|
|
Publications de M. Haindl
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. |
|
 haut de la page
2 Articles de conférence |
1 - A Hierarchical Texture Model for Unsupervised Segmentation of Remotely Sensed Images.
G. Scarpa et M. Haindl et J. Zerubia. Dans Scandinavian Conference on Image Analysis, Vol. 4522/2007, pages 303-312, series LNCS 4522, Ed. Springer Berlin / Heidelberg, Aalborg, Denmark, juin 2007.
@INPROCEEDINGS{scarpa_scia_07,
|
| author |
= |
{Scarpa, G. and Haindl, M. and Zerubia, J.}, |
| title |
= |
{A Hierarchical Texture Model for Unsupervised Segmentation of Remotely Sensed Images}, |
| year |
= |
{2007}, |
| month |
= |
{juin}, |
| booktitle |
= |
{Scandinavian Conference on Image Analysis}, |
| volume |
= |
{4522/2007}, |
| pages |
= |
{303-312}, |
| series |
= |
{LNCS 4522}, |
| editor |
= |
{Springer Berlin / Heidelberg}, |
| address |
= |
{Aalborg, Denmark}, |
| url |
= |
{http://link.springer.com/chapter/10.1007%2F978-3-540-73040-8_31}, |
| keyword |
= |
{} |
| } |
|
2 - A Hierarchical finite-state model for texture segmentation.
G. Scarpa et M. Haindl et J. Zerubia. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol. 1, pages 1209-1212, Honolulu, HI (USA), avril 2007.
@INPROCEEDINGS{scarpa_icassp_07,
|
| author |
= |
{Scarpa, G. and Haindl, M. and Zerubia, J.}, |
| title |
= |
{A Hierarchical finite-state model for texture segmentation}, |
| year |
= |
{2007}, |
| month |
= |
{avril}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| volume |
= |
{1}, |
| pages |
= |
{1209-1212}, |
| address |
= |
{Honolulu, HI (USA)}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4217303}, |
| keyword |
= |
{} |
| } |
|
haut de la page
Rapport de recherche et Rapport technique |
1 - Hierarchical finite-state modeling for texture segmentation with application to forest classification.
G. Scarpa et M. Haindl et J. Zerubia. Research Report 6066, INRIA, INRIA, France, décembre 2006.
Mots-clés : Texture, Segmentation, Co-occurrence matrix, Approche structurelle, MCMC, Synthesis.
@TECHREPORT{scarparr06,
|
| author |
= |
{Scarpa, G. and Haindl, M. and Zerubia, J.}, |
| title |
= |
{Hierarchical finite-state modeling for texture segmentation with application to forest classification}, |
| year |
= |
{2006}, |
| month |
= |
{décembre}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6066}, |
| address |
= |
{INRIA, France}, |
| url |
= |
{https://hal.inria.fr/inria-00118420}, |
| keyword |
= |
{Texture, Segmentation, Co-occurrence matrix, Approche structurelle, MCMC, Synthesis} |
| } |
Abstract :
In this research report we present a new model for texture representation which is particularly well suited for image analysis and segmentation. Any image is first discretized and then a hierarchical finite-state region-based model is automatically coupled with the data by means of a sequential optimization scheme, namely the Texture Fragmentation and Reconstruction (TFR) algorithm. The TFR algorithm allows to model both intra- and inter-texture interactions, and eventually addresses the segmentation task in a completely unsupervised manner. Moreover, it provides a hierarchical output, as the user may decide the scale at which the segmentation has to be given. Tests were carried out on both natural texture mosaics provided by the Prague Texture Segmentation Datagenerator Benchmark and remote-sensing data of forest areas provided by the French National Forest Inventory (IFN). |
|
haut de la page
Ces pages sont générées par 
|
Rapport d'activité 2011
bibtex
