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Publications about Higher-order
Result of the query in the list of publications :
3 Articles |
1 - A higher-order active contour model of a ‘gas of circles' and its application to tree crown extraction. P. Horvath and I. H. Jermyn and Z. Kato and J. Zerubia. Pattern Recognition, 42(5): pages 699-709, May 2009. Keywords : Shape, Higher-order, Active contour, Gas of circles, Tree Crown Extraction, Bayesian.
@ARTICLE{Horvath09,
|
author |
= |
{Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, |
title |
= |
{A higher-order active contour model of a ‘gas of circles' and its application to tree crown extraction}, |
year |
= |
{2009}, |
month |
= |
{May}, |
journal |
= |
{Pattern Recognition}, |
volume |
= |
{42}, |
number |
= |
{5}, |
pages |
= |
{699-709}, |
url |
= |
{http://dx.doi.org/10.1016/j.patcog.2008.09.008}, |
pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Horvathetal09.pdf}, |
keyword |
= |
{Shape, Higher-order, Active contour, Gas of circles, Tree Crown Extraction, Bayesian} |
} |
Abstract :
We present a model of a ‘gas of circles’: regions in the image domain composed of a unknown
number of circles of approximately the same radius. The model has applications
to medical, biological, nanotechnological, and remote sensing imaging. The model is constructed
using higher-order active contours (HOACs) in order to include non-trivial prior
knowledge about region shape without constraining topology. The main theoretical contribution
is an analysis of the local minima of the HOAC energy that allows us to guarantee
stable circles, fix one of the model parameters, and constrain the rest. We apply the model
to tree crown extraction from aerial images of plantations. Numerical experiments both
confirm the theoretical analysis and show the empirical importance of the prior shape information. |
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2 - Higher-Order Active Contour Energies for Gap Closure. M. Rochery and I. H. Jermyn and J. Zerubia. Journal of Mathematical Imaging and Vision, 29(1): pages 1-20, September 2007. Keywords : Gap closure, Higher-order, Active contour, Shape, Prior, Road network.
@ARTICLE{Rochery07,
|
author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
title |
= |
{Higher-Order Active Contour Energies for Gap Closure}, |
year |
= |
{2007}, |
month |
= |
{September}, |
journal |
= |
{Journal of Mathematical Imaging and Vision}, |
volume |
= |
{29}, |
number |
= |
{1}, |
pages |
= |
{1-20}, |
url |
= |
{http://dx.doi.org/10.1007/s10851-007-0021-x}, |
pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Rochery07.pdf}, |
keyword |
= |
{Gap closure, Higher-order, Active contour, Shape, Prior, Road network} |
} |
Abstract :
One of the main difficulties in extracting line networks from images, and in particular road networks from remote sensing images, is the existence of interruptions in the data caused, for example, by occlusions. These can lead to gaps in the extracted network that do not correspond to gaps in the real network. In this paper, we describe a higher-order active contour energy that in addition to favouring network-like regions, includes a prior term penalizing networks containing ‘nearby opposing extremities’, thereby making gaps in the extracted network less likely. The new energy term causes such extremities to attract one another during gradient descent. They thus move towards one another and join, closing the gap. To minimize the energy, we develop specific techniques to handle the high-order derivatives that appear in the gradient descent equation. We present the results of automatic extraction of networks from real remote-sensing images, showing the ability of the model to overcome interruptions. |
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3 - Higher Order Active Contours. M. Rochery and I. H. Jermyn and J. Zerubia. International Journal of Computer Vision, 69(1): pages 27--42, August 2006. Keywords : Active contour, Shape, Higher-order, Prior, Road network.
@ARTICLE{mr_ijcv_06,
|
author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
title |
= |
{Higher Order Active Contours}, |
year |
= |
{2006}, |
month |
= |
{August}, |
journal |
= |
{International Journal of Computer Vision}, |
volume |
= |
{69}, |
number |
= |
{1}, |
pages |
= |
{27--42}, |
url |
= |
{http://dx.doi.org/10.1007/s11263-006-6851-y}, |
pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_mr_ijcv_06.pdf}, |
keyword |
= |
{Active contour, Shape, Higher-order, Prior, Road network} |
} |
Abstract :
We introduce a new class of active contour models that
hold great promise for region and shape modelling, and
we apply a special case of these models to the
extraction of road networks from satellite and aerial
imagery. The new models are arbitrary polynomial
functionals on the space of boundaries, and thus
greatly generalize the linear functionals used in
classical contour energies. While classical energies
are expressed as single integrals over the contour,
the new energies incorporate multiple integrals, and
thus describe long-range interactions between
different sets of contour points. As prior terms, they
describe families of contours that share complex
geometric properties, without making reference to any
particular shape, and they require no pose estimation.
As likelihood terms, they can describe multi-point
interactions between the contour and the data. To
optimize the energies, we use a level set approach.
The forces derived from the new energies are non-local
however, thus necessitating an extension of standard
level set methods. Networks are a shape family of
great importance in a number of applications,
including remote sensing imagery. To model them, we
make a particular choice of prior quadratic energy
that describes reticulated structures, and augment it
with a likelihood term that couples the data at pairs
of contour points to their joint geometry. Promising
experimental results are shown on real images. |
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PhD Thesis and Habilitation |
1 - Contours actifs d'ordre supérieur et leur application à la détection de linéiques dans des images de télédétection. M. Rochery. PhD Thesis, Universite de Nice Sophia Antipolis, Sophia Antipolis, September 2005. Keywords : Active contour, Higher-order, Phase Field, Line networks, Road network.
@PHDTHESIS{rochery_these,
|
author |
= |
{Rochery, M.}, |
title |
= |
{Contours actifs d'ordre supérieur et leur application à la détection de linéiques dans des images de télédétection}, |
year |
= |
{2005}, |
month |
= |
{September}, |
school |
= |
{Universite de Nice Sophia Antipolis}, |
address |
= |
{Sophia Antipolis}, |
pdf |
= |
{http://hal.inria.fr/docs/00/04/86/28/PDF/tel-00010631.pdf}, |
keyword |
= |
{Active contour, Higher-order, Phase Field, Line networks, Road network} |
} |
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10 Conference articles |
1 - A Phase Field Model Incorporating Generic and Specific Prior Knowledge Applied to Road Network Extraction from VHR Satellite Images. T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia and B. Hu. In Proc. British Machine Vision Conference (BMVC), Warwick, UK, September 2007. Keywords : Road network, Very high resolution, Higher-order, Active contour, Shape, Prior.
@INPROCEEDINGS{Peng07a,
|
author |
= |
{Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J. and Hu, B.}, |
title |
= |
{A Phase Field Model Incorporating Generic and Specific Prior Knowledge Applied to Road Network Extraction from VHR Satellite Images}, |
year |
= |
{2007}, |
month |
= |
{September}, |
booktitle |
= |
{Proc. British Machine Vision Conference (BMVC)}, |
address |
= |
{Warwick, UK}, |
pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Peng07a.pdf}, |
keyword |
= |
{Road network, Very high resolution, Higher-order, Active contour, Shape, Prior} |
} |
Abstract :
We address the problem of updating road maps in dense urban areas by extracting the main road network from a very high resolution (VHR) satellite image. Our model of the region occupied by the road network in the image is innovative. It incorporates three different types of prior geometric knowledge: generic boundary smoothness constraints, equivalent to a standard active contour prior; knowledge of the geometric properties of road networks (i.e. that they occupy regions composed of long, low-curvature segments joined at junctions), equivalent to a higher-order active contour prior; and knowledge of the road network at an earlier date derived from GIS data, similar to other ‘shape priors’ in the literature. In addition, we represent the road network region as a ‘phase field’, which offers a number of important advantages over other region modelling frameworks. All three types of prior knowledge prove important for overcoming the complexity of geometric ‘noise’ in VHR images. Promising results and a comparison with several other techniques demonstrate the effectiveness of our approach. |
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2 - A Multispectral Data Model for Higher-Order Active Contours and its Application to Tree Crown Extraction. P. Horvath. In Proc. Advanced Concepts for Intelligent Vision Systems, Delft, Netherlands, August 2007. Keywords : Higher-order, Tree Crown Extraction, Colour.
@INPROCEEDINGS{Horvath07c,
|
author |
= |
{Horvath, P.}, |
title |
= |
{A Multispectral Data Model for Higher-Order Active Contours and its Application to Tree Crown Extraction}, |
year |
= |
{2007}, |
month |
= |
{August}, |
booktitle |
= |
{Proc. Advanced Concepts for Intelligent Vision Systems}, |
address |
= |
{Delft, Netherlands}, |
pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Horvath07c.pdf}, |
keyword |
= |
{Higher-order, Tree Crown Extraction, Colour} |
} |
Abstract :
Forestry management makes great use of statistics concerning the
individual trees making up a forest, but the acquisition of this
information is expensive. Image processing can potentially both
reduce this cost and improve the statistics. The key problem is the
delineation of tree crowns in aerial images. The automatic solution
of this problem requires considerable prior information to be built
into the image and region models. Our previous work has focused on
including shape information in the region model; in this paper we
examine the image model. The aerial images involved have three
bands. We study the statistics of these bands, and construct both
multispectral and single band image models. We combine these with a
higher-order active contour model of a `gas of circles' in order to
include prior shape information about the region occupied by the
tree crowns in the image domain. We compare the results produced by
these models on real aerial images and conclude that multiple bands
improves the quality of the segmentation. The model has many other
potential applications, e.g. to nano-technology, microbiology,
physics, and medical imaging.
|
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3 - Urban road extraction from VHR images using a multiscale image model and a phase field model of network geometry. T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia. In Proc. Urban, Paris, France, April 2007. Keywords : Road network, Very high resolution, Multiscale, Higher-order, Active contour, Shape.
@INPROCEEDINGS{Peng07_urban,
|
author |
= |
{Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, |
title |
= |
{Urban road extraction from VHR images using a multiscale image model and a phase field model of network geometry}, |
year |
= |
{2007}, |
month |
= |
{April}, |
booktitle |
= |
{Proc. Urban}, |
address |
= |
{Paris, France}, |
pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Peng07urban.pdf}, |
keyword |
= |
{Road network, Very high resolution, Multiscale, Higher-order, Active contour, Shape} |
} |
Abstract :
This paper addresses the problem of automatically
extracting the main road network in a dense urban area from
a very high resolution optical satellite image using a variational
approach. The model energy has two parts: a phase field higherorder
active contour energy that describes our prior knowledge
of road network geometry, i.e. that it is composed of elongated
structures with roughly parallel borders that meet at junctions;
and a multi-scale statistical image model describing the image
we expect to see given a road network. By minimizing the model
energy, an estimate of the road network is obtained. Promising
results on 0.6m QuickBird Panchromatic images are presented,
and future improvements to the models are outlined. |
|
4 - Circular object segmentation using higher-order active contours. P. Horvath and I. H. Jermyn and Z. Kato and J. Zerubia. In In Proc. Conference of the Hungarian Association for Image Analysis and Pattern Recognition (KEPAF'07), Debrecen, Hungary, January 2007. Note : In Hungarian Keywords : Higher-order, Tree Crown Extraction, Shape.
@INPROCEEDINGS{Horvath07a,
|
author |
= |
{Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, |
title |
= |
{Circular object segmentation using higher-order active contours}, |
year |
= |
{2007}, |
month |
= |
{January}, |
booktitle |
= |
{In Proc. Conference of the Hungarian Association for Image Analysis and Pattern Recognition (KEPAF'07)}, |
address |
= |
{Debrecen, Hungary}, |
note |
= |
{In Hungarian}, |
pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Horvath07a.pdf}, |
keyword |
= |
{Higher-order, Tree Crown Extraction, Shape} |
} |
|
5 - An improved 'gas of circles' higher-order active contour model and its application to tree crown extraction. P. Horvath and I. H. Jermyn and Z. Kato and J. Zerubia. In Proc. Indian Conference on Computer Vision, Graphics, and Image Processing (ICVGIP), Madurai, India, December 2006. Keywords : Tree Crown Extraction, Aerial images, Higher-order, Active contour, Gas of circles, Shape.
@INPROCEEDINGS{Horvath06_icvgip,
|
author |
= |
{Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, |
title |
= |
{An improved 'gas of circles' higher-order active contour model and its application to tree crown extraction}, |
year |
= |
{2006}, |
month |
= |
{December}, |
booktitle |
= |
{Proc. Indian Conference on Computer Vision, Graphics, and Image Processing (ICVGIP)}, |
address |
= |
{Madurai, India}, |
url |
= |
{http://dx.doi.org/10.1007/11949619_14}, |
pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_Horvath06_icvgip.pdf}, |
keyword |
= |
{Tree Crown Extraction, Aerial images, Higher-order, Active contour, Gas of circles, Shape} |
} |
Abstract :
A central task in image processing is to find the
region in the image corresponding to an entity. In a
number of problems, the region takes the form of a
collection of circles, eg tree crowns in remote
sensing imagery; cells in biological and medical
imagery. In~citeHorvath06b, a model of such regions,
the `gas of circles' model, was developed based on
higher-order active contours, a recently developed
framework for the inclusion of prior knowledge in
active contour energies. However, the model suffers
from a defect. In~citeHorvath06b, the model
parameters were adjusted so that the circles were local
energy minima. Gradient descent can become stuck in
these minima, producing phantom circles even with no
supporting data. We solve this problem by calculating,
via a Taylor expansion of the energy, parameter values
that make circles into energy inflection points rather
than minima. As a bonus, the constraint halves the
number of model parameters, and severely constrains one
of the two that remain, a major advantage for an
energy-based model. We use the model for tree crown
extraction from aerial images. Experiments show that
despite the lack of parametric freedom, the new model
performs better than the old, and much better than a
classical active contour. |
|
6 - A Higher-Order Active Contour Model for Tree Detection. P. Horvath and I. H. Jermyn and Z. Kato and J. Zerubia. In Proc. International Conference on Pattern Recognition (ICPR), Hong Kong, August 2006. Keywords : Active contour, Gas of circles, Higher-order, Shape, Prior, Tree Crown Extraction.
@INPROCEEDINGS{horvath_icpr06,
|
author |
= |
{Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, |
title |
= |
{A Higher-Order Active Contour Model for Tree Detection}, |
year |
= |
{2006}, |
month |
= |
{August}, |
booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
address |
= |
{Hong Kong}, |
pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_horvath_icpr06.pdf}, |
keyword |
= |
{Active contour, Gas of circles, Higher-order, Shape, Prior, Tree Crown Extraction} |
} |
Abstract :
We present a model of a ‘gas of circles’, the ensemble
of regions in the image domain consisting of an
unknown number of circles with approximately fixed
radius and short range repulsive interactions, and
apply it to the extraction of tree crowns from aerial
images. The method uses the re- cently introduced
‘higher order active contours’ (HOACs), which
incorporate long-range interactions between contour
points, and thereby include prior geometric
information without using a template shape. This makes
them ideal when looking for multiple instances of an
entity in an image. We study an existing HOAC model
for networks, and show via a stability calculation
that circles stable to perturbations are possible
for constrained parameter sets. Combining this prior
energy with a data term, we show results on aerial
imagery that demonstrate the effectiveness of the
method and the need for prior geometric knowledge. The
model has many other potential applications. |
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