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Publications de Raffaele Gaetano
Résultat de la recherche dans la liste des publications :
Article |
1 - Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation.
G. Scarpa et R. Gaetano et M. Haindl et J. Zerubia. IEEE Trans. on Image Processing, 18(8): pages 1830-1843, août 2009.
Mots-clés : Hierarchical Image Models, Markov Process, Pattern Analysis.
@ARTICLE{ScarpaTIP09,
|
| author |
= |
{Scarpa, G. and Gaetano, R. and Haindl, M. and Zerubia, J.}, |
| title |
= |
{ Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation}, |
| year |
= |
{2009}, |
| month |
= |
{août}, |
| journal |
= |
{IEEE Trans. on Image Processing}, |
| volume |
= |
{18}, |
| number |
= |
{8}, |
| pages |
= |
{1830-1843}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5161445&arnumber=4914796&count=21&index=11}, |
| keyword |
= |
{Hierarchical Image Models, Markov Process, Pattern Analysis} |
| } |
Abstract :
In this paper, we present a novel multiscale texture model and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled, in turn, by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The “fragmentation” step allows one to find the elementary textures of the model, while the “reconstruction” step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural and remote sensing images. |
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3 Articles de conférence |
1 - Morphological road segmentation in urban areas from high resolution satellite images.
R. Gaetano et J. Zerubia et G. Scarpa et G. Poggi. Dans International Conference on Digital Signal Processing, Corfu, Greece, juillet 2011.
Mots-clés : Segmentation, Classification, skeletonization , pattern recognition, shape analysis.
@INPROCEEDINGS{GaetanoDSP,
|
| author |
= |
{Gaetano, R. and Zerubia, J. and Scarpa, G. and Poggi, G.}, |
| title |
= |
{Morphological road segmentation in urban areas from high resolution satellite images}, |
| year |
= |
{2011}, |
| month |
= |
{juillet}, |
| booktitle |
= |
{International Conference on Digital Signal Processing}, |
| address |
= |
{Corfu, Greece}, |
| url |
= |
{http://hal.inria.fr/inria-00618222/fr/}, |
| keyword |
= |
{Segmentation, Classification, skeletonization , pattern recognition, shape analysis} |
| } |
Abstract :
High resolution satellite images provided by the last generation
sensors significantly increased the potential of almost
all the image information mining (IIM) applications related
to earth observation. This is especially true for the extraction
of road information, task of primary interest for many remote
sensing applications, which scope is more and more extended
to complex urban scenarios thanks to the availability of highly
detailed images. This context is particularly challenging due
to such factors as the variability of road visual appearence
and the occlusions from entities like trees, cars and shadows.
On the other hand, the peculiar geometry and morphology of
man-made structures, particularly relevant in urban areas, is
enhanced in high resolution images, making this kind of information
especially useful for road detection.
In this work, we provide a new insight on the use of morphological
image analysis for road extraction in complex urban
scenarios, and propose a technique for road segmentation
that only relies on this domain. The keypoint of the technique
is the use of skeletons as powerful descriptors for road objects:
the proposed method is based on an ad-hoc skeletonization
procedure that enhances the linear structure of road segments,
and extracts road objects by first detecting their skeletons
and then associating each of them with a region of the
image. Experimental results are presented on two different
high resolution satellite images of urban areas. |
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2 - Graph-based Analysis of Textured Images for Hierarchical Segmentation.
R. Gaetano et G. Scarpa et T. Sziranyi. Dans Proc. British Machine Vision Conference (BMVC), Aberystwyth, UK, août 2010.
@INPROCEEDINGS{Gaetano2010,
|
| author |
= |
{Gaetano, R. and Scarpa, G. and Sziranyi, T.}, |
| title |
= |
{Graph-based Analysis of Textured Images for Hierarchical Segmentation}, |
| year |
= |
{2010}, |
| month |
= |
{août}, |
| booktitle |
= |
{Proc. British Machine Vision Conference (BMVC)}, |
| address |
= |
{Aberystwyth, UK}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00506596}, |
| keyword |
= |
{} |
| } |
Abstract :
The Texture Fragmentation and Reconstruction (TFR) algorithm has beenrecently introduced to address the problem of image segmentationby textural properties, based on a suitable image description toolknown as the Hierarchical Multiple Markov Chain (H-MMC) model. TFRprovides a hierarchical set of nested segmentation maps by firstidentifying the elementary image patterns, and then merging themsequentially to identify complete textures at different scales ofobservation.In this work, we propose a major modification to the TFR by resortingto a graph based description of the image content and a graph clusteringtechnique for the enhancement and extraction of image patterns. Aprocedure based on mathematical morphology will be introduced thatallows for the construction of a color-wise image representationby means of multiple graph structures, along with a simple clusteringtechnique aimed at cutting the graphs and correspondingly segmentgroups of connected components with a similar spatial context.The performance assessment, realized both on synthetic compositionsof real-world textures and images from the remote sensing domain,confirm the effectiveness and potential of the proposed method. |
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3 - Unsupervised Hierarchical Image Segmentation based on the TS-MRF model and Fast Mean-Shift Clustering.
R. Gaetano et G. Scarpa et G. Poggi et J. Zerubia. Dans Proc. European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland, août 2008.
Mots-clés : Segmentation, Markov Random Fields, Mean Shift, Land Classification.
@INPROCEEDINGS{Gaetano2008,
|
| author |
= |
{Gaetano, R. and Scarpa, G. and Poggi, G. and Zerubia, J.}, |
| title |
= |
{Unsupervised Hierarchical Image Segmentation based on the TS-MRF model and Fast Mean-Shift Clustering}, |
| year |
= |
{2008}, |
| month |
= |
{août}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Lausanne, Switzerland}, |
| pdf |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7080521}, |
| keyword |
= |
{Segmentation, Markov Random Fields, Mean Shift, Land Classification} |
| } |
Abstract :
Tree-Structured Markov Random Field (TS-MRF) models have been recently proposed to provide a hierarchical multiscale description of images. Based on such a model, the unsupervised image segmentation is carried out by means of a sequence of nested class splits, where each class is modeled as a local binary MRF.
We propose here a new TS-MRF unsupervised segmentation technique which improves upon the original algorithm by selecting a better tree structure and eliminating spurious classes. Such results are obtained by using the Mean-Shift procedure to estimate the number of pdf modes at each node (thus allowing for a non-binary tree), and to obtain a more reliable initial clustering for subsequent MRF optimization. To this end, we devise a new reliable and fast clustering algorithm based on the Mean-Shift technique. Experimental results prove the potential of the proposed method. |
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