Other

PublicationListBookJournalConference

Other Conferences

[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}
}
[Bea2003e] Classification and Segmentation of Radar Polarimetric Images,
Beaulieu Jean-Marie, Ridha Touzi,
Classification Society of North America Annual Meeting, 2003, Tallahassee, Florida, June 25-15, 2003.
[PDF]   [Slide]   [.. More]   [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 = {Tallahassee, Florida},
pages = {},
year = {2003},
month = {June 25-15},
abstract = {},
mypdf = {6},
slide = {https://BeaulieuJM.ca/slide/slideBea2003e.pdf},
keywords = {}
}
[Bea2001b] SAR Image Enhancement: Combining Image Filtering and Segmentation,
Beaulieu Jean-Marie,
The 2001 International Conference on Imaging Science, Systems, and Technology, CISST’2001, Las Vegas, NV, United States, 25-28 June, 2001, pp. 327-333.
[PDF]   [URL]   [Open]   [Slide]   [.. More]   [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 = {http://www2.ift.ulaval.ca/~beaulieu/home/paper/322CT_CISST_Beaulieu.pdf},
isbn = {},
doi = {},
address = {Las Vegas, NV, United States},
pages = {327-333},
year = {2001},
month = {25-28 June},
abstract = {},
mypdf = {7},
slide = {https://BeaulieuJM.ca/slide/slideBea2001b.pdf},
keywords = {},
openpdf = {https://pdfs.semanticscholar.org/0a0f/1f0e8e1399851573fed0973d73e4343c67a2.pdf?_ga=2.156291896.2051265828.1566578008-697872498.1566578008},
openid = {from Semantics}
}
[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.
[PDF]   [Slide]   [.. More]   [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 = {6},
slide = {https://BeaulieuJM.ca/slide/slideBea2000a.pdf},
keywords = {}
}
[Bea2000b] Détection des Arbres Individuels dans des Images de Haute Résolution,
Beaulieu Jean-Marie, Mohammed Bouzkraoui,
Vision Interface 2000, mai, 2000, pp. 311-317.
[PDF]   [Slide]   [.. More]   [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 = {7},
slide = {https://BeaulieuJM.ca/slide/slideBea2000b.pdf},
keywords = {}
}
[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.
[PDF]   [.. More]   [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 = {6},
keywords = {}
}
[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.
[.. More]   [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)},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {},
pages = {},
year = {1992},
month = {},
abstract = {},
mypdf = {5},
keywords = {}
}
[Bea1991] Programming of Application Interface and Image Access Made Simple,
Beaulieu Jean-Marie,
Canadian Conference on Electrical and Computer Engineering, Quebec, Quebec, Canada, Sept., 1991, p. 23.1.1-4.
[PDF]   [.. More]   [Bibtex]   [Abstract]  
A good user interface is an important aspect of software products. We should also consider the effort and time spent for its programming. We present a “C” language extension facility which allows the definition of menus and fill-in forms. All the flexibility of the “C” programming language is preserved while making user interfaces programming easier. Following an object-oriented approach, we have also defined a set of functions and macros for image manipulation.
@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 = {Quebec, Quebec, Canada},
pages = {23.1.1-4},
year = {1991},
month = {Sept.},
abstract = {A good user interface is an important aspect of software products. We should also consider the effort and time spent for its programming. We present a "C" language extension facility which allows the definition of menus and fill-in forms. All the flexibility of the "C" programming language is preserved while making user interfaces programming easier. Following an object-oriented approach, we have also defined a set of functions and macros for image manipulation.},
mypdf = {9},
keywords = {}
}
[Bea1990b] Hierarchical Segmentation of SAR Picture,
Beaulieu Jean-Marie,
Image’Com 90, Bordeaux, Nov., 1990, pp. 392-397.
[PDF]   [.. More]   [Bibtex]   [Abstract]  
The segmentation of SAR (Synthetic Aperture Radar) pictures is greatly complicated by the presence of coherent speckle in the image. The complex structure of the SAR pictures requires the utilization of a composite criterion for the segmentation. This paper takes advantage of a powerful hierarchical segmentation technique based upon step-wise optimization. The algorithm could easily be adapted to complex criterion. We present a two stage approach. A constant approximation criterion is first employed to yield an initial partition of the image. Then, a composite criterion is employed to continue the merging. The segment means and variances are then exploited in the step-wise criterion (segment similarity measure). Moreover, the segment shape is employed to reduce the formation of random contours. Good segmentation results are obtained, and they compare advantageously with other segmentation approaches. The algorithm produces a good separation of regions, and in the same time, yields accurate boundary location.
@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 = {The segmentation of SAR (Synthetic Aperture Radar) pictures is greatly complicated by the presence of coherent speckle in the image. The complex structure of the SAR pictures requires the utilization of a composite criterion for the segmentation. This paper takes advantage of a powerful hierarchical segmentation technique based upon step-wise optimization. The algorithm could easily be adapted to complex criterion. We present a two stage approach. A constant approximation criterion is first employed to yield an initial partition of the image. Then, a composite criterion is employed to continue the merging. The segment means and variances are then exploited in the step-wise criterion (segment similarity measure). Moreover, the segment shape is employed to reduce the formation of random contours. Good segmentation results are obtained, and they compare advantageously with other segmentation approaches. The algorithm produces a good separation of regions, and in the same time, yields accurate boundary location.},
mypdf = {7},
keywords = {}
}
[Kal1990] Segmentation of SAR Picture,
Kaliaguine Nicolas, Jean-Marie Beaulieu,
Canadian Conference on Electrical and Computer Engineering, Ottawa, Ontario, Canada, 1990, p. 69.5.1-4.
[PDF]   [.. More]   [Bibtex]   [Abstract]  
This paper presents a new and simple filter useful for the segmentation of SAR (Synthetic Apertur Radar) picture. This filter corresponds to an adiabatic transformation, It is reversible and does not produce information lose. The purpose of the filter is to transform the noise model of the image from multip1icative gaussian to an additive gaussian one which is easier to process.
@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 = {Ottawa, Ontario, Canada},
pages = {69.5.1-4},
year = {1990},
month = {},
abstract = {This paper presents a new and simple filter useful for the segmentation of SAR (Synthetic Apertur Radar) picture. This filter corresponds to an adiabatic transformation, It is reversible and does not produce information lose. The purpose of the filter is to transform the noise model of the image from multip1icative gaussian to an additive gaussian one which is easier to process.},
mypdf = {7},
keywords = {}
}
[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]   [Abstract]  
Image segmentation is a relatively new image processing procedure which breaks an image into a set of contiguous regions characterized by some criteria of homogeneity and/or continuity. Segmentation is carried out before classification, and hence allows for classification which is based on the average spectral cha.racteristics of the componente ntities. Furthermore,s uch classificationc an also benefit from shape and context information. These advantages make image segmentation a powerful tool for automated image interpretation. However, when segmentation algorithms arc applied to remotely sensed images they often yield image partitions which do not correspond well to meaningful structures in the scene. This paper presents a scenario for introducing auxiliary cartographic information into image segmentation. In particular, we have found that the introduction of partially complete cartographic infonnation, based on the more stable elements of a scene, serves as a “structuring principle” for the segmentation algorithm. The resulting partitions contain structures which correspond better with meaningful entities in the image, even in regions for which no cartographic information was introduced, and hence greatly facilitate the task of automated image interpretation. This technique is shown to be particularly useful when crop covers are small compared to the pixel size. Examples are presented where cadastral information has been used as such a structuring principle with SPOT multispectral imagery and with airborne SAR imagery of agricultural scenes
@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 = {Image segmentation is a relatively new image processing procedure which breaks an image into a set of contiguous regions characterized by some criteria of homogeneity and/or continuity. Segmentation is carried out before classification, and hence allows for classification which is based on the average spectral cha.racteristics of the componente ntities. Furthermore,s uch classificationc an also benefit from shape and context information. These advantages make image segmentation a powerful tool for automated image interpretation. However, when segmentation algorithms arc applied to remotely sensed images they often yield image partitions which do not correspond well to meaningful structures in the scene. This paper presents a scenario for introducing auxiliary cartographic information into image segmentation. In particular, we have found that the introduction of partially complete cartographic infonnation, based on the more stable elements of a scene, serves as a "structuring principle" for the segmentation algorithm. The resulting partitions contain structures which correspond better with meaningful entities in the image, even in regions for which no cartographic information was introduced, and hence greatly facilitate the task of automated image interpretation. This technique is shown to be particularly useful when crop covers are small compared to the pixel size. Examples are presented where cadastral information has been used as such a structuring principle with SPOT multispectral imagery and with airborne SAR imagery of agricultural scenes},
mypdf = {5},
keywords = {}
}
[Val1988] Quantitative Evaluation of Image Segmentation Techniques,
Velarde Cesar, Jean-Marie Beaulieu,
Canadian Conference on Electrical and Computer Engineering, Montreal, Canads, September 17-20, 1989, pp. 314-317.
[PDF]   [.. More]   [Bibtex]   [Abstract]  
The objective evaluation of picture segmentation techniques is an import ant and difficult topic. This paper presents an objective evaluation approach, based upon the comparison of the results of a given segmentation technique to either the ground truth, or to the results of another technique. Three performance criteria are defined : 1) the ability to extract the structure of the images, 2) the sensitivity to noise , and 3) the consistency between the results of two techniques. These performance criteria are measured for 4 segmentation techniques, over a set of artificial images. The results show that one of the techniques outperforms the others in retrieving the structure of the images. They also show that two techniques are less sensitive to noise than the others . Finally, we note that the four techniques produced essentially different picture partitions.
@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 = {0-9694170-0-4},
doi = {},
address = {Montreal, Canads},
pages = {314-317},
year = {1989},
month = {September 17-20},
abstract = {The objective evaluation of picture segmentation techniques is an import ant and difficult topic. This paper presents an objective evaluation approach, based upon the comparison of the results of a given segmentation technique to either the ground truth, or to the results of another technique. Three performance criteria are defined : 1) the ability to extract the structure of the images, 2) the sensitivity to noise , and 3) the consistency between the results of two techniques. These performance criteria are measured for 4 segmentation techniques, over a set of artificial images. The results show that one of the techniques outperforms the others in retrieving the structure of the images. They also show that two techniques are less sensitive to noise than the others . Finally, we note that the four techniques produced essentially different picture partitions.},
mypdf = {9},
keywords = {}
}
[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.
[PDF]   [.. More]   [Bibtex]   [Abstract]  
Piecewise functional approximation of picture is shown to be a useful tool for the segmentation of range (3D) image. A hierarchical step-wise optimization algorithm is employed to transform the global optimization problem into one of sequential optimization. The step-wise criterion then corresponds to the increase of the approximation error produced by the merge of two segments. The segmentation results of range image of polyhedra are shown with the utilization of a planar approximation model. Constraints on segment contour length and segment shape is then added to improve the results.
@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 = {Proceedings Vision Interface'88},
url = {},
isbn = {},
doi = {},
address = {Edmonton, Canada},
pages = {19-14},
year = {1988},
month = {June},
abstract = {Piecewise functional approximation of picture is shown to be a useful tool for the segmentation of range (3D) image. A hierarchical step-wise optimization algorithm is employed to transform the global optimization problem into one of sequential optimization. The step-wise criterion then corresponds to the increase of the approximation error produced by the merge of two segments. The segmentation results of range image of polyhedra are shown with the utilization of a planar approximation model. Constraints on segment contour length and segment shape is then added to improve the results.},
mypdf = {9},
keywords = {}
}
[Bea1985b] Selection of Segment Similarity Measures for Hierarchical Picture Segmentation,
Beaulieu Jean-Marie, Morris Goldberg,
Proceedings Graphics Interface 1985, Montreal, Que, Can, May 27-31, 1985, pp. 179-186.
[PDF]   [.. More]   [Bibtex]   [Abstract]  
The problem of defining appropriate segment similarity measures for picture segmentation is examined. In agglomerative hierarchical segmentation, two segments are compared and merged if found similar. The proposed Hierarchical Step-Wise Optimization (HSWO) algorithm finds and then merges the two most similar segments, 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) are applied to a Landsat picture, and the results are presented.
@Conference{Bea1985b,
author = {Beaulieu, Jean-Marie and Goldberg, Morris},
title = {Selection of Segment Similarity Measures for Hierarchical Picture Segmentation},
booktitle = {Proceedings Graphics Interface 1985},
volume = {},
publisher = {Canadian Information Processing Soc, Toronto, Ont, Can},
address = {Montreal, Que, Can},
issn = {07135424},
year = {1985},
month = {May 27-31},
pages = {179-186},
url = {},
doi = {},
abstract = {The problem of defining appropriate segment similarity measures for picture segmentation is examined. In agglomerative hierarchical segmentation, two segments are compared and merged if found similar. The proposed Hierarchical Step-Wise Optimization (HSWO) algorithm finds and then merges the two most similar segments, 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) are applied to a Landsat picture, and the results are presented.},
mypdf = {9},
keywords = {}
}
[Bea1983] Step-Wise Optimization for Hierarchical Picture Segmentation,
Beaulieu Jean-Marie, Morris Goldberg,
Conference on Computer Vision and Pattern Recognition, Washington, D.C., 1983, p. 64.
[PDF]   [.. More]   [Bibtex]   [Abstract]  
Hierarchical picture segmentations are very useful in picture analysis. We present a sequential segment merging algorithm for picture segmentation. Each iteration merges two segments which optimize a step-wise criterion. We relate picture segmentation to optimization problems. The implementation of the segmentation algorithm is examined, and results are presented and discussed.
@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 = {59},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {Washington, D.C.},
pages = {64},
year = {1983},
month = {},
abstract = {Hierarchical picture segmentations are very useful in picture analysis. We present a sequential segment merging algorithm for picture segmentation. Each iteration merges two segments which optimize a step-wise criterion. We relate picture segmentation to optimization problems. The implementation of the segmentation algorithm is examined, and results are presented and discussed.},
mypdf = {7},
keywords = {}
}
[Bea1982] Hierarchical Picture Segmentation by Approximation,
Beaulieu Jean-Marie, Morris Goldberg,
Proc. Can. Commun. Energy Conf, Montreal, Canada, 1982, pp. 393-396.
[PDF]   [.. More]   [Bibtex]   [Abstract]  
Piecewise functional approximation of picture is shown to be a useful tool for primitive and segment extraction. We present an algorithm based on a hierarchical segmentation structure. Functional approximation criteria are employed to direct the sequential merging of segments. A criteria related to the mean squared error is used to guide the merging. The algorithm yields solutions to the picture segmentation and approximation problem. Techniques described have been applied to a LANDSAT picture of an agricultural region (32 multiplied by 32 pixels).
@Conference{Bea1982,
author = {Beaulieu, Jean-Marie and Goldberg, Morris},
editor = {},
title = {Hierarchical Picture Segmentation by Approximation},
booktitle = {Proc. Can. Commun. Energy Conf},
volume = {},
publisher = {},
url = {},
isbn = {},
doi = {},
address = {Montreal, Canada},
pages = {393-396},
year = {1982},
month = {},
abstract = {Piecewise functional approximation of picture is shown to be a useful tool for primitive and segment extraction. We present an algorithm based on a hierarchical segmentation structure. Functional approximation criteria are employed to direct the sequential merging of segments. A criteria related to the mean squared error is used to guide the merging. The algorithm yields solutions to the picture segmentation and approximation problem. Techniques described have been applied to a LANDSAT picture of an agricultural region (32 multiplied by 32 pixels).},
mypdf = {7},
keywords = {}
}
[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 17-19, 1979, pp. 344-348.
[PDF]   [.. More]   [Bibtex]   [Abstract]  
This paper describes some efficient techniques for visual stochastic field generation, based on statistical models of textures and contours. Computer simulation of these techniques yields pictures which show controllable properties of granularity, clustering and symmetry depending on the specified model parameters.
@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 17-19},
abstract = {This paper describes some efficient techniques for visual stochastic field generation, based on statistical models of textures and contours. Computer simulation of these techniques yields pictures which show controllable properties of granularity, clustering and symmetry depending on the specified model parameters.},
mypdf = {7},
keywords = {}
}

website © Jean-Marie Beaulieu