zones," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 2, pp. 224-239, February, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2010.74,
author = {Georgios K. Ouzounis and Michael H.F. Wilkinson},
title = {Hyperconnected Attribute Filters Based on k-Flat Zones},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {33},
number = {2},
issn = {0162-8828},
year = {2011},
pages = {224-239},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.74},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Hyperconnected Attribute Filters Based on k-Flat Zones
IS - 2
SN - 0162-8828
SP224
EP239
EPD - 224-239
A1 - Georgios K. Ouzounis,
A1 - Michael H.F. Wilkinson,
PY - 2011
KW - Image enhancement
KW - object detection
KW - hyperconnectivity
KW - connectivity
KW - Max-Tree
KW - document processing
KW - anisotropic diffusion
KW - attribute filter.
VL - 33
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Georgios K. Ouzounis, IPSC—Joint Research Centre—European Commission, Ispra
Michael H.F. Wilkinson, University of Groningen, Groningen
In this paper, we present a new method for attribute filtering, combining contrast and structural information. Using hyperconnectivity based on k-flat zones, we improve the ability of attribute filters to retain internal details in detected objects. Simultaneously, we improve the suppression of small, unwanted detail in the background. We extend the theory of attribute filters to hyperconnectivity and provide a fast algorithm to implement the new method. The new version is only marginally slower than the standard Max-Tree algorithm for connected attribute filters, and linear in the number of pixels or voxels. It is two orders of magnitude faster than anisotropic diffusion. The method is implemented in the form of a filtering rule suitable for handling both increasing (size) and nonincreasing (shape) attributes. We test this new framework on nonincreasing shape filters on both 2D images from astronomy, document processing, and microscopy, and 3D CT scans, and show increased robustness to noise while maintaining the advantages of previous methods.

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Index Terms:
Image enhancement, object detection, hyperconnectivity, connectivity, Max-Tree, document processing, anisotropic diffusion, attribute filter.
Citation:
Georgios K. Ouzounis, Michael H.F. Wilkinson, "Hyperconnected Attribute Filters Based on k-Flat Zones," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 2, pp. 224-239, Feb. 2011, doi:10.1109/TPAMI.2010.74
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