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Publications about Hidden Markov Model
Result of the query in the list of publications :
2 Conference articles |
1 - 3D city modeling based on Hidden Markov Model.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. IEEE International Conference on Image Processing (ICIP), San Antonio, U.S., September 2007.
Note : Copyright IEEE
Keywords : 3D reconstruction, Building, Hidden Markov Model.
@INPROCEEDINGS{lafarge_icip07,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{3D city modeling based on Hidden Markov Model}, |
| year |
= |
{2007}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{San Antonio, U.S.}, |
| note |
= |
{Copyright IEEE}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4379207}, |
| keyword |
= |
{3D reconstruction, Building, Hidden Markov Model} |
| } |
|
2 - Unsupervised Image Segmentation via Markov Trees and Complex Wavelets.
C. Shaffrey and N. Kingsbury and I. H. Jermyn. In Proc. IEEE International Conference on Image Processing (ICIP), Rochester, USA, September 2002.
Keywords : Segmentation, Hidden Markov Model, Texture, Colour.
@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 |
= |
{September}, |
| 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, Colour} |
| } |
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. |
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