Publications of Ian Jermyn

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• 12 Articles
• 46 Conference articles
• 9 Technical and Research Reports
• 2 Collection articles or Books chapters

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46 Conference articles

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 and I. H. Jermyn and V. Prinet and J. Zerubia. In Proc. European Conference on Computer Vision (ECCV), Marseille, France, October 2008. Keywords : Dense urban area, Phase Field, Road network, Variational methods, 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 = {October}, 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, Phase Field, Road network, Variational methods, 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.

10 - Indexing of mid-resolution satellite images with structural attributes. A. Bhattacharya and M. Roux and H. Maitre and I. H. Jermyn and X. Descombes and J. Zerubia. In The International Society for Photogrammetry and Remote Sensing, Beijing, China, July 2008. Keywords : 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 = {July}, 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.

11 - Extraction of main and secondary roads in VHR images using a higher-order phase field model. T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia. In Proc. XXI ISPRS Congress, Part A, pages 215-22, Beijing, China, July 2008. Keywords : Road network, Urban areas, Satellite images, Segmentation, Modelling, Variational methods. 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 = {July}, 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 = {Road network, Urban areas, Satellite images, Segmentation, Modelling, Variational methods} } 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.

12 - Diagramme de phase d'une énergie de type contours actifs d'ordre supérieur : le cas d'une barre longue. A. El Ghoul and I. H. Jermyn and J. Zerubia. In 16ème congrès francophone AFRIF-AFIA Reconnaissance des Formes et Intelligence Artificielle (RFIA), Amiens, France, January 2008. Keywords : Diagramme de phase, Contours actifs d'ordre supérieur, Shape, geometric prior, 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 = {January}, 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, Shape, geometric prior, 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 and I. H. Jermyn. In Proc. European Signal Processing Conference (EUSIPCO), Poznan, Poland, September 2007. Keywords : Phase Field, Tree Crown Extraction. @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 = {September}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Poznan, Poland}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_Horvath07d.pdf}, keyword = {Phase Field, Tree Crown Extraction} } 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 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.

15 - A New Phase Field Model of a `Gas of Circles' for Tree Crown Extraction from Aerial Images. P. Horvath and I. H. Jermyn. In Proc. International Conference on Computer Analysis of Images and Patterns (CAIP), Vienna, Austria, August 2007. Keywords : Phase Field, Tree Crown Extraction. @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 = {August}, 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 = {Phase Field, Tree Crown Extraction} } 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 and E. Klassen and A. Srivastava and I. H. Jermyn. In Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Ezhou, China, August 2007. Keywords : Shape, Reparameterization, Metric, 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 = {August}, 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 = {Shape, Reparameterization, Metric, 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 and E. Klassen and A. Srivastava and I. H. Jermyn. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Minneapolis, USA, June 2007. Keywords : Shape, Metric, Geodesic, Prior. @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 = {June}, 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 = {Shape, Metric, Geodesic, Prior} } 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 and M. Roux and H. Maitre and I. H. Jermyn and X. Descombes and J. Zerubia. In Proc. International Workshop on Content-Based Multimedia Indexing, Bordeaux, France, June 2007. Keywords : Indexation, Road network, Semantic, 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 = {June}, 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, Road network, Semantic, 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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