Publications de Ian Jermyn

Résultat de la recherche dans la liste des publications :

• 12 Articles
• 46 Articles de conférence
• 9 Rapports de recherche et Rapports techniques
• 2 Articles de collection ou Chapitres de livres

nouvelle recherche | bibtex

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46 Articles de conférence

39 - Adaptive Probabilistic Models of Wavelet Packets for the Analysis and Segmentation of Textured Remote Sensing Images. K. Brady et I. H. Jermyn et J. Zerubia. Dans Proc. British Machine Vision Conference (BMVC), Norwich, U. K., septembre 2003. Mots-clés : probabilistic, Adaptatif, 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 = {septembre}, 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, Adaptatif, 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 et J.M. Francos et I. H. Jermyn. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hong Kong, avril 2003. Mots-clés : Texture, Mixture de gaussiennes, 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 = {avril}, 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, Mixture de gaussiennes, 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.

41 - Unsupervised Image Segmentation via Markov Trees and Complex Wavelets. C. Shaffrey et N. Kingsbury et I. H. Jermyn. Dans Proc. IEEE International Conference on Image Processing (ICIP), Rochester, USA, septembre 2002. Mots-clés : Segmentation, Hidden Markov Model, Texture, Couleur. @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 = {septembre}, 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, Couleur} } 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.

42 - Psychovisual Evaluation of Image Segmentation Algorithms. C. Shaffrey et I. H. Jermyn et N. Kingsbury. Dans Proc. Advanced Concepts for Intelligent Vision Systems, Ghent, Belgique, septembre 2002. @INPROCEEDINGS{kingij, author = {Shaffrey, C. and Jermyn, I. H. and Kingsbury, N.}, title = {Psychovisual Evaluation of Image Segmentation Algorithms}, year = {2002}, month = {septembre}, 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 et C. Shaffrey et N. Kingsbury. Dans Proc. Advanced Concepts for Intelligent Vision Systems, Ghent, Belgique, septembre 2002. @INPROCEEDINGS{shaffreyij, author = {Jermyn, I. H. and Shaffrey, C. and Kingsbury, N.}, title = {Evaluation Methodologies for Image Retrieval Systems}, year = {2002}, month = {septembre}, 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 et I. H. Jermyn. Dans Proc. IEEE International Conference on Computer Vision (ICCV), Vancouver, Canada, juillet 2001. Mots-clés : Stereo, Motion, global, optimum, Graphe, Cycle. @INPROCEEDINGS{IJ01a, author = {Ishikawa, H. and Jermyn, I. H.}, title = {Region extraction from multiple images}, year = {2001}, month = {juillet}, 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, Graphe, 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.

45 - Judging whether multiple silhouettes can come from the same object. D. Jacobs et P. Belhumeur et I. H. Jermyn. Dans Int. Workshop on Visual Form, Springer-Verlag Lecture Notes in Computer Science 2059, Capri, Italie, mai 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 = {mai}, 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 et H. Ishikawa. Dans Proc. IEEE International Conference on Computer Vision (ICCV), 1999. Mots-clés : global, optimum, Graphe, 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, Graphe, 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 Rapports de recherche et Rapports techniques

1 - A higher-order active contour model of a `gas of circles' and its application to tree crown extraction. P. Horvath et I. H. Jermyn et Z. Kato et J. Zerubia. Research Report 6026, INRIA, France, novembre 2006. Mots-clés : Extraction de Houppiers, Aerial images, Ordre superieur, Contour actif, Gaz de cercles, Forme. @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 = {novembre}, 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 = {Extraction de Houppiers, Aerial images, Ordre superieur, Contour actif, Gaz de cercles, Forme} } 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 et I. H. Jermyn et J. Zerubia. Rapport de Recherche 5717, INRIA, France, octobre 2005. Mots-clés : Reseaux routiers, Continuity, Gap closure, Ordre superieur, Contour actif, Forme. @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 = {octobre}, 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 = {Reseaux routiers, Continuity, Gap closure, Ordre superieur, Contour actif, Forme} } 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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