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Publications de Ian Jermyn
Résultat de la recherche dans la liste des publications :
46 Articles de conférence |
9 - An extended phase field higher-order active contour model for networks and its application to road network extraction from VHR satellite images.
T. Peng et I. H. Jermyn et V. Prinet et J. Zerubia. Dans Proc. European Conference on Computer Vision (ECCV), Marseille, France, octobre 2008.
Mots-clés : Dense urban area, Champ de Phase, Reseaux routiers, Methodes variationnelles, Very high resolution.
Copyright :
@INPROCEEDINGS{Peng08c,
|
| author |
= |
{Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, |
| title |
= |
{An extended phase field higher-order active contour model for networks and its application to road network extraction from VHR satellite images}, |
| year |
= |
{2008}, |
| month |
= |
{octobre}, |
| booktitle |
= |
{Proc. European Conference on Computer Vision (ECCV)}, |
| address |
= |
{Marseille, France}, |
| pdf |
= |
{http://link.springer.com/chapter/10.1007%2F978-3-540-88690-7_38}, |
| keyword |
= |
{Dense urban area, Champ de Phase, Reseaux routiers, Methodes variationnelles, Very high resolution} |
| } |
Abstract :
This paper addresses the segmentation from an image of entities that have the form of a 'network', i.e. the region in the image corresponding to the entity is composed of branches joining together at junctions, e.g. road or vascular networks. We present a new phase field higher-order active contour (HOAC) prior model for network regions, and apply it to the segmentation of road networks from very high resolution satellite images. This is a hard problem for two reasons. First, the images are complex, with much 'noise' in the road region due to cars, road markings, etc., while the background is very varied, containing many features that are locally similar to roads. Second, network regions are complex to model, because they may have arbitrary topology. In particular, we address a severe limitation of a previous model in which network branch width was constrained to be similar to maximum network branch radius of curvature, thereby providing a poor model of networks with straight narrow branches or highly curved, wide branches. To solve this problem, we propose a new HOAC prior energy term, and reformulate it as a nonlocal phase field energy. We analyse the stability of the new model, and find that in addition to solving the above problem by separating the interactions between points on the same and opposite sides of a network branch, the new model permits the modelling of two widths
simultaneously. The analysis also fixes some of the model parameters in terms of network width(s). After adding a likelihood energy, we use the model to extract the road network quasi-automatically from pieces of a QuickBird image, and compare the results to other models in the literature. The results demonstrate the superiority of the new model, the importance of strong prior knowledge in general, and of the new term in particular. |
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10 - Indexing of mid-resolution satellite images with structural attributes.
A. Bhattacharya et M. Roux et H. Maitre et I. H. Jermyn et X. Descombes et J. Zerubia. Dans The International Society for Photogrammetry and Remote Sensing, Beijing, China, juillet 2008.
Mots-clés : Landscape, Segmentation, Features, Extraction, Classification, Modelling.
@INPROCEEDINGS{Bhattacharya08,
|
| author |
= |
{Bhattacharya, A. and Roux, M. and Maitre, H. and Jermyn, I. H. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Indexing of mid-resolution satellite images with structural attributes}, |
| year |
= |
{2008}, |
| month |
= |
{juillet}, |
| booktitle |
= |
{The International Society for Photogrammetry and Remote Sensing}, |
| address |
= |
{Beijing, China}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Bhattacharya08isprs.pdf}, |
| keyword |
= |
{Landscape, Segmentation, Features, Extraction, Classification, Modelling} |
| } |
Abstract :
Indexing and retrieval of satellite images relies on the extraction of appropriate information from the data about the entity of interest
(e.g. land cover type) and on the robustness of this extraction to nuisance variables. Entities in an image may be strongly correlated
with each other and can therefore be used to characterize geographical environments on the Earth’s surface.
The properties of road networks vary considerably from one geographical environment to another. The networks pertaining in a
satellite image can therefore be used to classify and retrieve such environments. In the work presented in this paper we have defined
7 such classes. These classes can be categorized as follows: 2 urban classes consisting of “Urban USA” and “Urban Europe”; 3
rural classes consisting of “Villages”, “Mountains” and “Fields”; an “Airports” class and a “Common” class (this can be considered
as a rejection class). These classes were then classified with the aid of geometrical and topological features computed from the road
networks occurring in them. In our work we have used two extraction methods simultaneously on an image to extract the road networks
pertaining in it. A set of 16 network features were computed from one extraction method and were categorized into 6 groups as follows:
6 measures of ‘density’, 4 measures of ‘curviness’, 2 measures of ‘homogeneity’, 1 measure of ‘length’, 2 measures of ‘distribution’
and 1 measure of ‘entropy’.
Due to certain limitations of these extraction methods there was a relative failure of network extraction in certain urban regions con-
taining narrow and dense road structures. This loss of information was circumvented by segmenting the urban regions and computing
a second set of geometrical and topological features from them. A set of 4 urban region features were computed and were categorized
into 3 groups as follows: 2 measures of ‘density’, 1 measure of ‘labels’ and 1 measure of ‘compactness’.
The 500 images (each of size 512x512 pixels) forming our database were selected from SPOT5 scenes with 5m resolution. From each
image a set of geometrical and topological features were computed from the road networks and urban regions. These features were
then used to classify the pre-defined geographical classes. Feature selection was done to avoid the burden of feature dimensionality
and increase the classification performance. A set of 20 features was selected from 36 features by Fisher Linear Discriminant (FLD)
analysis which gave the least classification error with an one-vs-rest linear Support Vector Machine (SVM).
The impact of spatial resolution and size of images on the feature set have been explored in this work. We took a closer look at the effect
of spatial resolution and size of images on the discriminative power of the feature set to classify the images belonging to the pre-defined
geographical classes. Tests were performed with feature selection by FLD and one-vs-rest linear SVM classification on a database with
images of 10m resolution. Another test was performed with feature selection by FLD and one-vs-rest linear SVM classification on a
database with 5m resolution images (each of size 256x256 pixels).
With the above mentioned approaches, we developed a novel method to classify large satellite images acquired by SPOT5 satellite (5m
resolution) with patches of images each of size 512x512 pixels extracted from them. There has been a large amount of work dedicated
to the classification of large satellite images at pixel level rather than considering image patches of different sizes. Classification of
image patches of different sizes from a large satellite image is a novel idea in the sense that the patches considered contain significant
coverage of a particular type of geographical environment.
Road networks and urban region features were computed from these image patches extracted from the large image. A one-vs-rest
Gaussian kernel SVM classification method was used to classify this large image. The classification results show that the image
patches were labeled with the class having the maximum geographical coverage of the area associated in the large image. The large
image was mapped into a “region matrix”, where each element of the matrix corresponds to a geographical class. This is a ‘hard’
classification and no inference can be drawn about the classification confidence.
In certain cases, this produces some anomalies, as a single patch may contain two or more different geographical coverages. In order
to have an estimate of these partial coverages, the output of the SVM was mapped into probabilities. These probability measures were
then studied to have a closer look at the classification accuracies. The results confirm that our method is able to classify a large image
into various geographical classes with a mean error of less than 10%.
Future studies can use operators to detect not only man-made structures like roads and urban areas, but also natural entities like rivers,
forests, etc. In this work we have restricted ourselves to a single resolution, but our methodology can be adapted to consider images
of higher resolutions from QuickBird and the future Pleiade satellite. At a better resolution it may be possible to extract different
structures like buildings, gardens, cross-roads, etc. This in turn will allow us to incorporate more classes to appropriately classify any
geographical environment. At an image resolution of 1m, we may imagine to have sub-classes of an existing class, e.g., classes like
urban Europe and urban USA can de divided into downtown, residential and industrial classes. |
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11 - Extraction of main and secondary roads in VHR images using a higher-order phase field model.
T. Peng et I. H. Jermyn et V. Prinet et J. Zerubia. Dans Proc. XXI ISPRS Congress, Part A, pages 215-22, Beijing, China, juillet 2008.
Mots-clés : Reseaux routiers, Zones urbaines, Imagerie satellitaire, Segmentation, Modelling, Methodes variationnelles.
Copyright : ISPRS
@INPROCEEDINGS{Peng08a,
|
| author |
= |
{Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, |
| title |
= |
{Extraction of main and secondary roads in VHR images using a higher-order phase field model}, |
| year |
= |
{2008}, |
| month |
= |
{juillet}, |
| booktitle |
= |
{Proc. XXI ISPRS Congress, Part A}, |
| pages |
= |
{215-22}, |
| address |
= |
{Beijing, China}, |
| pdf |
= |
{http://www.isprs.org/proceedings/XXXVII/congress/3_pdf/33.pdf}, |
| keyword |
= |
{Reseaux routiers, Zones urbaines, Imagerie satellitaire, Segmentation, Modelling, Methodes variationnelles} |
| } |
Abstract :
This paper addresses the issue of extracting main and secondary road networks in dense urban areas from very high resolution (VHR, ~0.61m) satellite images. The difficulty with secondary roads lies in the low discriminative power of the grey-level distributions of road regions and the background, and the greater effect of occlusions and other noise on narrower roads. To tackle this problem, we use a previously developed higher-order active contour (HOAC) phase field model and augment it with an additional non-linear nonlocal term. The additional term allows separate control of road width and road curvature; thus more precise prior knowledge can be incorporated, and better road prolongation can be achieved for the same width. Promising results on QuickBird panchromatic images at reduced resolutions and comparisons with other models demonstrate the role and the efficiency of our new model. |
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12 - Diagramme de phase d'une énergie de type contours actifs d'ordre supérieur : le cas d'une barre longue.
A. El Ghoul et I. H. Jermyn et J. Zerubia. Dans 16ème congrès francophone AFRIF-AFIA Reconnaissance des Formes et Intelligence Artificielle (RFIA), Amiens, France, janvier 2008.
Mots-clés : Diagramme de phase, Contours actifs d'ordre supérieur, Forme, a priori géométrique, Télédétection.
@INPROCEEDINGS{ElGhoul08,
|
| author |
= |
{El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Diagramme de phase d'une énergie de type contours actifs d'ordre supérieur : le cas d'une barre longue}, |
| year |
= |
{2008}, |
| month |
= |
{janvier}, |
| booktitle |
= |
{16ème congrès francophone AFRIF-AFIA Reconnaissance des Formes et Intelligence Artificielle (RFIA)}, |
| address |
= |
{Amiens, France}, |
| url |
= |
{https://hal.inria.fr/inria-00319575}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/31/95/75/PDF/rfia08aymenelghoul.pdf}, |
| keyword |
= |
{Diagramme de phase, Contours actifs d'ordre supérieur, Forme, a priori géométrique, Télédétection} |
| } |
Résumé :
Dans cet article, nous présentons l’analyse de stabilité du modèle des “contours actifs d’ordre supérieur” (CAOS), pour l’extraction des réseaux routiers présents dans des images de télédétection. Le modèle énergétique des CAOS à minimiser présente des comportements différents en fonction des valeurs des paramètres du modèle.
Il s’est avéré que deux structures géométriques sont favorisées
par ce modèle : des structures linéiques et circulaires. Nous nous intéressons ici à la détermination du diagramme de phase, qui définit les gammes des valeurs des paramètres du modèle des CAOS, permettant d’obtenir des structures linéiques. |
Abstract :
In this paper, we present a stability analysis of a “higher-order active contour” (HOAC) model for road network extraction from remotely sensed images. The HOAC energy presents several different behaviours depending on the model parameter values. Two types of geometric structure are favoured, namely line networks and circles. In this
work, we derive the phase diagram giving the parameter ranges of the HOAC model that allow stable linear structures. |
|
13 - A `Gas of Circles' Phase Field Model and its Application to Tree Crown Extraction.
P. Horvath et I. H. Jermyn. Dans Proc. European Signal Processing Conference (EUSIPCO), Poznan, Poland, septembre 2007.
Mots-clés : Champ de Phase, Extraction de Houppiers.
@INPROCEEDINGS{Horvath07d,
|
| author |
= |
{Horvath, P. and Jermyn, I. H.}, |
| title |
= |
{A `Gas of Circles' Phase Field Model and its Application to Tree Crown Extraction}, |
| year |
= |
{2007}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Poznan, Poland}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Horvath07d.pdf}, |
| keyword |
= |
{Champ de Phase, Extraction de Houppiers} |
| } |
Abstract :
The problem of extracting the region in the image domain
corresponding to an a priori unknown number of circular objects
occurs in several domains. We propose a new model of a `gas of
circles', the ensemble of regions in the image domain composed of
circles of a given radius. The model uses the phase field
reformulation of higher-order active contours (HOACs). Phase fields
possess several advantages over contour and level set approaches to
region modelling, in particular for HOAC models. The reformulation
allows us to benefit from these advantages without losing the
strengths of the HOAC framework. Combined with a suitable likelihood
energy, and applied to the tree crown extraction problem, the new
model shows markedly improved performance, both in quality of
results and in computation time, which is two orders of magnitude
less than the HOAC level set implementation.
|
|
14 - A Phase Field Model Incorporating Generic and Specific Prior Knowledge Applied to Road Network Extraction from VHR Satellite Images.
T. Peng et I. H. Jermyn et V. Prinet et J. Zerubia et B. Hu. Dans Proc. British Machine Vision Conference (BMVC), Warwick, UK, septembre 2007.
Mots-clés : Reseaux routiers, Very high resolution, Ordre superieur, Contour actif, Forme, A priori.
@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 |
= |
{septembre}, |
| booktitle |
= |
{Proc. British Machine Vision Conference (BMVC)}, |
| address |
= |
{Warwick, UK}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Peng07a.pdf}, |
| keyword |
= |
{Reseaux routiers, Very high resolution, Ordre superieur, Contour actif, Forme, A priori} |
| } |
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. |
|
15 - A New Phase Field Model of a `Gas of Circles' for Tree Crown Extraction from Aerial Images.
P. Horvath et I. H. Jermyn. Dans Proc. International Conference on Computer Analysis of Images and Patterns (CAIP), Vienna, Austria, août 2007.
Mots-clés : Champ de Phase, Extraction de Houppiers.
@INPROCEEDINGS{Horvath07b,
|
| author |
= |
{Horvath, P. and Jermyn, I. H.}, |
| title |
= |
{A New Phase Field Model of a `Gas of Circles' for Tree Crown Extraction from Aerial Images}, |
| year |
= |
{2007}, |
| month |
= |
{août}, |
| booktitle |
= |
{Proc. International Conference on Computer Analysis of Images and Patterns (CAIP)}, |
| address |
= |
{Vienna, Austria}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Horvath07b.pdf}, |
| keyword |
= |
{Champ de Phase, Extraction de Houppiers} |
| } |
Abstract :
We describe a model for tree crown extraction from aerial images, a
problem of great practical importance for the forestry industry. The
novelty lies in the prior model of the region occupied by tree
crowns in the image, which is a phase field version of the
higher-order active contour inflection point `gas of circles' model.
The model combines the strengths of the inflection point model with
those of the phase field framework: it removes the `phantom circles'
produced by the original `gas of circles' model, while executing two
orders of magnitude faster than the contour-based inflection point
model. The model has many other areas of application e.g., to
imagery in nanotechnology, biology, and physics. |
|
16 - Removing Shape-Preserving Transformations in Square-Root Elastic (SRE) Framework for Shape Analysis of Curves.
S. Joshi et E. Klassen et A. Srivastava et I. H. Jermyn. Dans Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Ezhou, China, août 2007.
Mots-clés : Forme, Reparameterization, Metrique, Geodesic.
Copyright : The original publication is available at www.springerlink.com.
@INPROCEEDINGS{Joshi07b,
|
| author |
= |
{Joshi, S. and Klassen, E. and Srivastava, A. and Jermyn, I. H.}, |
| title |
= |
{Removing Shape-Preserving Transformations in Square-Root Elastic (SRE) Framework for Shape Analysis of Curves}, |
| year |
= |
{2007}, |
| month |
= |
{août}, |
| booktitle |
= |
{Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, |
| address |
= |
{Ezhou, China}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Joshi07b.pdf}, |
| keyword |
= |
{Forme, Reparameterization, Metrique, Geodesic} |
| } |
Abstract :
This paper illustrates and extends an efficient framework, called the square-root-elastic (SRE) framework, for studying shapes of closed curves, that was first introduced in [2]. This framework combines the strengths of two important ideas - elastic shape metric and path-straightening methods - for finding geodesics in shape spaces of curves. The elastic metric allows for optimal matching of features between curves while path-straightening ensures that the algorithm results in geodesic paths. This paper extends this framework by removing two important shape preserving transformations: rotations and re-parameterizations, by forming quotient spaces and constructing geodesics on these quotient spaces. These ideas are demonstrated using experiments involving 2D and 3D curves. |
|
17 - A Novel Representation for Riemannian Analysis of Elastic Curves in R^n.
S. Joshi et E. Klassen et A. Srivastava et I. H. Jermyn. Dans Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Minneapolis, USA, juin 2007.
Mots-clés : Forme, Metrique, Geodesic, A priori.
@INPROCEEDINGS{Joshi07a,
|
| author |
= |
{Joshi, S. and Klassen, E. and Srivastava, A. and Jermyn, I. H.}, |
| title |
= |
{A Novel Representation for Riemannian Analysis of Elastic Curves in R^n}, |
| year |
= |
{2007}, |
| month |
= |
{juin}, |
| booktitle |
= |
{Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, |
| address |
= |
{Minneapolis, USA}, |
| url |
= |
{http://dx.doi.org/10.1109/CVPR.2007.383185}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Joshi07a.pdf}, |
| keyword |
= |
{Forme, Metrique, Geodesic, A priori} |
| } |
Abstract :
We propose an efficient representation for studying shapes of closed curves in R^n. This paper combines the strengths of two important ideas---elastic shape metric and path-straightening methods---and results in a very fast algorithm for finding geodesics in shape spaces. The elastic metric allows for optimal matching of features between the two curves while path-straightening ensures that the algorithm results in geodesic paths. For the novel representation proposed here, the elastic metric becomes the simple L^2 metric, in contrast to the past usage where more complex forms were used. We present the step-by-step algorithms for computing geodesics and demonstrate them with 2-D as well as 3-D examples. |
|
18 - Indexing Satellite Images with Features Computed from Man-Made Structures on the Earth’s Surface.
A. Bhattacharya et M. Roux et H. Maitre et I. H. Jermyn et X. Descombes et J. Zerubia. Dans Proc. International Workshop on Content-Based Multimedia Indexing, Bordeaux, France, juin 2007.
Mots-clés : Indexation, Reseaux routiers, Semantique, Retrieval, Feature statistics.
@INPROCEEDINGS{Bhattacharya07a,
|
| author |
= |
{Bhattacharya, A. and Roux, M. and Maitre, H. and Jermyn, I. H. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Indexing Satellite Images with Features Computed from Man-Made Structures on the Earth’s Surface}, |
| year |
= |
{2007}, |
| month |
= |
{juin}, |
| booktitle |
= |
{Proc. International Workshop on Content-Based Multimedia Indexing}, |
| address |
= |
{Bordeaux, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Bhattacharya07a.pdf}, |
| keyword |
= |
{Indexation, Reseaux routiers, Semantique, Retrieval, Feature statistics} |
| } |
Abstract :
Indexing and retrieval from remote sensing image databases relies on the extraction of appropriate information from the data about the entity of interest (e.g. land cover type) and on the robustness of this extraction to nuisance variables. Other entities in an image may be strongly correlated with the entity of interest and their properties can therefore be used to characterize this entity. The road network contained in an image is one example. The properties of road networks vary considerably from one geographical environment to another, and they can therefore be used to classify and retrieve such environments. In this paper, we define several such environments, and classify them with the aid of geometrical and topological features computed from the road networks occurring in them. The relative failure of network extraction methods in certain types of urban area obliges us to segment such areas and to add a second set of geometrical and topological features computed from the segmentations. To validate the approach, feature selection and SVM linear kernel classification are performed on the feature set arising from a diverse image database. |
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Rapport d'activité 2011
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