Publications

Publications

[Bea2019b] Efficient Hierarchical Clustering for Polsar Image Analysis,
Beaulieu Jean-Marie,
Advanced SAR Workshop 2019, Canadian Space Agency, Montreal (Saint-Hubert), 1-3 Oct. 2019, p. 1.
[PDF]   [URL]   [.. More]   [Bibtex]  
@Conference{Bea2019b,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Efficient Hierarchical Clustering for Polsar Image Analysis},
booktitle = {Advanced SAR Workshop 2019, Canadian Space Agency},
volume = {},
publisher = {},
url = {http://www.asc-csa.gc.ca/eng/events/2019/asar-2019-workshop-on-synthetic-aperture-radar.asp},
isbn = {},
doi = {},
address = {Montreal (Saint-Hubert)},
pages = {1},
year = {2019},
month = {1-3 Oct.},
abstract = {},
mypdf = {6},
keywords = {},
openpdf = {},
openid = {}
}
[Bea2019a] Contrôle du Voisinage pour un Regroupement Hiérarchique Efficace,
Beaulieu Jean-Marie,
Colloque AQT/RHQ 2019: La télédétection et l’eau dans tous leurs états, Campus de l’U. Bishop’s, Sherbrooke, 15-17 mai 2019, p. 1.
[PDF]   [URL]   [.. More]   [Bibtex]  
@Conference{Bea2019a,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Contr{\^o}le du Voisinage pour un Regroupement Hi{\'e}rarchique Efficace},
booktitle = {Colloque AQT/RHQ 2019: La t{\'e}l{\'e}d{\'e}tection et l'eau dans tous leurs {\'e}tats},
volume = {},
publisher = {},
url = {https://aqtrhq2019.sciencesconf.org},
isbn = {},
doi = {},
address = {Campus de l'U. Bishop's, Sherbrooke},
pages = {1},
year = {2019},
month = {15-17 mai},
abstract = {},
mypdf = {6},
keywords = {},
openpdf = {},
openid = {}
}
[Bea2017a] Mean-Shif Polsar Image Denoising with Position Tensor,
Beaulieu Jean-Marie,
Earth Observation Summit 2017, Montreal, 20-22 June 2017, p. 1.
[PDF]   [URL]   [Open]   [.. More]   [Bibtex]  
@Conference{Bea2017a,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Mean-Shif Polsar Image Denoising with Position Tensor},
booktitle = {Earth Observation Summit 2017},
volume = {},
publisher = {},
url = {https://crss-sct.ca/conferences/csrs2017},
isbn = {},
doi = {},
address = {Montreal},
pages = {1},
year = {2017},
month = {20-22 June},
abstract = {},
mypdf = {6},
keywords = {},
openpdf = {https://sommetot2017-eosummit2017.exordo.com/files/papers/60/initial_draft/SOT-2017-Polarimetry-Beaulieu.pdf},
openid = {ExOrdo}
}
[Bea2015a] Filtrage d’Image Polsar par Mean-Shift avec Tenseur,
Beaulieu Jean-Marie,
XVIe Congrès de l’Association Québécoise de Télédétection, INRS, Quebec, 28-30 oct. 2015, p. 1.
[PDF]   [URL]   [.. More]   [Bibtex]  
@Conference{Bea2015a,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Filtrage d'Image Polsar par Mean-Shift avec Tenseur},
booktitle = {XVIe Congr{\`e}s de l'Association Qu{\'e}b{\'e}coise de T{\'e}l{\'e}d{\'e}tection},
volume = {},
publisher = {},
url = {http://laqt.org},
isbn = {},
doi = {},
address = {INRS, Quebec},
pages = {1},
year = {2015},
month = {28-30 oct.},
abstract = {},
mypdf = {6},
keywords = {},
openpdf = {},
openid = {}
}
[Bea2015b] Filtrage d’Image Polsar par Mean-Shift avec Tenseur / Tensor Based Mean-Shift Polsar Image Enhancement,
Beaulieu Jean-Marie,
Advanced SAR Workshop 2015, Canadian Space Agency, Montreal (Saint-Hubert), 20-22 Oct. 2015.
[PDF]   [URL]   [.. More]   [Bibtex]  
@Conference{Bea2015b,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Filtrage d'Image Polsar par Mean-Shift avec Tenseur
/ Tensor Based Mean-Shift Polsar Image Enhancement},
booktitle = {Advanced SAR Workshop 2015, Canadian Space Agency},
volume = {},
publisher = {},
url = {http://www.asc-csa.gc.ca},
isbn = {},
doi = {},
address = {Montreal (Saint-Hubert)},
pages = {},
year = {2015},
month = {20-22 Oct.},
abstract = {},
mypdf = {6},
keywords = {},
openpdf = {},
openid = {}
}
[Bea2014a] Tensor Based Mean-Shift Polsar Image Enhancement,
Beaulieu Jean-Marie,
IEEE International Geoscience and Remote Sensing Symposium, Quebec City, QC, July 13-18, 2014, pp. 4544-4547.
[PDF]   [URL]   [DOI]   [.. More]   [Bibtex]   [Abstract]  
The mean-shift approach uses a local estimation of the pdf and moves every data points toward the modes. The direction is calculated from the mean value of surrounding points weighted by a Gaussian kernel. An advantage of the technique is that both radiometric and spatial information could be used in the weighted mean calculation. For polarimetric SAR images, we use likelihood ratio as radiometric similarity or distance measure. The spatial distance between pixels is also used with a Gaussian weight. Contours are well preserved because pixels on one side are dissimilar to pixels on the other side. To improve contour preservation, we examine how the tensor of pixel position can be integrated into the weight calculation. The tensor is calculated from weighted pixel position inside a window. Good PolSAR image smoothing is obtained.
@Conference{Bea2014a,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Tensor Based Mean-Shift Polsar Image Enhancement},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
volume = {IGARSS 2014},
publisher = {},
url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=\&arnumber=6947503},
isbn = {},
doi = {10.1109/IGARSS.2014.6947503},
address = {Quebec City, QC},
pages = {4544-4547},
year = {2014},
month = {July 13-18,},
abstract = {The mean-shift approach uses a local estimation of the pdf and moves every data points toward the modes. The direction is calculated from the mean value of surrounding points weighted by a Gaussian kernel. An advantage of the technique is that both radiometric and spatial information could be used in the weighted mean calculation. For polarimetric SAR images, we use likelihood ratio as radiometric similarity or distance measure. The spatial distance between pixels is also used with a Gaussian weight. Contours are well preserved because pixels on one side are dissimilar to pixels on the other side. To improve contour preservation, we examine how the tensor of pixel position can be integrated into the weight calculation. The tensor is calculated from weighted pixel position inside a window. Good PolSAR image smoothing is obtained.},
mypdf = {11},
keywords = {},
openpdf = {},
openid = {Beaulieu 2014}
}
[ElM2012] “Segmentation, Regroupement et Classification pour l’Analyse d’Image Polarimétrique Radar,”
El Mabrouk Abdelhai, Msc,
Master Thesis, Département d’Informatique et de Génie Logiciel, Université Laval, 2012.
[URL]   [Bibtex]  
@phdthesis{ElM2012,
author = {El Mabrouk, Abdelhai},
title = {Segmentation, Regroupement et Classification pour l'Analyse d'Image Polarim{\'e}trique Radar},
school = {Universit{\'e} Laval},
dept = {D{\'e}partement d'Informatique et de G{\'e}nie Logiciel},
degree = {Msc},
thesis = {Master},
address = {},
pages = {},
year = {2012},
month = {},
publisher = {Universit{\'e} Laval},
url = {Google Scholar},
isbn = {},
doi = {},
wdown = {},
mypdf = {},
openpdf = {},
openid = {ElMabrouk 2012a}
abstract = {},
keywords = {}}
[Bea2011b] Mean-Shift Clustering and Hierarchical Segmentation for Polsar Image Analysis,
Beaulieu Jean-Marie, Ridha Touzi,
Advanced SAR Workshop 2011, Canadian Space Agency, Montreal (Saint-Hubert), June 2011, p. 6.
[PDF]   [URL]   [.. More]   [Bibtex]   [Abstract]  
Image segmentation and unsupervised classification are difficult problems. We propose to combine both. A clustering process is applied over segment mean values. Only large segments are considered. The clustering is composed of a mean-shift step and a hierarchical clustering step. The hierarchical grouping is based upon a powerful segmentation technique previously developed [1]. The approach is applied on a 9-look polarimetric SAR image. Textured and non- textured image regions are considered. The K and Wishart distributions are used respectively. The unsupervised classification results can be very useful for image analysis and further supervised classification. The obtained region groups constitute an important simplification of the image.
@Conference{Bea2011b,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
editor = {},
title = {Mean-Shift Clustering and Hierarchical Segmentation for Polsar Image Analysis},
booktitle = {Advanced SAR Workshop 2011, Canadian Space Agency},
volume = {},
publisher = {},
url = {http://www.asc-csa.gc.ca},
isbn = {},
doi = {},
address = {Montreal (Saint-Hubert)},
pages = {6},
year = {2011},
month = {June},
abstract = {Image segmentation and unsupervised classification are difficult problems. We propose to combine both. A clustering process is applied over segment mean values. Only large segments are considered. The clustering is composed of a mean-shift step and a hierarchical clustering step. The hierarchical grouping is based upon a powerful segmentation technique previously developed [1]. The approach is applied on a 9-look polarimetric SAR image. Textured and non- textured image regions are considered. The K and Wishart distributions are used respectively. The unsupervised classification results can be very useful for image analysis and further supervised classification. The obtained region groups constitute an important simplification of the image.},
mypdf = {7},
keywords = {},
openpdf = {},
openid = {Beaulieu 2011}
}
[ElM2011] Segmentation hiérarchique par optimisation séquentielle pour la classification H/A/? d’image polarimétrique SAR,
El Mabrouk Abdelhai, Jean-Marie Beaulieu,
32e Symposium canadien sur la télédétection et 14e Congrès de l’AQT, Sherbrooke, 2011, p. 1114.
[Bibtex]  
@Conference{ElM2011,
author = {El Mabrouk, Abdelhai and Beaulieu, Jean-Marie},
editor = {},
title = {Segmentation hi{\'e}rarchique par optimisation s{\'e}quentielle pour la classification H/A/? d'image polarim{\'e}trique {SAR}},
booktitle = {32e Symposium canadien sur la t{\'e}l{\'e}d{\'e}tection et 14e Congr{\`e}s de l'AQT, Sherbrooke},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {1114},
year = {2011},
month = {},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {ElMabrouk 2011}
}
[Bea2011a] Segmentation/Classification des Images Polsar par Regroupement Hierarchique et Mean-Shift,
Beaulieu Jean-Marie, Ridha Touzi,
32e Symposium Canadien sur la Télédétection et 14e Congrès de l’AQT, Sherbrooke, Campus de l’U. Bishop’s, Sherbrooke, 13-16 June 2011, pp. 1-7.
[PDF]   [URL]   [.. More]   [Bibtex]   [Abstract]  
Nous avons développé une approche de segmentation hiérarchique performante pour les images polarimétriques SAR. Cependant, la segmentation et la classification non supervisée demeurent des problèmes difficiles. Dans cet article, nous proposons de combiner les deux. En télédétection, la tâche principale est l’interprétation de l’image. Nous devons développer des outils qui facilitent l’accomplissement de cette tâche complexe. Ceci est l’objectif des techniques automatiques de classification, qu’on nomme techniques de regroupement (clustering). Nous examinerons les relations entre les techniques itératives de regroupement, le regroupement hiérarchique et la segmentation de l’image. Nous regarderons comment nous pouvons passer d’une à l’autre.
@Conference{Bea2011a,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
editor = {},
title = {Segmentation/Classification des Images Polsar par Regroupement Hierarchique et Mean-Shift},
booktitle = {32e Symposium Canadien sur la T{\'e}l{\'e}d{\'e}tection et 14e Congr{\`e}s de l'AQT, Sherbrooke},
volume = {},
publisher = {},
url = {https://crss-sct.ca},
isbn = {},
doi = {},
address = {Campus de l'U. Bishop's, Sherbrooke},
pages = {1-7},
year = {2011},
month = {13-16 June},
abstract = {Nous avons d{\'e}velopp{\'e} une approche de segmentation hi{\'e}rarchique performante pour les images polarim{\'e}triques SAR. Cependant, la segmentation et la classification non supervis{\'e}e demeurent des probl{\`e}mes difficiles. Dans cet article, nous proposons de combiner les deux.
En t{\'e}l{\'e}d{\'e}tection, la t{\^a}che principale est l'interpr{\'e}tation de l'image. Nous devons d{\'e}velopper des outils qui facilitent l'accomplissement de cette t{\^a}che complexe. Ceci est l'objectif des techniques automatiques de classification, qu'on nomme techniques de regroupement (clustering). Nous examinerons les relations entre les techniques it{\'e}ratives de regroupement, le regroupement hi{\'e}rarchique et la segmentation de l'image. Nous regarderons comment nous pouvons passer d'une {\`a} l'autre.},
mypdf = {7},
keywords = {},
openpdf = {},
openid = {Beaulieu 2011}
}
[Bea2010a] Mean-shift and Hierarchical Clustering for Textured Polarimetric SAR Image Segmentation/Classification,
Beaulieu Jean-Marie, Ridha Touzi,
IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, July 25-30, 2010, pp. 2519-2522.
[PDF]   [URL]   [DOI]   [.. More]   [Bibtex]   [Abstract]  
Image segmentation and unsupervised classification are difficult problems. We propose to combine both. A clustering process is applied over segment mean values. Only large segments are considered. The clustering is composed of a mean-shift step and a hierarchical clustering step. The hierarchical grouping is based upon a powerful segmentation technique previously developed. The approach is applied on a 9-look polarimetric SAR image. Textured and non-textured image regions are considered. The K and Wishart distributions are used respectively. The unsupervised classification results can be very useful for image analysis and further supervised classification. The obtained region groups constitute an important simplification of the image.
@Conference{Bea2010a,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
editor = {},
title = {Mean-shift and Hierarchical Clustering for Textured Polarimetric {SAR} Image Segmentation/Classification},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
volume = {IGARSS 2010},
publisher = {},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5653919},
isbn = {978-1-4244-9565-8},
doi = {10.1109/IGARSS.2010.5653919},
address = {Honolulu, HI},
pages = {2519-2522},
year = {2010},
month = {July 25-30,},
abstract = {Image segmentation and unsupervised classification are difficult problems. We propose to combine both. A clustering process is applied over segment mean values. Only large segments are considered. The clustering is composed of a mean-shift step and a hierarchical clustering step. The hierarchical grouping is based upon a powerful segmentation technique previously developed. The approach is applied on a 9-look polarimetric SAR image. Textured and non-textured image regions are considered. The K and Wishart distributions are used respectively. The unsupervised classification results can be very useful for image analysis and further supervised classification. The obtained region groups constitute an important simplification of the image.},
mypdf = {11},
keywords = {9-look polarimetric SAR image; hierarchical clustering; hierarchical grouping; image analysis; image classification; image segmentation; image texture; K distribution; mean-shift step; nontextured image region; pattern clustering; radar imaging; radar polarimetry; segment mean value; statistical distributions; synthetic aperture radar; textured polarimetric SAR image; unsupervised classification; Wishart distribution},
openpdf = {},
openid = {}
}
[Bom2009a] Hierarchical Segmentation of Polarimetric SAR Images using Heterogeneous Clutter Models,
Bombrun Lionel, Jean-Marie Beaulieu, G Vasile, JP Ovarlez, F Pascal, M Gay,
IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009, Cape Town, South Africa, 12-17 July 2009, pp. 5-8.
[URL]   [DOI]   [Open]   [.. More]   [Bibtex]   [Abstract]  
In this paper, heterogeneous clutter models are introduced to describe Polarimetric Synthetic Aperture Radar (PolSAR) data. Based on the Spherically Invariant Random Vectors (SIRV) estimation scheme, the scalar texture parameter and the normalized covariance matrix are extracted. If the texture parameter is modeled by a Fisher PDF, the observed target scattering vector follows a KummerU PDF. Then, this PDF is implemented in a hierarchical segmentation algorithm. Segmentation results are shown on high resolution PolSAR data at L and X band.
@Conference{Bom2009a,
author = {Bombrun, Lionel and Beaulieu, Jean-Marie and Vasile, G and Ovarlez, J P and Pascal, F and Gay, M},
editor = {},
title = {Hierarchical Segmentation of Polarimetric {SAR} Images using Heterogeneous Clutter Models},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009},
volume = {III},
publisher = {IEEE},
url = {https://ieeexplore.ieee.org/abstract/document/5418271},
isbn = {978-1-4244-3394-0},
doi = {10.1109/IGARSS.2009.5418271},
address = {Cape Town, South Africa},
pages = {5-8},
year = {2009},
month = {12-17 July},
abstract = {In this paper, heterogeneous clutter models are introduced to describe Polarimetric Synthetic Aperture Radar (PolSAR) data. Based on the Spherically Invariant Random Vectors (SIRV) estimation scheme, the scalar texture parameter and the normalized covariance matrix are extracted. If the texture parameter is modeled by a Fisher PDF, the observed target scattering vector follows a KummerU PDF. Then, this PDF is implemented in a hierarchical segmentation algorithm. Segmentation results are shown on high resolution PolSAR data at L and X band.},
mypdf = {13},
keywords = {Backscatter; Clutter; Covariance matrix; Data mining; Fisher PDF; geophysical image processing; geophysical techniques; heterogeneous clutter models; hierarchical image segmentation; hierarchical segmentation algorithm; image segmentation; image texture; KummerU PDF; L band high resolution PolSAR data; L-band; Maximum likelihood estimation; normalized covariance matrix; Parameter estimation; polarimetric SAR images; polarimetric synthetic aperture radar data; PolSAR data; radar clutter; radar polarimetry; Radar scattering; remote sensing by radar; scalar texture parameter; Segmentation; Spherically Invariant Random Vectors; spherically invariant random vectors estimation scheme; synthetic aperture radar; target scattering vector; Testing; X band high resolution PolSAR data},
openpdf = {https://hal.archives-ouvertes.fr/hal-00398923/},
openid = {HAL archives-ouvertes}
}
[Bom2008a] “Fisher Distribution for Texture Modeling of Polarimetric SAR Data,”
Bombrun Lionel, Jean-Marie Beaulieu,
IEEE Geoscience and Remote Sensing Letters, vol. 5, iss. 3, p. 512–516, July 2008.
[URL]   [DOI]   [Open]   [.. More]   [Bibtex]   [Abstract]  
The multilook polarimetric synthetic aperture radar (PolSAR) covariance matrix is generally modeled by a complex Wishart distribution. For textured areas, the product model is used, and the texture component is modeled by a Gamma distribution. In many cases, the assumption of Gamma-distributed texture is not appropriate. The Fisher distribution does not have this limitation and can represent a large set of texture distributions. As an example, we examine its advantage for an urban area. From a Fisher-distributed texture component, we derive the distribution of the complex covariance matrix for multilook PolSAR data. The obtained distribution is expressed in terms of the KummerU confluent hypergeometric function of the second kind. Those distributions are related to the Mellin transform and second-kind statistics (Log-statistics). The new KummerU-based distribution should provide in many cases a better representation of textured areas than the classic K distribution. Finally, we show that the new model can discriminate regions with different texture distribution in a segmentation experiment with synthetic textured PolSAR images.
@Article{Bom2008a,
author = {Bombrun, Lionel and Beaulieu, Jean-Marie},
title = {Fisher Distribution for Texture Modeling of Polarimetric {SAR} Data},
journal = {IEEE Geoscience and Remote Sensing Letters},
volume = {5},
number = {3},
pages = {512--516},
year = {2008},
month = {July},
abstract = {The multilook polarimetric synthetic aperture radar (PolSAR) covariance matrix is generally modeled by a complex Wishart distribution. For textured areas, the product model is used, and the texture component is modeled by a Gamma distribution. In many cases, the assumption of Gamma-distributed texture is not appropriate. The Fisher distribution does not have this limitation and can represent a large set of texture distributions. As an example, we examine its advantage for an urban area. From a Fisher-distributed texture component, we derive the distribution of the complex covariance matrix for multilook PolSAR data. The obtained distribution is expressed in terms of the KummerU confluent hypergeometric function of the second kind. Those distributions are related to the Mellin transform and second-kind statistics (Log-statistics). The new KummerU-based distribution should provide in many cases a better representation of textured areas than the classic K distribution. Finally, we show that the new model can discriminate regions with different texture distribution in a segmentation experiment with synthetic textured PolSAR images.},
publisher = {IEEE},
url = {https://ieeexplore.ieee.org/abstract/document/4554026},
isbn = {1545-598X},
doi = {10.1109/LGRS.2008.923262},
mypdf = {13},
address = {},
keywords = {Classification; complex covariance matrix distribution; covariance matrices; Fisher distributed texture component; Fisher distribution; geophysical signal processing; geophysical techniques; image segmentation; image texture; KummerU; log statistics; Mellin transform; multilook PolSAR covariance matrix; polarimetric SAR data texture modeling; polarimetric synthetic aperture radar; polarimetric synthetic aperture radar (PolSAR) images; radar polarimetry; radar signal processing; remote sensing by radar; second kind KummerU confluent hypergeometric function; second kind statistics; segmentation; statistical distributions; synthetic aperture radar; synthetic textured PolSAR image segmentation; texture; texture distributions; urban area},
openpdf = {https://hal.archives-ouvertes.fr/hal-00350055/},
openid = {HAL archives-ouvertes}
}
[Bom2008b] Segmentation of Polarimetric SAR Data based on the Fisher Distribution for Texture Modeling,
Bombrun Lionel, Jean-Marie Beaulieu,
IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008, Boston, MA, USA, 7-11 July 2008, pp. 350-353.
[URL]   [DOI]   [Open]   [.. More]   [Bibtex]   [Abstract]  
The Polarimetric Synthetic Aperture Radar (PolSAR) covariance matrix is generally modeled by a complex Wishart distribution. For textured scenes, the product model is used and the texture component is often modeled by a Gamma distribution. In this paper, authors propose to use the Fisher distribution for texture modeling. From a Fisher distributed texture component, we derive the distribution of the complex covariance matrix and we propose to implement the KummerU distribution in a hierarchical segmentation and a hierarchical clustering algorithm. Segmentation and classification results are shown on synthetic images and on ESAR L-band PolSAR data over the Oberpfaffenhofen test-site.
@Conference{Bom2008b,
author = {Bombrun, Lionel and Beaulieu, Jean-Marie},
editor = {},
title = {Segmentation of Polarimetric {SAR} Data based on the Fisher Distribution for Texture Modeling},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008},
volume = {V},
publisher = {IEEE},
url = {https://ieeexplore.ieee.org/abstract/document/4780100},
isbn = {978-1-4244-2807-6},
doi = {10.1109/IGARSS.2008.4780100},
address = {Boston, MA, USA},
pages = {350-353},
year = {2008},
month = {7-11 July},
abstract = {The Polarimetric Synthetic Aperture Radar (PolSAR) covariance matrix is generally modeled by a complex Wishart distribution. For textured scenes, the product model is used and the texture component is often modeled by a Gamma distribution. In this paper, authors propose to use the Fisher distribution for texture modeling. From a Fisher distributed texture component, we derive the distribution of the complex covariance matrix and we propose to implement the KummerU distribution in a hierarchical segmentation and a hierarchical clustering algorithm. Segmentation and classification results are shown on synthetic images and on ESAR L-band PolSAR data over the Oberpfaffenhofen test-site.},
mypdf = {13},
keywords = {Classification; Clustering algorithms; complex Wishart distribution; covariance matrices; covariance matrix; Electromagnetic scattering; ESAR L-band PolSAR data; Fisher distribution; Gamma distribution; geophysical techniques; geophysics computing; hierarchical clustering algorithm; hierarchical segmentation; image classification; image segmentation; image texture; KummerU; KummerU distribution; L-band; Layout; Oberpfaffenhofen test-site; Polarimetric SAR images; Polarimetric Synthetic Aperture Radar data; Polarization; radar polarimetry; Radar scattering; Receiving antennas; remote sensing by radar; Segmentation; Speckle; synthetic aperture radar; Texture; texture component; texture modeling},
openpdf = {https://hal.archives-ouvertes.fr/hal-00369374/},
openid = {HAL archives-ouvertes}
}
[Bea2008a] Classification of Polarimetric SAR Images using Radiometric and Texture Information,
Beaulieu Jean-Marie, Ridha Touzi,
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2008, Boston, MA, USA, 7-11 July 2008, pp. 29-32.
[PDF]   [URL]   [DOI]   [.. More]   [Bibtex]  
@Conference{Bea2008a,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
editor = {},
title = {Classification of Polarimetric {SAR} Images using Radiometric and Texture Information},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium IGARSS 2008},
volume = {IV},
publisher = {IEEE},
url = {https://ieeexplore.ieee.org/document/4779648},
isbn = {978-1-4244-2807-6},
doi = {10.1109/IGARSS.2008.4779648},
address = {Boston, MA, USA},
pages = {29-32},
year = {2008},
month = {7-11 July},
abstract = {},
mypdf = {11},
keywords = {classification; classification map; clustering; Clustering algorithms; clustering process; Covariance matrix; geophysical techniques; hierarchical clustering; hierarchical segmentation; image classification; image segmentation; Iterative algorithms; K distribution; mean shift clustering; mean-shift; Merging; Partitioning algorithms; pattern clustering; Pixel; polarimetric SAR image; Probability; radar polarimetry; radiometry; Remote sensing; remote sensing by radar; scalar texture component; synthetic aperture radar; texture; texture information; Wishart distribution},
openpdf = {},
openid = {Beaulieu 2008}
}
[Bea2008b] Aller–Retour Segmentation/Classification des Images Polarimétriques SAR,
Beaulieu Jean-Marie, Ridha Touzi,
13e Congrès de l’Association Québécoise de Télédétection, Trois-Rivières, Canada, 30 avril – 2 mai 2008, pp. 1-6.
[PDF]   [URL]   [.. More]   [Bibtex]   [Abstract]  
Nous avons développé une technique efficace de segmentation hiérarchique et l’avons appliqué aux images polarimétriques SAR. La segmentation et la classification non-supervisée d’image sont des problèmes difficiles. On peut simplifier le problème en acceptant un nombre élevé de seg- ments (régions) ou de classes. Il est reconnu que la classification basée sur la valeur des seg- ments est moins affectée par le bruit que la classification basée sur la valeur des pixels. Nous pouvons utilisez une partition avec beaucoup de régions (sur-segmentation) simplifiant ainsi la tâche de la segmentation. Cependant, la classification non-supervisée de segment demeure un problème difficile. Pour simplifier, nous utilisons seulement un sous ensemble des segments et nous produisons une classification avec beaucoup de classes. Chaque segment de la sur- segmentation est alors assigné à une des nombreuses classes. Nous pouvons utiliser cette infor- mation de classe pour poursuivre la segmentation en fusionnant les régions et réduire à une valeur convenable le nombre de régions.
@Conference{Bea2008b,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
editor = {},
title = {Aller--Retour Segmentation/Classification des Images Polarim{\'e}triques {SAR}},
booktitle = {13e Congr{\`e}s de l'Association Qu{\'e}b{\'e}coise de T{\'e}l{\'e}d{\'e}tection},
volume = {},
publisher = {},
url = {http://laqt.org},
isbn = {},
doi = {},
address = {Trois-Rivi{\`e}res, Canada},
pages = {1-6},
year = {2008},
month = {30 avril - 2 mai},
abstract = {Nous avons d{\'e}velopp{\'e} une technique efficace de segmentation hi{\'e}rarchique et l'avons appliqu{\'e} aux images polarim{\'e}triques SAR. La segmentation et la classification non-supervis{\'e}e d'image sont des probl{\`e}mes difficiles. On peut simplifier le probl{\`e}me en acceptant un nombre {\'e}lev{\'e} de seg- ments (r{\'e}gions) ou de classes. Il est reconnu que la classification bas{\'e}e sur la valeur des seg- ments est moins affect{\'e}e par le bruit que la classification bas{\'e}e sur la valeur des pixels. Nous pouvons utilisez une partition avec beaucoup de r{\'e}gions (sur-segmentation) simplifiant ainsi la t{\^a}che de la segmentation. Cependant, la classification non-supervis{\'e}e de segment demeure un probl{\`e}me difficile. Pour simplifier, nous utilisons seulement un sous ensemble des segments et nous produisons une classification avec beaucoup de classes. Chaque segment de la sur- segmentation est alors assign{\'e} {\`a} une des nombreuses classes. Nous pouvons utiliser cette infor- mation de classe pour poursuivre la segmentation en fusionnant les r{\'e}gions et r{\'e}duire {\`a} une valeur convenable le nombre de r{\'e}gions.},
mypdf = {7},
keywords = {},
openpdf = {},
openid = {Beaulieu 2008}
}
[Bea2006a] Pseudo-Convex Contour Criterion for Hierarchical Segmentation of SAR Images,
Beaulieu Jean-Marie,
The 3rd Canadian Conference on Computer and Robot Vision, Laval University, Canada, June 07-09, 2006, pp. 29-29.
[PDF]   [URL]   [DOI]   [.. More]   [Bibtex]   [Abstract]  
The hierarchical segmentation of SAR (Synthetic Aperture Radar) images is greatly complicated by the presence of coherent speckle. We are exploring the utilization of spatial constraints and contour shapes in order to improve the segmentation results. With standard merging criterion, the high noise level of SAR images results in the production of regions that have variable mean and variance values and irregular shapes. If the first segments are not correctly delimited then the following steps will merge segments from different fields. In examining the evolution of the initial segments, we see that the merging should take into account spatial aspects. Particularly, the segment contours should have good shapes. In this paper, we examine how the pseudo-convex envelope of a region can be used to evaluate the region contour. We present a pseudo-convex measure adapted to the geometry of image lattice. We show how the pseudo-convex envelope can be calculated. We present measures comparing contour shapes and using the perimeter, the area and the boundary length of segments. We use a hierarchical segmentation algorithm based upon stepwise optimization. A stepwise merging criterion is derived from the multiplicative speckle noise model. The shape measures are combined with the merging criterion in order to guide correctly the segment merging process. The new criterion produces good segmentation of SAR images. This is illustrated by synthetic and real image results.
@Conference{Bea2006a,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Pseudo-Convex Contour Criterion for Hierarchical Segmentation of {SAR} Images},
booktitle = {The 3rd Canadian Conference on Computer and Robot Vision},
volume = {},
publisher = {IEEE},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1640384},
isbn = {0-7695-2542-3},
doi = {10.1109/CRV.2006.58},
address = {Laval University, Canada},
pages = {29-29},
year = {2006},
month = {June 07-09,},
abstract = {The hierarchical segmentation of SAR (Synthetic Aperture Radar) images is greatly complicated by the presence of coherent speckle. We are exploring the utilization of spatial constraints and contour shapes in order to improve the segmentation results. With standard merging criterion, the high noise level of SAR images results in the production of regions that have variable mean and variance values and irregular shapes. If the first segments are not correctly delimited then the following steps will merge segments from different fields. In examining the evolution of the initial segments, we see that the merging should take into account spatial aspects. Particularly, the segment contours should have good shapes. In this paper, we examine how the pseudo-convex envelope of a region can be used to evaluate the region contour. We present a pseudo-convex measure adapted to the geometry of image lattice. We show how the pseudo-convex envelope can be calculated. We present measures comparing contour shapes and using the perimeter, the area and the boundary length of segments. We use a hierarchical segmentation algorithm based upon stepwise optimization. A stepwise merging criterion is derived from the multiplicative speckle noise model. The shape measures are combined with the merging criterion in order to guide correctly the segment merging process. The new criterion produces good segmentation of SAR images. This is illustrated by synthetic and real image results.},
mypdf = {11},
keywords = {Area measurement; Geometry; Image segmentation; Lattices; Merging; Noise level; Production; Shape measurement; Speckle; Synthetic aperture radar},
openpdf = {},
openid = {}
}
[Bea2005b] Segmentation of Polarimetric Sar Images Composed of Textured and Non-Textured Fields,
Beaulieu Jean-Marie, Ridha Touzi,
Advanced SAR Workshop 2005, Canadian Space Agency, Montreal (Saint-Hubert), 15-17 Nov. 2005, p. 6.
[PDF]   [URL]   [.. More]   [Bibtex]  
@Conference{Bea2005b,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
editor = {},
title = {Segmentation of Polarimetric Sar Images Composed of Textured and Non-Textured Fields},
booktitle = {Advanced SAR Workshop 2005, Canadian Space Agency},
volume = {},
publisher = {},
url = {http://www.asc-csa.gc.ca},
isbn = {},
doi = {},
address = {Montreal (Saint-Hubert)},
pages = {6},
year = {2005},
month = {15-17 Nov.},
abstract = {},
mypdf = {7},
keywords = {},
openpdf = {},
openid = {Beaulieu 2005}
}
[Bea2005a] Évaluation des Mesures de Dissimilarité entre Régions dans les Images SAR,
Beaulieu Jean-Marie,
12ème Congrès de l’Association Québécoise de Télédétection, Chicoutimi (Québec) Canada, 10-12 mai 2005, pp. 1-8.
[PDF]   [URL]   [.. More]   [Bibtex]   [Abstract]  
Nous montrons comment les courbes de la probabilité de détection vs la probabilité de fausse alarme (courbes ROC) peuvent être utilisées pour comparer différentes mesures ou critères de détection d’arêtes ou de segmentation d’images radar. Les 3 critères étudiés sont 1) le logarithme du rapport de vraisemblance, 2) le rapport des moyennes et 3) une adaptation du critère de Ward pour les images SAR. Les 3 critères donnent des résultats identiques lorsque nous utilisons 2 ré- gions de même taille. Lorsque les régions sont petites et ont une différence de taille importante, nous obtenons des courbes différentes selon que la région d’intensité la plus faible est plus petite ou plus grande que l’autre région. Nous observons alors une différence entre les 3 critères. Nous notons alors un léger avantage pour le logarithme du rapport de vraisemblance. La similarité des résultats suggère que nous pourrions utiliser indifféremment une mesure ou l’autre dans plusieurs applications. Nous avons examiné comment le critère du rapport des moyennes peut être utilisé dans la segmentation hiérarchique et nous avons comparé le résultat obtenu sur une image de synthèse avec les 2 autres critères.
@Conference{Bea2005a,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {{\'E}valuation des Mesures de Dissimilarit{\'e} entre R{\'e}gions dans les Images {SAR}},
booktitle = {12{\`e}me Congr{\`e}s de l'Association Qu{\'e}b{\'e}coise de T{\'e}l{\'e}d{\'e}tection},
volume = {},
publisher = {},
url = {http://laqt.org},
isbn = {},
doi = {},
address = {Chicoutimi (Qu{\'e}bec) Canada},
pages = {1-8},
year = {2005},
month = {10-12 mai},
abstract = {Nous montrons comment les courbes de la probabilit{\'e} de d{\'e}tection vs la probabilit{\'e} de fausse alarme (courbes ROC) peuvent {\^e}tre utilis{\'e}es pour comparer diff{\'e}rentes mesures ou crit{\`e}res de d{\'e}tection d'ar{\^e}tes ou de segmentation d'images radar. Les 3 crit{\`e}res {\'e}tudi{\'e}s sont 1) le logarithme du rapport de vraisemblance, 2) le rapport des moyennes et 3) une adaptation du crit{\`e}re de Ward pour les images SAR. Les 3 crit{\`e}res donnent des r{\'e}sultats identiques lorsque nous utilisons 2 r{\'e}- gions de m{\^e}me taille. Lorsque les r{\'e}gions sont petites et ont une diff{\'e}rence de taille importante, nous obtenons des courbes diff{\'e}rentes selon que la r{\'e}gion d'intensit{\'e} la plus faible est plus petite ou plus grande que l'autre r{\'e}gion. Nous observons alors une diff{\'e}rence entre les 3 crit{\`e}res. Nous notons alors un l{\'e}ger avantage pour le logarithme du rapport de vraisemblance. La similarit{\'e} des r{\'e}sultats sugg{\`e}re que nous pourrions utiliser indiff{\'e}remment une mesure ou l'autre dans plusieurs applications. Nous avons examin{\'e} comment le crit{\`e}re du rapport des moyennes peut {\^e}tre utilis{\'e} dans la segmentation hi{\'e}rarchique et nous avons compar{\'e} le r{\'e}sultat obtenu sur une image de synth{\`e}se avec les 2 autres crit{\`e}res.},
mypdf = {7},
keywords = {},
openpdf = {},
openid = {Beaulieu 2005}
}
[Bea2004a] “Segmentation of Textured Polarimetric SAR Scenes by Likelihood Approximation,”
Beaulieu Jean-Marie, Ridha Touzi,
IEEE Transactions on Geoscience and Remote Sensing, vol. 42, iss. 10, p. 2063–2072, Oct. 2004.
[PDF]   [URL]   [DOI]   [Open]   [.. More]   [Bibtex]   [Abstract]  
A hierarchical stepwise optimization process is developed for polarimetric synthetic aperture radar image segmentation. We show that image segmentation can be viewed as a likelihood approximation problem. The likelihood segment merging criteria are derived using the multivariate complex Gaussian, the Wishart distribution, and the K-distribution. In the presence of spatial texture, the Gaussian-Wishart segmentation is not appropriate. The K-distribution segmentation is more effective in textured forested areas. The validity of the product model is also assessed, and a field-adaptable segmentation strategy combining different criteria is examined.
@Article{Bea2004a,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
title = {Segmentation of Textured Polarimetric {SAR} Scenes by Likelihood Approximation},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {42},
number = {10},
pages = {2063--2072},
year = {2004},
month = {Oct.},
abstract = {A hierarchical stepwise optimization process is developed for polarimetric synthetic aperture radar image segmentation. We show that image segmentation can be viewed as a likelihood approximation problem. The likelihood segment merging criteria are derived using the multivariate complex Gaussian, the Wishart distribution, and the K-distribution. In the presence of spatial texture, the Gaussian-Wishart segmentation is not appropriate. The K-distribution segmentation is more effective in textured forested areas. The validity of the product model is also assessed, and a field-adaptable segmentation strategy combining different criteria is examined.},
publisher = {},
url = {https://ieeexplore.ieee.org/document/1344159},
isbn = {0196-2892},
doi = {10.1109/TGRS.2004.835302},
mypdf = {11},
address = {},
keywords = {},
openpdf = {https://www.academia.edu/4591322/Segmentation_of_textured_polarimetric_SAR_scenes_by_likelihood_approximation},
openid = {Academia}
}
[Bea2004b] “Utilisation of Contour Criteria in Micro-Segmentation of SAR Images,”
Beaulieu Jean-Marie,
International Journal of Remote Sensing, vol. 25, iss. 17, p. 3497–3512, Sept. 2004.
[PDF]   [URL]   [DOI]   [Open]   [.. More]   [Bibtex]   [Abstract]  
The segmentation of SAR (Synthetic Aperture Radar) images is greatly complicated by the presence of coherent speckle. To carry out this process a hierarchical segmentation algorithm based on stepwise optimization is used. It starts with each individual pixel as a segment and then sequentially merges the segment pair that minimizes the criterion. In a hypothesis testing approach, we show how the stepwise merging criterion is derived from the probability model of image regions. The Ward criterion is derived from the Gaussian additive noise model. A new criterion is derived from the multiplicative speckle noise model of SAR images. The first merging steps produce micro-regions. With standard merging criteria, the high noise level of SAR images results in the production of micro-regions that have unreliable mean and variance values and irregular shapes. If the micro-segments are not correctly delimited then the following steps will merge segments from different fields. In examining the evolution of the initial segments, we see that the merging should take into account spatial aspects. In particular, the segment contours should have good shapes. We present three measures based on contour shapes, using the perimeter, the area and the boundary length of segments. These measures are combined with the SAR criterion in order to guide correctly the segment merging process. The new criterion produces good micro-segmentation of SAR images. The criterion is also used in the following merges to produce larger segments. This is illustrated by synthetic and real image results.
@Article{Bea2004b,
author = {Beaulieu, Jean-Marie},
title = {Utilisation of Contour Criteria in Micro-Segmentation of {SAR} Images},
journal = {International Journal of Remote Sensing},
volume = {25},
number = {17},
pages = {3497--3512},
year = {2004},
month = {Sept.},
abstract = {The segmentation of SAR (Synthetic Aperture Radar) images is greatly complicated by the presence of coherent speckle. To carry out this process a hierarchical segmentation algorithm based on stepwise optimization is used. It starts with each individual pixel as a segment and then sequentially merges the segment pair that minimizes the criterion. In a hypothesis testing approach, we show how the stepwise merging criterion is derived from the probability model of image regions. The Ward criterion is derived from the Gaussian additive noise model. A new criterion is derived from the multiplicative speckle noise model of SAR images. The first merging steps produce micro-regions. With standard merging criteria, the high noise level of SAR images results in the production of micro-regions that have unreliable mean and variance values and irregular shapes. If the micro-segments are not correctly delimited then the following steps will merge segments from different fields. In examining the evolution of the initial segments, we see that the merging should take into account spatial aspects. In particular, the segment contours should have good shapes. We present three measures based on contour shapes, using the perimeter, the area and the boundary length of segments. These measures are combined with the SAR criterion in order to guide correctly the segment merging process. The new criterion produces good micro-segmentation of SAR images. The criterion is also used in the following merges to produce larger segments. This is illustrated by synthetic and real image results.},
publisher = {},
url = {http://www.tandfonline.com/doi/abs/10.1080/01431160310001647714},
isbn = {0143-1161},
doi = {10.1080/01431160310001647714},
mypdf = {11},
address = {},
keywords = {},
openpdf = {https://pdfs.semanticscholar.org/190a/fd1a2dd30e24ea998bf7c99a66505ca78929.pdf?_ga=2.93565854.2051265828.1566578008-697872498.1566578008},
openid = {Semantics}
}
[Bea2003b] Utilisation of Segment Border Information in Hierarchical Segmentation,
Beaulieu Jean-Marie,
25rd Canadian Symposium on Remote Sensing, Oct. 2003.
[Bibtex]  
@Conference{Bea2003b,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Utilisation of Segment Border Information in Hierarchical Segmentation},
booktitle = {25rd Canadian Symposium on Remote Sensing},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {},
year = {2003},
month = {Oct.},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 2003}
}
[Bea2003c] Segmentation of Textured Areas using Polarimetric SAR,
Beaulieu Jean-Marie, Ridha Touzi,
25rd Canadian Symposium on Remote Sensing, Oct. 2003.
[Bibtex]  
@Conference{Bea2003c,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
editor = {},
title = {Segmentation of Textured Areas using Polarimetric {SAR}},
booktitle = {25rd Canadian Symposium on Remote Sensing},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {},
year = {2003},
month = {Oct.},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 2003}
}
[Bea2003e] Classification and Segmentation of Radar Polarimetric Images,
Beaulieu Jean-Marie, Ridha Touzi,
Classification Society of North America Annual Meeting, 2003, June 2003.
[Bibtex]  
@Conference{Bea2003e,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
editor = {},
title = {Classification and Segmentation of Radar Polarimetric Images},
booktitle = {Classification Society of North America Annual Meeting, 2003},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {},
year = {2003},
month = {June},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 2003}
}
[Bea:2003d] Segmentation of Polarimetric SAR Images: a Best Estimate Partitioning Approach,
Beaulieu Jean-Marie, Ridha Touzi,
Advanced SAR Workshop 2003, Canadian Space Agency, June 2003, pp. 1-7.
[Bibtex]  
@Conference{Bea:2003d,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
editor = {},
title = {Segmentation of Polarimetric {SAR} Images: a Best Estimate Partitioning Approach},
booktitle = {Advanced SAR Workshop 2003, Canadian Space Agency},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {1-7},
year = {2003},
month = {June},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 2003}
}
[Bea2003a] Segmentation of Textured Scenes using Polarimetric SARs,
Beaulieu Jean-Marie, Ridha Touzi,
IEEE International Geoscience and Remote Sensing Symposium, GARSS’03, IEEE, 2003, pp. 446-448.
[URL]   [Bibtex]  
@Conference{Bea2003a,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
editor = {},
title = {Segmentation of Textured Scenes using Polarimetric {SAR}s},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium, GARSS'03},
volume = {1},
publisher = {},
url = {Google Scholar},
isbn = {},
doi = {},
address = {IEEE},
pages = {446-448},
year = {2003},
month = {},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 2003a}
}
[Bea2002] Hierarchical Segmentation of Polarimetric SAR Images,
Beaulieu Jean-Marie, Ridha Touzi,
IEEE International Geoscience and Remote Sensing Symposium, IGARSS’02, IEEE, 2002, pp. 2590-2592.
[URL]   [Bibtex]  
@Conference{Bea2002,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
editor = {},
title = {Hierarchical Segmentation of Polarimetric {SAR} Images},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS'02},
volume = {5},
publisher = {},
url = {Google Scholar},
isbn = {},
doi = {},
address = {IEEE},
pages = {2590-2592},
year = {2002},
month = {},
abstract = {},
mypdf = {},
keywords = {nw-02},
openpdf = {},
openid = {Beaulieu 2002}
}
[Bea2001b] SAR Image Enhancement: Combining Image Filtering and Segmentation,
Beaulieu Jean-Marie,
The 2001 International Conference on Imaging Science, Systems, and Technology, CISST’2001, August 2001, pp. 327-333.
[Open]   [Bibtex]  
@Conference{Bea2001b,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {{SAR} Image Enhancement: Combining Image Filtering and Segmentation},
booktitle = {The 2001 International Conference on Imaging Science, Systems, and Technology, CISST'2001},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {327-333},
year = {2001},
month = {August},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {https://pdfs.semanticscholar.org/0a0f/1f0e8e1399851573fed0973d73e4343c67a2.pdf?_ga=2.156291896.2051265828.1566578008-697872498.1566578008},
openid = {Beaulieu 2001a}
}
[Bea2001a] Utilisation of Contour Criteria in Micro-Segmentation of SAR Images,
Beaulieu Jean-Marie,
23rd Canadian Symposium on Remote Sensing, August 2001, pp. 91-100.
[Bibtex]  
@Conference{Bea2001a,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Utilisation of Contour Criteria in Micro-Segmentation of {SAR} Images},
booktitle = {23rd Canadian Symposium on Remote Sensing},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {91-100},
year = {2001},
month = {August},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 2001}
}
[Bea2000a] Hierarchical Segmentation of SAR Images with Shape Criteria,
Beaulieu Jean-Marie, Guy Mineau,
Classification Society of North America Annual Meeting, 2000, June 2000, p. 35.
[Bibtex]  
@Conference{Bea2000a,
author = {Beaulieu, Jean-Marie and Mineau, Guy},
editor = {},
title = {Hierarchical Segmentation of {SAR} Images with Shape Criteria},
booktitle = {Classification Society of North America Annual Meeting, 2000},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {35},
year = {2000},
month = {June},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 2000}
}
[Bea2000b] Détection des Arbres Individuels dans des Images de Haute Résolution,
Beaulieu Jean-Marie, Mohammed Bouzkraoui,
Vision Interface 2000, mai 2000, pp. 311-317.
[Bibtex]  
@Conference{Bea2000b,
author = {Beaulieu, Jean-Marie and Bouzkraoui, Mohammed},
editor = {},
title = {D{\'e}tection des Arbres Individuels dans des Images de Haute R{\'e}solution},
booktitle = {Vision Interface 2000},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {311-317},
year = {2000},
month = {mai},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 2000}
}
[Bea1999] Evaluation of a Least Commitment Approach for Feature Preserving in SAR Image Filtering,
Beaulieu Jean-Marie, Guy Mineau,
Classification Society of North America Annual Meeting, 1999, August 1999, p. 8.
[Bibtex]  
@Conference{Bea1999,
author = {Beaulieu, Jean-Marie and Mineau, Guy},
editor = {},
title = {Evaluation of a Least Commitment Approach for Feature Preserving in {SAR} Image Filtering},
booktitle = {Classification Society of North America Annual Meeting, 1999},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {8},
year = {1999},
month = {August},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 1999}
}
[Min1998] “An object indexing methodology as support to object recognition,”
Mineau Guy W, Mounsif Lahboub, Jean-Marie Beaulieu,
in Advances in Artificial Intelligence, Université Laval, Springer Berlin Heidelberg, 1998, pp. 72-85.
[URL]   [DOI]   [Bibtex]  
@incollection{Min1998,
author = {Mineau, Guy W and Lahboub, Mounsif and Beaulieu, Jean-Marie},
title = {An object indexing methodology as support to object recognition},
booktitle = {Advances in Artificial Intelligence},
editor = {},
publisher = {Springer Berlin Heidelberg},
address = {Universit{\'e} Laval},
pages = {72-85},
year = {1998},
month = {},
url = {SpringerLink},
isbn = {},
doi = {10.1007/3-540-64575-6_41},
mypdf = {},
openpdf = {},
openid = {Mineau 1998}
abstract = {This paper presents an object recognition methodology which uses a step-by-step discrimination process. This process is made possible by the use of a classification structure built over examples of the objects to recognize. Thus, our approach combines numerical vision (object recognition) with conceptual clustering, showing how the latter helps the former, giving another example of useful synergy among different AI techniques. It presents our application domain: the recognition of road signs, which must support semi-autonomous vehicles in their navigational task. The discrimination process allows appropriate actions to be taken by the recognizer with regard to the actual data it has to recognize the object from: light, angle, shading, etc., and with regard to its recognition capabilities and their associated cost. Therefore, this paper puts the emphasis on this multiple criteria adaptation capability, which is the novelty of our approach.},
keywords = {}}
[Naj1997] A Common Evaluation Approach to Smooting and Feature Preservation in SAR Image Filtering,
Najeh Maher, Jean-Marie Beaulieu,
Symposium International La Géomatique à l’Ère de Radasat, Ottawa, 1997.
[Bibtex]  
@Conference{Naj1997,
author = {Najeh, Maher and Beaulieu, Jean-Marie},
editor = {},
title = {A Common Evaluation Approach to Smooting and Feature Preservation in {SAR} Image Filtering},
booktitle = {Symposium International La G{\'e}omatique {\`a} l'{\`E}re de Radasat},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {Ottawa},
pages = {},
year = {1997},
month = {},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 1997}
}
[Bea1997a] A Least Commitment Approach to SAR Image Filtering,
Beaulieu Jean-Marie, Maher Najeh,
Symposium International La Géomatique à l’Ère de Radasat, Ottawa, mai 1997.
[Bibtex]  
@Conference{Bea1997a,
author = {Beaulieu, Jean-Marie and Najeh, Maher},
editor = {},
title = {A Least Commitment Approach to {SAR} Image Filtering},
booktitle = {Symposium International La G{\'e}omatique {\`a} l'{\`E}re de Radasat, Ottawa},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {},
year = {1997},
month = {mai},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 1997}
}
[Bea1996] Filtrage des Images Radar par Détection des Régions Homogènes,
Beaulieu Jean-Marie,
9e Congrès de l’Association Québécoise de Télédétection, avril 1996.
[Bibtex]  
@Conference{Bea1996,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Filtrage des Images Radar par D{\'e}tection des R{\'e}gions Homog{\`e}nes},
booktitle = {9e Congr{\`e}s de l'Association Qu{\'e}b{\'e}coise de T{\'e}l{\'e}d{\'e}tection},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {},
year = {1996},
month = {avril},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 1996}
}
[Bel1992] “Post-segmentation classification of images containing small agricultural fields,”
Belaid Ait M, G Edwards, A Jaton, KPB Thomson, Jean-Marie Beaulieu,
Geocarto International, vol. 7, iss. 3, p. 53–60, 1992.
[URL]   [DOI]   [.. More]   [Bibtex]   [Abstract]  
This paper presents results from applying a hierarchical segmentation algorithm to two agricultural data sets characterised by small fields. Several new techniques were developed over the course of this project. These include a new supervised classification technique for identifying segments and the inclusion of other information derived from the segmentation process. In addition, a technique for including cartographic information to help structure the segmentation is also described. The results indicate that significant improvement in classification accuracy can be achieved. A number of problems which arise when working with segmentation data are also reported and discussed. These problems appear to be different enough from those encountered in pixel classifications to be worth describing in greater detail. The paper concludes with the lines of research being pursued to circumvent these problems and to further increase the fidelity of the segmentation results.
@Article{Bel1992,
author = {Belaid, M Ait and Edwards, G and Jaton, A and Thomson, K P B and Beaulieu, Jean-Marie},
title = {Post-segmentation classification of images containing small agricultural fields},
journal = {Geocarto International},
volume = {7},
number = {3},
pages = {53--60},
year = {1992},
month = {},
abstract = {This paper presents results from applying a hierarchical segmentation algorithm to two agricultural data sets characterised by small fields. Several new techniques were developed over the course of this project. These include a new supervised classification technique for identifying segments and the inclusion of other information derived from the segmentation process. In addition, a technique for including cartographic information to help structure the segmentation is also described. The results indicate that significant improvement in classification accuracy can be achieved.
A number of problems which arise when working with segmentation data are also reported and discussed. These problems appear to be different enough from those encountered in pixel classifications to be worth describing in greater detail. The paper concludes with the lines of research being pursued to circumvent these problems and to further increase the fidelity of the segmentation results.},
publisher = {Taylor \& Francis},
url = {https://www.tandfonline.com/doi/abs/10.1080/10106049209354380},
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doi = {10.1080/10106049209354380},
mypdf = {13},
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openpdf = {},
openid = {Belaid 1992}
}
[Jao1992] Optimal rectangular decomposition of a finite binary relation,
Jaoua A, Jean-Marie Beaulieu, N Belkhiter, J Deshernais, M Reguig,
International Conference on Discrete Mathematics (sixth conference, 1992.
[URL]   [Bibtex]  
@Conference{Jao1992,
author = {Jaoua, A and Beaulieu, Jean-Marie and Belkhiter, N and Deshernais, J and Reguig, M},
editor = {},
title = {Optimal rectangular decomposition of a finite binary relation},
booktitle = {International Conference on Discrete Mathematics (sixth conference},
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year = {1992},
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}
[Bea1991] Programming of Application Interface and Image Access Made Simple,
Beaulieu Jean-Marie,
Canadian Conference on Electrical and Computer Engineering, Sept. 1991, p. 23.1.1-4.
[Bibtex]  
@Conference{Bea1991,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Programming of Application Interface and Image Access Made Simple},
booktitle = {Canadian Conference on Electrical and Computer Engineering},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {23.1.1-4},
year = {1991},
month = {Sept.},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 1991}
}
[Bea1990b] Hierarchical Segmentation of SAR Picture,
Beaulieu Jean-Marie,
Image’Com 90, Bordeaux, Nov. 1990, pp. 392-397.
[Bibtex]  
@Conference{Bea1990b,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Hierarchical Segmentation of {SAR} Picture},
booktitle = {Image'Com 90, Bordeaux},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {392-397},
year = {1990},
month = {Nov.},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 1990}
}
[Bea1990a] Versatile And Efficient Hierarchical Clustering For Picture Segmentation,
Beaulieu Jean-Marie,
International Geoscience and Remote Sensing Symposium, IGARSS’90, Universite Laval, May 1990, pp. 1663-1663.
[URL]   [DOI]   [Bibtex]  
@Conference{Bea1990a,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Versatile And Efficient Hierarchical Clustering For Picture Segmentation},
booktitle = {International Geoscience and Remote Sensing Symposium, IGARSS'90},
volume = {},
publisher = {IEEE},
url = {IEEE},
isbn = {},
doi = {10.1109/IGARSS.1990.688832},
address = {Universite Laval},
pages = {1663-1663},
year = {1990},
month = {May},
abstract = {},
mypdf = {},
keywords = {Approximation algorithms; Approximation error; Bismuth; Clustering algorithms; Corporate acquisitions; Data structures; Image segmentation; Partitioning algorithms; Probability; Shape},
openpdf = {},
openid = {Beaulieu 1990}
}
[Kal1990] Segmentation of SAR Picture,
Kaliaguine Nicolas, Jean-Marie Beaulieu,
Canadian Conference on Electrical and Computer Engineering, 1990, p. 69.5.1-4.
[Bibtex]  
@Conference{Kal1990,
author = {Kaliaguine, Nicolas and Beaulieu, Jean-Marie},
editor = {},
title = {Segmentation of {SAR} Picture},
booktitle = {Canadian Conference on Electrical and Computer Engineering},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {69.5.1-4},
year = {1990},
month = {},
abstract = {},
mypdf = {},
keywords = {},
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openid = {Beaulieu 1990}
}
[Edw1990] Cartographic Information as a Structuring Principle for Image Segmentation,
Edwards G, M Ait-Belaid, KPB Thomson, G Cauchon, Jean-Marie Beaulieu,
ISPRS Commission II/VII International Workshop, University of Main, Orono, 1990.
[.. More]   [Bibtex]  
@Conference{Edw1990,
author = {Edwards, G and Ait-Belaid, M and Thomson, K P B and Cauchon, G and Beaulieu, Jean-Marie},
editor = {},
title = {Cartographic Information as a Structuring Principle for Image Segmentation},
booktitle = {ISPRS Commission II/VII International Workshop},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {University of Main, Orono},
pages = {},
year = {1990},
month = {},
abstract = {},
mypdf = {5},
keywords = {},
openpdf = {},
openid = {Belaid 1990}
}
[Edw1989] Segmentation of SAR Imagery Containing Forest Clear Cuts,
Edwards G, Jean-Marie Beaulieu,
IEEE International Geoscience and Remote Sensing Symposium, IGARSS’89, Vancouver, Canada, 10-14 July 1989, pp. 1195-1197.
[URL]   [DOI]   [.. More]   [Bibtex]   [Abstract]  
A Hierarchical Step-Wise Optimisation (HSWO) algorithm has been adapted to the problem of identifying and mapping forest clear cuts in synthetic aperture radar (SAR) C-band imagery. Preliminary results are presented. The mean grey level of a segment is the most useful segment discriminator, especially for recent clear cuts, but relative segment size and the ratio of perimeter length to surface area (P/A) appear to be useful secondary discriminators. A filtered image which is segmented appears to be the most reliable for locating clear cuts, whereas the unfiltered image, when segmented, yields better boundary information. A method for combining both segment partitions is presented. All clear cuts in the sample were identified. Surface areas concord with manually estimated values.
@Conference{Edw1989,
author = {Edwards, G and Beaulieu, Jean-Marie},
editor = {},
title = {Segmentation of {SAR} Imagery Containing Forest Clear Cuts},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS'89},
volume = {3},
publisher = {IEEE},
url = {https://ieeexplore.ieee.org/abstract/document/576042},
isbn = {},
doi = {10.1109/IGARSS.1989.576042},
address = {Vancouver, Canada},
pages = {1195-1197},
year = {1989},
month = {10-14 July},
abstract = {A Hierarchical Step-Wise Optimisation (HSWO) algorithm has been adapted to the problem of identifying and mapping forest clear cuts in synthetic aperture radar (SAR) C-band imagery.
Preliminary results are presented. The mean grey level of a segment is the most useful segment discriminator, especially for recent clear cuts, but relative segment size and the ratio of perimeter length to surface area (P/A) appear to be useful secondary discriminators. A filtered image which is segmented appears to be the most reliable for locating clear cuts, whereas the unfiltered image, when segmented, yields better boundary information. A method for combining both segment partitions is presented. All clear cuts in the sample were identified. Surface areas concord with manually estimated values.},
mypdf = {13},
keywords = {Clouds; Forestry; Image segmentation; Information filtering; Information filters; Layout; Partitioning algorithms; Pixel; Satellites; Synthetic aperture radar},
openpdf = {},
openid = {Edwards 1989}
}
[Val1988] Quantitative Evaluation of Image Segmentation Techniques,
Velarde Cesar, Jean-Marie Beaulieu,
Canadian Conference on Electrical and Computer Engineering, 1989, pp. 314-317.
[Bibtex]  
@Conference{Val1988,
author = {Velarde, Cesar and Beaulieu, Jean-Marie},
editor = {},
title = {Quantitative Evaluation of Image Segmentation Techniques},
booktitle = {Canadian Conference on Electrical and Computer Engineering},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {314-317},
year = {1989},
month = {},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 1989}
}
[Bel1989] Sementation d’image spot integree a l’information cartographique en vu de l’etablissment de la carte d’utilization de sol au maroc,
Belaid Ait M, KPB Thomson, G Edwards, Jean-Marie Beaulieu,
IEEE International Geoscience and Remote Sensing Symposium, IGARSS’89, Vancouver, Canada, 10-14 July 1989, pp. 56-59.
[URL]   [DOI]   [.. More]   [Bibtex]   [Abstract]  
This paper is concerned with the integration of remote sensing and conventional data. It presents a purely digital method of merging a multispectral SPOT image and field boundaries. This yields a product which is a new image registered to the national grid of Morocco, having four channels with images resampled to 10 m. The fou rth channel contains the field boundaries which are di gitized using the spatial information system PAMAP. A Hierarchical Step -Wise Optimization (HSWO) algorithm developed by Beaulieu is applied to the new four band “image” to test the capability of the segmentation to map the land use and to provide the crop inventory in small areas of land.
@Conference{Bel1989,
author = {Belaid, M Ait and Thomson, K P B and Edwards, G and Beaulieu, Jean-Marie},
editor = {},
title = {Sementation d'image spot integree a l'information cartographique en vu de l'etablissment de la carte d'utilization de sol au maroc},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS'89},
volume = {1},
publisher = {IEEE},
url = {https://ieeexplore.ieee.org/abstract/document/567151},
isbn = {},
doi = {10.1109/IGARSS.1989.567151},
address = {Vancouver, Canada},
pages = {56-59},
year = {1989},
month = {10-14 July},
abstract = {This paper is concerned with the integration of remote sensing and conventional data. It presents a purely digital method of merging a multispectral SPOT image and field boundaries. This yields a product which is a new image registered to the national grid of Morocco, having four channels with images resampled to 10 m. The fou rth channel contains the field boundaries which are di gitized using the spatial information system PAMAP.
A Hierarchical Step -Wise Optimization (HSWO) algorithm developed by Beaulieu is applied to the new four band ``image'' to test the capability of the segmentation to map the land use and to provide the crop inventory in small areas of land.},
mypdf = {13},
keywords = {},
openpdf = {},
openid = {Belaid 1989}
}
[Bea1989c] “Segmentation Hiérarchique de l’Image par Optimisation Séquentielle,”
Beaulieu Jean-Marie,
in Télédétection et Gestion des Ressources, Association Québécoise de Télédétection, Québec, 1989, pp. 245-251.
[Bibtex]  
@incollection{Bea1989c,
author = {Beaulieu, Jean-Marie},
title = {Segmentation Hi{\'e}rarchique de l'Image par Optimisation S{\'e}quentielle},
booktitle = {T{\'e}l{\'e}d{\'e}tection et Gestion des Ressources},
editor = {},
publisher = {Association Qu{\'e}b{\'e}coise de T{\'e}l{\'e}d{\'e}tection, Qu{\'e}bec},
address = {},
pages = {245-251},
year = {1989},
month = {},
url = {},
isbn = {},
doi = {},
mypdf = {},
openpdf = {},
openid = {Beaulieu 1989}
abstract = {},
keywords = {}}
[Bea1989b] “Segmentation of Range Images by Piecewise Approximation with Shape Constraints,”
Beaulieu Jean-Marie, Pierre Boulanger,
in Computer Vision and Shape Recognition, World Scientific Pub Co Inc, 1989, p. 87.
[URL]   [Bibtex]  
@incollection{Bea1989b,
author = {Beaulieu, Jean-Marie and Boulanger, Pierre},
title = {Segmentation of Range Images by Piecewise Approximation with Shape Constraints},
booktitle = {Computer Vision and Shape Recognition},
editor = {},
publisher = {World Scientific Pub Co Inc},
address = {},
pages = {87},
year = {1989},
month = {},
url = {Google Scholar},
isbn = {},
doi = {},
mypdf = {},
openpdf = {},
openid = {Beaulieu 1989}
abstract = {},
keywords = {}}
[Bea1989a] “Hierarchy in Picture Segmentation: a Stepwise Optimization Approach,”
Beaulieu Jean-Marie, Moris Goldberg,
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, iss. 2, p. 150–163, 1989.
[PDF]   [URL]   [DOI]   [.. More]   [Bibtex]   [Abstract]  
A segmentation algorithm based on sequential optimization which produces a hierarchical decomposition of the picture is presented. The decomposition is data driven with no restriction on segment shapes. It can be viewed as a tree, where the nodes correspond to picture segments and where links between nodes indicate set inclusions. Picture segmentation is first regarded as a problem of piecewise picture approximation, which consists of finding the partition with the minimum approximation error. Then, picture segmentation is presented as an hypothesis-testing process which merges only segments that belong to the same region. A hierarchical decomposition constraint is used in both cases, which results in the same stepwise optimization algorithm. At each iteration, the two most similar segments are merged by optimizing a stepwise criterion. The algorithm is used to segment a remote-sensing picture, and illustrate the hierarchical structure of the picture
@Article{Bea1989a,
author = {Beaulieu, Jean-Marie and Goldberg, Moris},
title = {Hierarchy in Picture Segmentation: a Stepwise Optimization Approach},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {11},
number = {2},
pages = {150--163},
year = {1989},
month = {},
abstract = {A segmentation algorithm based on sequential optimization which produces a hierarchical decomposition of the picture is presented. The decomposition is data driven with no restriction on segment shapes. It can be viewed as a tree, where the nodes correspond to picture segments and where links between nodes indicate set inclusions. Picture segmentation is first regarded as a problem of piecewise picture approximation, which consists of finding the partition with the minimum approximation error. Then, picture segmentation is presented as an hypothesis-testing process which merges only segments that belong to the same region. A hierarchical decomposition constraint is used in both cases, which results in the same stepwise optimization algorithm. At each iteration, the two most similar segments are merged by optimizing a stepwise criterion. The algorithm is used to segment a remote-sensing picture, and illustrate the hierarchical structure of the picture},
publisher = {},
url = {https://ieeexplore.ieee.org/document/16711},
isbn = {0162-8828},
doi = {10.1109/34.16711},
mypdf = {12},
address = {},
keywords = {computerised picture processing; data structure; hierarchical decomposition; iterative methods; optimisation; picture segmentation; sequential optimization; stepwise optimization; tree; trees (mathematics); nw-05},
openpdf = {},
openid = {}
}
[Bea1988a] Segmentation of Range Image by Piecewise Approximation with Shape Constraints,
Beaulieu Jean-Marie, Pierre Boulanger,
Vision Interface’88, Edmonton, Canada, June 1988, pp. 19-14.
[Bibtex]  
@Conference{Bea1988a,
author = {Beaulieu, Jean-Marie and Boulanger, Pierre},
editor = {},
title = {Segmentation of Range Image by Piecewise Approximation with Shape Constraints},
booktitle = {Vision Interface'88},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {Edmonton, Canada},
pages = {19-14},
year = {1988},
month = {June},
abstract = {},
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keywords = {},
openpdf = {},
openid = {Beaulieu 1988}
}
[Bea1988b] Segmentation Hiérarchique de l’Image par Optimisation Séquentielle,
Beaulieu Jean-Marie,
6e Congrès de l’Association Québécoise de Télédétection, juin 1988.
[Bibtex]  
@Conference{Bea1988b,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Segmentation Hi{\'e}rarchique de l'Image par Optimisation S{\'e}quentielle},
booktitle = {6e Congr{\`e}s de l'Association Qu{\'e}b{\'e}coise de T{\'e}l{\'e}d{\'e}tection},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {},
year = {1988},
month = {juin},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 1988}
}
[Bea1985] “Selection of Segment Similarity Measures for Hierarchical Picture Segmentation,”
Beaulieu Jean-Marie, M Goldberg,
in Computer-Generated Images, Springer, 1985, pp. 87-97.
[URL]   [Bibtex]  
@incollection{Bea1985,
author = {Beaulieu, Jean-Marie and Goldberg, M},
title = {Selection of Segment Similarity Measures for Hierarchical Picture Segmentation},
booktitle = {Computer-Generated Images},
editor = {},
publisher = {Springer},
address = {},
pages = {87-97},
year = {1985},
month = {},
url = {https://link.springer.com/chapter/10.1007/978-4-431-68033-8_8},
isbn = {},
doi = {},
mypdf = {},
openpdf = {},
openid = {Beaulieu 1985}
abstract = {The problem of defining appropriate segment similarity measures for picture segmentation is exmained. In agglomerative hierqarchical segmentation, two segments are coamapared and merged if found similar. The propesed Hierarchical Step-Wise Optimization (HSWO) algorithm finds and then merges the two most similar segements, on a step-by-step basis. By considering picture segmentation as a piece-wise picture approximation problem, the similarity measure (or the step-wise criterion) is related to the overall approximation error. The measure then corresponds to the increase of the approximation error resulting from merging two segments. Similarity measures derived from constant approximations (zeroth order polynomials) and planar approximations (first order polynomials). An adaptive measurebased upon local variance is also used. The advantages of combining similarity measures (or cirteria) are also stressed. Different picture areas can require different measures which must therefore be combined in order to obtain good overall results. Moreover, in hierarchical segmentation, simple measures can be used for the first merging steps, while, at a higher level of the segment hierarchy, more complex measures can be employed.},
keywords = {Hierarchical segmentation; Similarity measures; Clustering}}
[Bea1984] “Hierarchical Picture Segmentation by Step-Wise Optimization,”
Beaulieu Jean-Marie, Ph.D.,
PhD Thesis, Electrical Engineering, University of Ottawa (Canada)., 1984.
[URL]   [Bibtex]  
@phdthesis{Bea1984,
author = {Beaulieu, Jean-Marie},
title = {Hierarchical Picture Segmentation by Step-Wise Optimization},
school = {University of Ottawa (Canada).},
dept = {Electrical Engineering},
degree = {Ph.D.},
thesis = {PhD},
address = {},
pages = {},
year = {1984},
month = {},
publisher = {University of Ottawa (Canada).},
url = {Google Scholar},
isbn = {},
doi = {},
wdown = {},
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openid = {Beaulieu 1984}
abstract = {},
keywords = {nw-01}}
[Bea1983] Step-Wise Optimization for Hierarchical Picture Segmentation,
Beaulieu Jean-Marie, Morris Goldberg,
Conference on Computer Vision and Pattern Recognition, 1983, pp. 59-64.
[URL]   [Bibtex]  
@Conference{Bea1983,
author = {Beaulieu, Jean-Marie and Goldberg, Morris},
editor = {},
title = {Step-Wise Optimization for Hierarchical Picture Segmentation},
booktitle = {Conference on Computer Vision and Pattern Recognition},
volume = {83},
publisher = {},
url = {Google Scholar},
isbn = {},
doi = {},
address = {},
pages = {59-64},
year = {1983},
month = {},
abstract = {},
mypdf = {},
keywords = {},
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openid = {Beaulieu 1983}
}
[Bea1982] Hierarchical Picture Segmentation by Approximation,
Beaulieu Jean-Marie, Moris Goldberg,
Proc. Can. Commun. Energy Conf, 1982, pp. 393-396.
[URL]   [Bibtex]  
@Conference{Bea1982,
author = {Beaulieu, Jean-Marie and Goldberg, Moris},
editor = {},
title = {Hierarchical Picture Segmentation by Approximation},
booktitle = {Proc. Can. Commun. Energy Conf},
volume = {},
publisher = {},
url = {Google Scholar},
isbn = {},
doi = {},
address = {},
pages = {393-396},
year = {1982},
month = {},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 1982}
}
[Coh1980] “The Modeling and Generation of Visual Textures,”
Cohen Paul, Jean-Marie Beaulieu,
Canadian Electrical Engineering Journal, vol. 5, iss. 3, p. 5–8, 1980.
[URL]   [Bibtex]   [Abstract]  
Describes two methods of generating artificial textures, based on a second order statistical model. By choosing the correct model parameters, these methods make it possible to obtain textures with given statistical properties (granularity, homogeneity, periodicity, desired directions). Such artificial textures are a useful tool both in analyzing the stochastic structure of real images and in studying the discriminatory power of the eye.
@Article{Coh1980,
author = {Cohen, Paul and Beaulieu, Jean-Marie},
title = {The Modeling and Generation of Visual Textures},
journal = {Canadian Electrical Engineering Journal},
volume = {5},
number = {3},
pages = {5--8},
year = {1980},
month = {},
abstract = {Describes two methods of generating artificial textures, based on a second order statistical model. By choosing the correct model parameters, these methods make it possible to obtain textures with given statistical properties (granularity, homogeneity, periodicity, desired directions). Such artificial textures are a useful tool both in analyzing the stochastic structure of real images and in studying the discriminatory power of the eye.},
publisher = {},
url = {Google Scholar},
isbn = {},
doi = {},
mypdf = {},
address = {},
keywords = {},
openpdf = {},
openid = {Cohen 1980}
}
[Bea1979] Digital Picture Generation by Texture and Contour Modeling,
Beaulieu Jean-Marie, Paul Cohen, Jean-Pierre Adoul,
22nd Midwest Symposium on Circuits and Systems, Philadelphia, June 1979, pp. 344-348.
[Bibtex]  
@Conference{Bea1979,
author = {Beaulieu, Jean-Marie and Cohen, Paul and Adoul, Jean-Pierre},
editor = {},
title = {Digital Picture Generation by Texture and Contour Modeling},
booktitle = {22nd Midwest Symposium on Circuits and Systems},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {Philadelphia},
pages = {344-348},
year = {1979},
month = {June},
abstract = {},
mypdf = {},
keywords = {},
openpdf = {},
openid = {Beaulieu 1979}
}