2012
51.
Azzopardi, George; Petkov, Nicolai
Contour detection by CORF operator Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, contour detection
@inproceedings{azzopardi2012contour,
title = {Contour detection by CORF operator},
author = {George Azzopardi and Nicolai Petkov},
doi = {10.1007/978-3-642-33269-2_50},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
booktitle = {International Conference on Artificial Neural Networks},
pages = {395--402},
organization = {Springer, Berlin, Heidelberg},
abstract = {We propose a contour operator, called CORF, inspired by the properties of simple cells in visual cortex. It combines, by a weighted geometric mean, the blurred responses of difference-of-Gaussian operators that model cells in the lateral geniculate nucleus (LGN). An operator that has gained particular popularity as a computational model of a simple cell is based on a family of Gabor Functions (GFs). However, the GF operator short-cuts the LGN, and its effectiveness in contour detection tasks, which is assumed to be the primary biological role of simple cells, has never been compared with the effectiveness of alternative operators. We compare the performances of the CORF and the GF operators using the RuG and the Berkeley data sets of natural scenes with associated ground truths. The proposed CORF operator outperforms the GF operator (RuG: ?(39)=4.39, ?<10−4 and Berkeley: ?(499)=4.95, ?<10−6).
},
keywords = {brain-inspired, contour detection},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a contour operator, called CORF, inspired by the properties of simple cells in visual cortex. It combines, by a weighted geometric mean, the blurred responses of difference-of-Gaussian operators that model cells in the lateral geniculate nucleus (LGN). An operator that has gained particular popularity as a computational model of a simple cell is based on a family of Gabor Functions (GFs). However, the GF operator short-cuts the LGN, and its effectiveness in contour detection tasks, which is assumed to be the primary biological role of simple cells, has never been compared with the effectiveness of alternative operators. We compare the performances of the CORF and the GF operators using the RuG and the Berkeley data sets of natural scenes with associated ground truths. The proposed CORF operator outperforms the GF operator (RuG: ?(39)=4.39, ?<10−4 and Berkeley: ?(499)=4.95, ?<10−6).
2011
52.
Azzopardi, George; Petkov, Nicolai
Detection of retinal vascular bifurcations by trainable V4-like filters Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, medical image analysis, trainable filters
@inproceedings{azzopardi2011detection,
title = {Detection of retinal vascular bifurcations by trainable V4-like filters},
author = {George Azzopardi and Nicolai Petkov},
doi = {10.1007/978-3-642-23672-3_55},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {International Conference on Computer Analysis of Images and Patterns},
pages = {451--459},
organization = {Springer, Berlin, Heidelberg},
abstract = {The detection of vascular bifurcations in retinal fundus images is important for finding signs of various cardiovascular diseases. We propose a novel method to detect such bifurcations. Our method is implemented in trainable filters that mimic the properties of shape-selective neurons in area V4 of visual cortex. Such a filter is configured by combining given channels of a bank of Gabor filters in an AND-gate-like operation. Their selection is determined by the automatic analysis of a bifurcation feature that is specified by the user from a training image. Consequently, the filter responds to the same and similar bifurcations. With only 25 filters we achieved a correct detection rate of 98.52% at a precision rate of 95.19% on a set of 40 binary fundus images, containing more than 5000 bifurcations. In principle, all vascular bifurcations can be detected if a sufficient number of filters are configured and used.},
keywords = {brain-inspired, medical image analysis, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
The detection of vascular bifurcations in retinal fundus images is important for finding signs of various cardiovascular diseases. We propose a novel method to detect such bifurcations. Our method is implemented in trainable filters that mimic the properties of shape-selective neurons in area V4 of visual cortex. Such a filter is configured by combining given channels of a bank of Gabor filters in an AND-gate-like operation. Their selection is determined by the automatic analysis of a bifurcation feature that is specified by the user from a training image. Consequently, the filter responds to the same and similar bifurcations. With only 25 filters we achieved a correct detection rate of 98.52% at a precision rate of 95.19% on a set of 40 binary fundus images, containing more than 5000 bifurcations. In principle, all vascular bifurcations can be detected if a sufficient number of filters are configured and used.
2009
53.
Azzopardi, George; Smeraldi, Fabrizio
Variance Ranklets: Orientation-selective Rank Features for Contrast Modulations Inproceedings
Abstract | Links | BibTeX | Tags: brain-inspired, image classification
@inproceedings{azzopardi2009variance,
title = {Variance Ranklets: Orientation-selective Rank Features for Contrast Modulations},
author = {George Azzopardi and Fabrizio Smeraldi},
url = {http://www.bmva.org/bmvc/2009/Papers/Paper456/Paper456.html},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {BMVC},
pages = {1--11},
abstract = {We introduce a novel type of orientation\textendashselective rank features that are sensitive to contrast modulations (second\textendashorder stimuli). Variance Ranklets are designed in close analogy with the standard Ranklets, but use the Siegel\textendashTukey statistics for dispersion instead of the Wilcoxon statistics. Their response shows the same orientation selectivity pattern of Haar wavelets on second\textendashorder signals that are not detectable by linear filters. To the best of our knowledge, this is the first family of rank filters designed to detect orientation in variance modulations. We validate our descriptors with an application to texture classification over a subset of the VisTex and Brodatz databases. The combination of standard (intensity) Ranklets with Variance Ranklets greatly improves on the performance of Ranklets alone. Comparison with other published results shows that state\textendashof\textendashthe\textendashart recognition rates can be achieved with a simple Nearest Neighbour classifier.},
keywords = {brain-inspired, image classification},
pubstate = {published},
tppubtype = {inproceedings}
}
We introduce a novel type of orientation–selective rank features that are sensitive to contrast modulations (second–order stimuli). Variance Ranklets are designed in close analogy with the standard Ranklets, but use the Siegel–Tukey statistics for dispersion instead of the Wilcoxon statistics. Their response shows the same orientation selectivity pattern of Haar wavelets on second–order signals that are not detectable by linear filters. To the best of our knowledge, this is the first family of rank filters designed to detect orientation in variance modulations. We validate our descriptors with an application to texture classification over a subset of the VisTex and Brodatz databases. The combination of standard (intensity) Ranklets with Variance Ranklets greatly improves on the performance of Ranklets alone. Comparison with other published results shows that state–of–the–art recognition rates can be achieved with a simple Nearest Neighbour classifier.
2006
54.
Azzopardi, George
Offline handwritten signature verification using Radial Basis Function neural networks Inproceedings
BibTeX | Tags: image classification, pattern recognition
@inproceedings{azzopardi2006offline,
title = {Offline handwritten signature verification using Radial Basis Function neural networks},
author = {George Azzopardi},
year = {2006},
date = {2006-01-01},
urldate = {2006-01-01},
booktitle = {WICT2008},
publisher = {University of Malta},
keywords = {image classification, pattern recognition},
pubstate = {published},
tppubtype = {inproceedings}
}