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Publications of Ian Jermyn
Result of the query in the list of publications :
46 Conference articles |
19 - Riemannian Analysis of Probability Density Functions with Applications in Vision.
S. Joshi and A. Srivastava and I. H. Jermyn. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Minneapolis, USA, June 2007.
Keywords : Probability density function, Metric, Geodesic, Reparameterization.
@INPROCEEDINGS{Joshi07,
|
| author |
= |
{Joshi, S. and Srivastava, A. and Jermyn, I. H.}, |
| title |
= |
{Riemannian Analysis of Probability Density Functions with Applications in Vision}, |
| year |
= |
{2007}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, |
| address |
= |
{Minneapolis, USA}, |
| url |
= |
{http://dx.doi.org/10.1109/CVPR.2007.383188 }, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Joshi07.pdf}, |
| keyword |
= |
{Probability density function, Metric, Geodesic, Reparameterization} |
| } |
Abstract :
Applications in computer vision involve statistically analyzing an important class of constrained, non- negative functions, including probability density functions (in texture analysis), dynamic time-warping functions (in activity analysis), and re-parametrization or non-rigid registration functions (in shape analysis of curves). For this one needs to impose a Riemannian structure on the spaces formed by these functions. We propose a em spherical version of the Fisher-Rao metric that provides closed form expressions for geodesics and distances, and allows an efficient computation of statistics. We compare this metric with some previously used metrics and present an application in planar shape classification. |
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20 - 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. |
|
21 - 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} |
| } |
|
22 - 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. |
|
23 - Computing statistics from a graph representation of road networks in satellite images for indexing and retrieval.
A. Bhattacharya and I. H. Jermyn and X. Descombes and J. Zerubia. In Proc. compImage, Coimbra, Portugal, October 2006.
Keywords : Road network, Indexation, Semantic, Retrieval, Feature statistics.
@INPROCEEDINGS{bhatta_compimage06,
|
| author |
= |
{Bhattacharya, A. and Jermyn, I. H. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Computing statistics from a graph representation of road networks in satellite images for indexing and retrieval}, |
| year |
= |
{2006}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. compImage}, |
| address |
= |
{Coimbra, Portugal}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_bhatta_compimage06.pdf}, |
| keyword |
= |
{Road network, Indexation, Semantic, Retrieval, Feature statistics} |
| } |
Abstract :
Retrieval from remote sensing image archives relies on the
extraction of pertinent information from the data about the entity of interest (e.g. land cover type), and on the robustness of this extraction to nuisance variables (e.g. illumination). Most image-based characterizations are not invariant to such variables. However, other semantic entities in the image may be strongly correlated with the entity of interest and their properties can therefore be used to characterize this entity. Road networks are one example: their properties vary considerably, for example, from urban to rural areas. This paper takes the first steps towards classification (and hence retrieval) based on this idea. We study the dependence of a number of network features on the class of the image ('urban' or 'rural'). The chosen features include measures of the network density, connectedness, and `curviness'. The feature distributions of the two classes are well separated in feature space, thus providing a basis for retrieval. Classification using kernel k-means confirms this conclusion. |
|
24 - Nonlinear models for the statistics of adaptive wavelet packet coefficients of texture.
J. Aubray and I. H. Jermyn and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Florence, Italy, September 2006.
Keywords : Texture, Adaptive, Wavelet packet, Nonlinear, Bimodal, Statistics.
@INPROCEEDINGS{aubray_eusipco06,
|
| author |
= |
{Aubray, J. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Nonlinear models for the statistics of adaptive wavelet packet coefficients of texture}, |
| year |
= |
{2006}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Florence, Italy}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_aubray_eusipco06.pdf}, |
| keyword |
= |
{Texture, Adaptive, Wavelet packet, Nonlinear, Bimodal, Statistics} |
| } |
Abstract :
Probabilistic adaptive wavelet packet models of
texture pro- vide new insight into texture structure
and statistics by focus- ing the analysis on
significant structure in frequency space. In very
adapted subbands, they have revealed new bimodal
statistics, corresponding to the structure inherent to
a texture, and strong dependencies between such
bimodal sub- bands, related to phase coherence in a
texture. Existing models can capture the former but
not the latter. As a first step to- wards modelling
the joint statistics, and in order to simplify earlier
approaches, we introduce a new parametric family of
models capable of modelling both bimodal and unimodal
subbands, and of being generalized to capture the
joint statistics. We show how to compute MAP estimates
for the adaptive basis and model parameters, and apply
the models to Brodatz textures to illustrate their
performance. |
|
25 - 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. |
|
26 - Phase field models and higher-order active contours.
M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Computer Vision (ICCV), Beijing, China, October 2005.
Keywords : Active contour, Higher-order, Shape, Line networks, Road network, Phase Field.
@INPROCEEDINGS{rochery_iccv05,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Phase field models and higher-order active contours}, |
| year |
= |
{2005}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Computer Vision (ICCV)}, |
| address |
= |
{Beijing, China}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/rochery_iccv05.pdf}, |
| keyword |
= |
{Active contour, Higher-order, Shape, Line networks, Road network, Phase Field} |
| } |
Abstract :
The representation and modelling of regions is an important topic in computer vision. In this paper, we represent a region via a level set of a `phase field' function. The function is not constrained, eg to be a distance function; nevertheless, phase field energies equivalent to classical active contour energies can be defined. They represent an advantageous alternative to other methods: a linear representation space; ease of implementation (a PDE with no reinitialization); neutral initialization; greater topological freedom. We extend the basic phase field model with terms that reproduce `higher-order active contour' energies, a powerful way of including prior geometric knowledge in the active contour framework via nonlocal interactions between contour points. In addition to the above advantages, the phase field greatly simplifies the analysis and implementation of the higher-order terms. We define a phase field model that favours regions composed of thin arms meeting at junctions, combine this with image terms, and apply the model to the extraction of line networks from remote sensing images. |
|
27 - New Higher-order Active Contour Energies for Network Extraction.
M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005.
Keywords : Gap closure, Shape, Prior, Higher-order, Active contour.
@INPROCEEDINGS{rochery_icip05,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{New Higher-order Active Contour Energies for Network Extraction}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Genoa, Italy}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/rochery_icip05.pdf}, |
| keyword |
= |
{Gap closure, Shape, Prior, Higher-order, Active contour} |
| } |
Abstract :
Using the framework of higher-order active contours, we present a new quadratic em continuation energy for the extraction of line networks (e.g. road, hydrographic, vascular) in the presence of occlusions. Occlusions create gaps in the data that frequently translate to gaps in the extracted network. The new energy penalizes earby opposing extremities of the network, and thus favours the closure of the gaps created by occlusions. Nearby opposing extremities are identified using a
sophisticated interaction between pairs of points on the contour. This new model allows the extraction of fully connected networks, even though occlusions violate common assumptions about the homogeneity of the
interior, and high contrast with the exterior, of the network. We present experimental results on real aerial images that demonstrate the effectiveness of the new model for network extraction tasks. |
|
28 - Texture-adaptive mother wavelet selection for texture analysis.
G.C.K. Abhayaratne and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005.
Keywords : Texture, Wavelet packet, Adaptive, Mother.
@INPROCEEDINGS{abhayaratne_icip05,
|
| author |
= |
{Abhayaratne, G.C.K. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Texture-adaptive mother wavelet selection for texture analysis}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Genoa, Italy}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Abhayaratne05icip.pdf}, |
| keyword |
= |
{Texture, Wavelet packet, Adaptive, Mother} |
| } |
Abstract :
Classification results obtained using wavelet-based texture analysis techniques vary with the choice of mother wavelet used in the methodology. We discuss the use of mother wavelet filters as parameters in a probabilistic approach to texture analysis based on adaptive biorthogonal wavelet packet bases. The optimal choice for the mother wavelet filters is estimated from the data, in addition to the other model parameters. The model is applied to the classification of single texture images and mosaics of Brodatz textures, the results showing improvement over the performance of standard wavelets for a given filter length. |
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