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Publications of Ian Jermyn
Result of the query in the list of publications :
46 Conference articles |
39 - Adaptive Probabilistic Models of Wavelet Packets for the Analysis and Segmentation of Textured Remote Sensing Images.
K. Brady and I. H. Jermyn and J. Zerubia. In Proc. British Machine Vision Conference (BMVC), Norwich, U. K., September 2003.
Keywords : probabilistic, Adaptive, wavelet, Texture.
@INPROCEEDINGS{Brady03a,
|
| author |
= |
{Brady, K. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Adaptive Probabilistic Models of Wavelet Packets for the Analysis and Segmentation of Textured Remote Sensing Images}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. British Machine Vision Conference (BMVC)}, |
| address |
= |
{Norwich, U. K.}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Brady03bmvc.pdf}, |
| keyword |
= |
{probabilistic, Adaptive, wavelet, Texture} |
| } |
Abstract :
Remote sensing imagery plays an important role in many elds. It has
become an invaluable tool for diverse applications ranging from cartography
to ecosystem management. In many of the images processed in these types
of applications, semantic entities in the scene are correlated with textures
in the image. In this paper, we propose a new method of analysing such
textures based on adaptive probabilistic models of wavelet packets. Our approach
adapts to the principal periodicities present in the textures, and can
capture long-range correlations while preserving the independence of the
wavelet packet coefcients. This technique has been applied to several remote
sensing images, the results of which are presented. |
|
40 - Gaussian Mixture Models of Texture and Colour for Image Database Retrieval.
H. Permuter and J.M. Francos and I. H. Jermyn. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hong Kong, April 2003.
Keywords : Texture, Gaussian mixture, Classification, Aerial images.
@INPROCEEDINGS{Permuter03,
|
| author |
= |
{Permuter, H. and Francos, J.M. and Jermyn, I. H.}, |
| title |
= |
{Gaussian Mixture Models of Texture and Colour for Image Database Retrieval}, |
| year |
= |
{2003}, |
| month |
= |
{April}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Hong Kong}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Permuter03icassp.pdf}, |
| keyword |
= |
{Texture, Gaussian mixture, Classification, Aerial images} |
| } |
Abstract :
We introduce Gaussian mixture models of ‘structure’ and
colour features in order to classify coloured textures in images,
with a view to the retrieval of textured colour images
from databases. Classifications are performed separately
using structure and colour and then combined using
a confidence criterion. We apply the models to the VisTex
database and to the classification of man-made and natural
areas in aerial images. We compare these models with others
in the literature, and show an overall improvement in
performance. |
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41 - Unsupervised Image Segmentation via Markov Trees and Complex Wavelets.
C. Shaffrey and N. Kingsbury and I. H. Jermyn. In Proc. IEEE International Conference on Image Processing (ICIP), Rochester, USA, September 2002.
Keywords : Segmentation, Hidden Markov Model, Texture, Colour.
@INPROCEEDINGS{ijking,
|
| author |
= |
{Shaffrey, C. and Kingsbury, N. and Jermyn, I. H.}, |
| title |
= |
{Unsupervised Image Segmentation via Markov Trees and Complex Wavelets}, |
| year |
= |
{2002}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Rochester, USA}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Shaffrey02icip.pdf}, |
| keyword |
= |
{Segmentation, Hidden Markov Model, Texture, Colour} |
| } |
Abstract :
The goal in image segmentation is to label pixels in an image based
on the properties of each pixel and its surrounding region. Recently
Content-Based Image Retrieval (CBIR) has emerged as an
application area in which retrieval is attempted by trying to gain
unsupervised access to the image semantics directly rather than
via manual annotation. To this end, we present an unsupervised
segmentation technique in which colour and texture models are
learned from the image prior to segmentation, and whose output
(including the models) may subsequently be used as a content
descriptor in a CBIR system. These models are obtained in a
multiresolution setting in which Hidden Markov Trees (HMT) are
used to model the key statistical properties exhibited by complex
wavelet and scaling function coefficients. The unsupervised Mean
Shift Iteration (MSI) procedure is used to determine a number of
image regions which are then used to train the models for each
segmentation class. |
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42 - Psychovisual Evaluation of Image Segmentation Algorithms.
C. Shaffrey and I. H. Jermyn and N. Kingsbury. In Proc. Advanced Concepts for Intelligent Vision Systems, Ghent, Belgique, September 2002.
@INPROCEEDINGS{kingij,
|
| author |
= |
{Shaffrey, C. and Jermyn, I. H. and Kingsbury, N.}, |
| title |
= |
{Psychovisual Evaluation of Image Segmentation Algorithms}, |
| year |
= |
{2002}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. Advanced Concepts for Intelligent Vision Systems}, |
| address |
= |
{Ghent, Belgique}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/acivs2002_final.pdf}, |
| keyword |
= |
{} |
| } |
|
43 - Evaluation Methodologies for Image Retrieval Systems.
I. H. Jermyn and C. Shaffrey and N. Kingsbury. In Proc. Advanced Concepts for Intelligent Vision Systems, Ghent, Belgique, September 2002.
@INPROCEEDINGS{shaffreyij,
|
| author |
= |
{Jermyn, I. H. and Shaffrey, C. and Kingsbury, N.}, |
| title |
= |
{Evaluation Methodologies for Image Retrieval Systems}, |
| year |
= |
{2002}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. Advanced Concepts for Intelligent Vision Systems}, |
| address |
= |
{Ghent, Belgique}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/acivs2002final.pdf}, |
| keyword |
= |
{} |
| } |
|
44 - Region extraction from multiple images.
H. Ishikawa and I. H. Jermyn. In Proc. IEEE International Conference on Computer Vision (ICCV), Vancouver, Canada, July 2001.
Keywords : Stereo, Motion, global, optimum, Graph, Cycle.
@INPROCEEDINGS{IJ01a,
|
| author |
= |
{Ishikawa, H. and Jermyn, I. H.}, |
| title |
= |
{Region extraction from multiple images}, |
| year |
= |
{2001}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. IEEE International Conference on Computer Vision (ICCV)}, |
| address |
= |
{Vancouver, Canada}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Jermyn01iccv.pdf}, |
| keyword |
= |
{Stereo, Motion, global, optimum, Graph, Cycle} |
| } |
Abstract :
We present a method for region identification in multiple
images. A set of regions in different images and the
correspondences on their boundaries can be thought of as
a boundary in the multi-dimensional space formed by the
product of the individual image domains. We minimize an
energy functional on the space of such boundaries, thereby
identifying simultaneously both the optimal regions in each
image and the optimal correspondences on their boundaries.
We use a ratio form for the energy functional, thus
enabling the global minimization of the energy functional
using a polynomial time graph algorithm, among other desirable
properties. We choose a simple form for this energy
that favours boundaries that lie on high intensity gradients
in each image, while encouraging correspondences between
boundaries in different images that match intensity values.
The latter tendency is weighted by a novel heuristic energy
that encourages the boundaries to lie on disparity or optical
flow discontinuities, although no dense optical flow or
disparity map is computed. |
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45 - Judging whether multiple silhouettes can come from the same object.
D. Jacobs and P. Belhumeur and I. H. Jermyn. In Int. Workshop on Visual Form, Springer-Verlag Lecture Notes in Computer Science 2059, Capri, Italie, May 2001.
@INPROCEEDINGS{IJ01b,
|
| author |
= |
{Jacobs, D. and Belhumeur, P. and Jermyn, I. H.}, |
| title |
= |
{Judging whether multiple silhouettes can come from the same object}, |
| year |
= |
{2001}, |
| month |
= |
{May}, |
| booktitle |
= |
{Int. Workshop on Visual Form, Springer-Verlag Lecture Notes in Computer Science 2059}, |
| address |
= |
{Capri, Italie}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Jacobs01iwvf.pdf}, |
| keyword |
= |
{} |
| } |
Abstract :
We consider the problem of recognizing an object from its
silhouette. We focus on the case in which the camera translates, and
rotates about a known axis parallel to the image, such as when a mo-
bile robot explores an environment. In this case we present an algorithm
for determining whether a new silhouette could come from the same ob-
ject that produced two previously seen silhouettes. In a basic case, when
cross-sections of each silhouette are single line segments, we can check
for consistency between three silhouettes using linear programming. This
provides the basis for methods that handle more complex cases. We show
many experiments that demonstrate the performance of these methods
when there is noise, some deviation from the assumptions of the algo-
rithms, and partial occlusion. Previous work has addressed the problem
of precisely reconstructing an object using many silhouettes taken under
controlled conditions. Our work shows that recognition can be performed
without complete reconstruction, so that a small number of images can
be used, with viewpoints that are only partly constrained. |
|
46 - Globally optimal regions and boundaries.
I. H. Jermyn and H. Ishikawa. In Proc. IEEE International Conference on Computer Vision (ICCV), 1999.
Keywords : global, optimum, Graph, Cycle, Ratio, Segmentation.
Copyright :
@INPROCEEDINGS{Jermyn99iccv,
|
| author |
= |
{Jermyn, I. H. and Ishikawa, H.}, |
| title |
= |
{Globally optimal regions and boundaries}, |
| year |
= |
{1999}, |
| booktitle |
= |
{Proc. IEEE International Conference on Computer Vision (ICCV)}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Jermyn99iccv.pdf}, |
| keyword |
= |
{global, optimum, Graph, Cycle, Ratio, Segmentation} |
| } |
Abstract :
We propose a new form of energy functional for the segmentation
of regions in images, and an efficient method for
finding its global optima. The energy can have contributions
from both the region and its boundary, thus combining
the best features of region- and boundary-based approaches
to segmentation. By transforming the region energy
into a boundary energy, we can treat both contributions
on an equal footing, and solve the global optimization
problem as a minimum mean weight cycle problem on
a directed graph. The simple, polynomial-time algorithm
requires no initialization and is highly parallelizable. |
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9 Technical and Research Reports |
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. Research Report 6026, INRIA, France, November 2006.
Keywords : Tree Crown Extraction, Aerial images, Higher-order, Active contour, Gas of circles, Shape.
@TECHREPORT{Horvath05,
|
| 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 |
= |
{2006}, |
| month |
= |
{November}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6026}, |
| address |
= |
{France}, |
| url |
= |
{http://hal.inria.fr/inria-00115631}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_Horvath05.pdf}, |
| keyword |
= |
{Tree Crown Extraction, Aerial images, Higher-order, Active contour, Gas of circles, Shape} |
| } |
Abstract :
Many image processing problems involve identifying the region in the image domain occupied by a given entity in the scene. Automatic solution of these problems requires models that incorporate significant prior knowledge about the shape of the region. Many methods for including such knowledge run into difficulties when the topology of the region is unknown a priori, for example when the entity is composed of an unknown number of similar objects. Higher-order active contours (HOACs) represent one method for the modelling of non-trivial prior knowledge about shape without necessarily constraining region topology, via the inclusion of non-local interactions between region boundary points in the energy defining the model. The case of an unknown number of circular objects arises in a number of domains, \eg medical, biological, nanotechnological, and remote sensing imagery. Regions composed of an a priori unknown number of circles may be referred to as a `gas of circles'. In this report, we present a HOAC model of a `gas of circles'. In order to guarantee stable circles, we conduct a stability analysis via a functional Taylor expansion of the HOAC energy around a circular shape. This analysis fixes one of the model parameters in terms of the others and constrains the rest. In conjunction with a suitable likelihood energy, we apply the model to the extraction of tree crowns from aerial imagery, and show that the new model outperforms other techniques. |
|
2 - Higher-Order Active Contour Energies for Gap Closure.
M. Rochery and I. H. Jermyn and J. Zerubia. Research Report 5717, INRIA, France, October 2005.
Keywords : Road network, Continuity, Gap closure, Higher-order, Active contour, Shape.
@TECHREPORT{RR_5717,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Higher-Order Active Contour Energies for Gap Closure}, |
| year |
= |
{2005}, |
| month |
= |
{October}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5717}, |
| address |
= |
{France}, |
| url |
= |
{http://hal.inria.fr/inria-00070300/fr/}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/70300/filename/RR-5717.pdf}, |
| ps |
= |
{http://hal.inria.fr/docs/00/07/03/00/PS/RR-5717.ps}, |
| keyword |
= |
{Road network, Continuity, Gap closure, Higher-order, Active contour, Shape} |
| } |
Résumé :
L'un des principaux problèmes lors de l'extraction de réseaux
linéiques dans des images, et en particulier l'extraction de réseaux
routiers dans des images de télédétection, est l'existence d'interruptions
dans les données, causées, par exemple, par des occultations. Ces
interruptions peuvent mener à des trous dans le réseau extrait qui
n'existent pas dans le réseau réel. Dans ce rapport, nous décrivons une
énergie de contour actif d'ordre supérieur qui, en plus de favoriser les
régions composées de bras fins et connectés entre eux, inclut un terme d'a
priori qui pénalise les configurations du réseau où des extremités proches
et se faisant face apparaissent. L'apparition dans le réseau extrait de ces
configurations est donc moins probable. Si des extremités proches et se
faisant face apparaissent pendant l'évolution par descente de gradient
utilisée pour minimiser l'énergie, le nouveau terme dans l'énergie crée une
attraction entre ces extremités, qui se rapprochent donc l'une de l'autre
et se rejoignent, fermant ainsi le trou entre elles. Pour minimiser
l'énergie, nous développons des techniques spécifiques pour traiter les
derivées d'ordre élevé qui apparaissent dans l'équation de descente de
gradient. Nous présentons des résultats d'extraction automatique de réseaux
routiers à partir d'images de télédétection, montrant ainsi la capacité du
modèle à surmonter les interruptions. |
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 report, we describe a higher-order active
contour energy that in addition to favouring network-like regions composed
of thin arms joining at junctions, also includes a prior term that
penalizes network configurations containing `nearby opposing extremities',
and thereby makes their appearance in the extracted network less likely. If
nearby opposing extremities form during the gradient descent evolution used
to minimize the energy, the new energy term causes the extremities to
attract one another, and hence to move towards one another and join, thus
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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