AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2014
Aprovitola, Andrea; Gallo, Luigi
Edge and Junction Detection Improvement Using the Canny Algorithm with a Fourth Order Accurate Derivative Filter Proceedings Article
In: Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference On, pp. 104–111, IEEE, Marrakech, Morocco, 2014, ISBN: 978-1-4799-7978-3.
Abstract | Links | BibTeX | Tags: Canny algorithm, Derivative of Gaussian, Edge detection
@inproceedings{aprovitolaEdgeJunctionDetection2014,
title = {Edge and Junction Detection Improvement Using the Canny Algorithm with a Fourth Order Accurate Derivative Filter},
author = { Andrea Aprovitola and Luigi Gallo},
doi = {10.1109/SITIS.2014.28},
isbn = {978-1-4799-7978-3},
year = {2014},
date = {2014-11-01},
urldate = {2016-12-06},
booktitle = {Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference On},
pages = {104--111},
publisher = {IEEE},
address = {Marrakech, Morocco},
abstract = {The Canny algorithm has been extensively adopted to perform edge detection in images. The Derivative of Gaussian (DoG) proposed by Canny has been shown to be the optimal edge detector to compute the image gradient due to its robustness to noise. However, the DoG has some important drawbacks in relation to images with thin edges of a few pixels width and junctions. The excessive blurring provided by the DoG affects the detection of the double and triple junctions that sometimes appear broken while the corners appear rounded. Such a loss in detail is due to the second order approximation of the finite difference (FD) operator adopted to discretize the DoG detector. In this work an improvement of the Canny algorithm is proposed for images having thin edges, computing the edge detector as a convolution of a fourth order accurate FD with the smoothed Gaussian image. The modified wave number analysis of the FD formulation is adopted to motivate the improvement in the edge resolution gained by the fourth order FD discretization. Quantitative comparisons performed with a second order FD discretization of the edge detector adopting both synthetic and benchmark images highlight an improvement in edge localization and in junction detection.},
keywords = {Canny algorithm, Derivative of Gaussian, Edge detection},
pubstate = {published},
tppubtype = {inproceedings}
}
The Canny algorithm has been extensively adopted to perform edge detection in images. The Derivative of Gaussian (DoG) proposed by Canny has been shown to be the optimal edge detector to compute the image gradient due to its robustness to noise. However, the DoG has some important drawbacks in relation to images with thin edges of a few pixels width and junctions. The excessive blurring provided by the DoG affects the detection of the double and triple junctions that sometimes appear broken while the corners appear rounded. Such a loss in detail is due to the second order approximation of the finite difference (FD) operator adopted to discretize the DoG detector. In this work an improvement of the Canny algorithm is proposed for images having thin edges, computing the edge detector as a convolution of a fourth order accurate FD with the smoothed Gaussian image. The modified wave number analysis of the FD formulation is adopted to motivate the improvement in the edge resolution gained by the fourth order FD discretization. Quantitative comparisons performed with a second order FD discretization of the edge detector adopting both synthetic and benchmark images highlight an improvement in edge localization and in junction detection.
Aprovitola, Andrea; Gallo, Luigi
Edge and junction detection improvement using the Canny algorithm with a fourth order accurate derivative filter Proceedings Article
In: Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference on, pp. 104–111, IEEE, Marrakech, Morocco, 2014, ISBN: 978-1-4799-7978-3.
Abstract | Links | BibTeX | Tags: Canny algorithm, Derivative of Gaussian, Edge detection
@inproceedings{aprovitola_edge_2014,
title = {Edge and junction detection improvement using the Canny algorithm with a fourth order accurate derivative filter},
author = {Andrea Aprovitola and Luigi Gallo},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7081534},
doi = {10.1109/SITIS.2014.28},
isbn = {978-1-4799-7978-3},
year = {2014},
date = {2014-11-01},
urldate = {2016-12-06},
booktitle = {Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference on},
pages = {104–111},
publisher = {IEEE},
address = {Marrakech, Morocco},
abstract = {The Canny algorithm has been extensively adopted to perform edge detection in images. The Derivative of Gaussian (DoG) proposed by Canny has been shown to be the optimal edge detector to compute the image gradient due to its robustness to noise. However, the DoG has some important drawbacks in relation to images with thin edges of a few pixels width and junctions. The excessive blurring provided by the DoG affects the detection of the double and triple junctions that sometimes appear broken while the corners appear rounded. Such a loss in detail is due to the second order approximation of the finite difference (FD) operator adopted to discretize the DoG detector. In this work an improvement of the Canny algorithm is proposed for images having thin edges, computing the edge detector as a convolution of a fourth order accurate FD with the smoothed Gaussian image. The modified wave number analysis of the FD formulation is adopted to motivate the improvement in the edge resolution gained by the fourth order FD discretization. Quantitative comparisons performed with a second order FD discretization of the edge detector adopting both synthetic and benchmark images highlight an improvement in edge localization and in junction detection.},
keywords = {Canny algorithm, Derivative of Gaussian, Edge detection},
pubstate = {published},
tppubtype = {inproceedings}
}
The Canny algorithm has been extensively adopted to perform edge detection in images. The Derivative of Gaussian (DoG) proposed by Canny has been shown to be the optimal edge detector to compute the image gradient due to its robustness to noise. However, the DoG has some important drawbacks in relation to images with thin edges of a few pixels width and junctions. The excessive blurring provided by the DoG affects the detection of the double and triple junctions that sometimes appear broken while the corners appear rounded. Such a loss in detail is due to the second order approximation of the finite difference (FD) operator adopted to discretize the DoG detector. In this work an improvement of the Canny algorithm is proposed for images having thin edges, computing the edge detector as a convolution of a fourth order accurate FD with the smoothed Gaussian image. The modified wave number analysis of the FD formulation is adopted to motivate the improvement in the edge resolution gained by the fourth order FD discretization. Quantitative comparisons performed with a second order FD discretization of the edge detector adopting both synthetic and benchmark images highlight an improvement in edge localization and in junction detection.
2012
Franchini, Silvia; Gentile, Antonio; Sorbello, Filippo; Vassallo, Giorgio; Vitabile, Salvatore
Clifford Algebra Based Edge Detector for Color Images Proceedings Article
In: pp. 84–91, 2012, ISBN: 978-0-7695-4687-2.
Abstract | Links | BibTeX | Tags: Clifford algebra, Clifford convolution, Clifford Fourier transform, Color image edge detection, Edge detection, Geometric algebra, Image processing, Segmentation
@inproceedings{franchiniCliffordAlgebraBased2012,
title = {Clifford Algebra Based Edge Detector for Color Images},
author = { Silvia Franchini and Antonio Gentile and Filippo Sorbello and Giorgio Vassallo and Salvatore Vitabile},
doi = {10.1109/CISIS.2012.128},
isbn = {978-0-7695-4687-2},
year = {2012},
date = {2012-01-01},
pages = {84--91},
abstract = {Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend the traditional concepts of convolution and Fourier transform to vector fields. Experimental results presented in the paper show that the proposed algorithm achieves detection performance comparable to the classical edge detection methods allowing at the same time for a significant reduction (about 33%) of computational times. textcopyright 2012 Crown Copyright.},
keywords = {Clifford algebra, Clifford convolution, Clifford Fourier transform, Color image edge detection, Edge detection, Geometric algebra, Image processing, Segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend the traditional concepts of convolution and Fourier transform to vector fields. Experimental results presented in the paper show that the proposed algorithm achieves detection performance comparable to the classical edge detection methods allowing at the same time for a significant reduction (about 33%) of computational times. textcopyright 2012 Crown Copyright.
Franchini, Silvia; Gentile, Antonio; Sorbello, Filippo; Vassallo, Giorgio; Vitabile, Salvatore
Clifford Algebra based edge detector for color images Proceedings Article
In: pp. 84–91, 2012, ISBN: 978-0-7695-4687-2.
Abstract | Links | BibTeX | Tags: Clifford algebra, Clifford convolution, Clifford Fourier transform, Color image edge detection, Edge detection, Geometric algebra, Image processing, Segmentation
@inproceedings{franchini_clifford_2012,
title = {Clifford Algebra based edge detector for color images},
author = {Silvia Franchini and Antonio Gentile and Filippo Sorbello and Giorgio Vassallo and Salvatore Vitabile},
doi = {10.1109/CISIS.2012.128},
isbn = {978-0-7695-4687-2},
year = {2012},
date = {2012-01-01},
pages = {84–91},
abstract = {Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend the traditional concepts of convolution and Fourier transform to vector fields. Experimental results presented in the paper show that the proposed algorithm achieves detection performance comparable to the classical edge detection methods allowing at the same time for a significant reduction (about 33%) of computational times. © 2012 Crown Copyright.},
keywords = {Clifford algebra, Clifford convolution, Clifford Fourier transform, Color image edge detection, Edge detection, Geometric algebra, Image processing, Segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend the traditional concepts of convolution and Fourier transform to vector fields. Experimental results presented in the paper show that the proposed algorithm achieves detection performance comparable to the classical edge detection methods allowing at the same time for a significant reduction (about 33%) of computational times. © 2012 Crown Copyright.