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Publications about MAP estimation
Result of the query in the list of publications :
Article |
1 - Change Detection in Optical Aerial Images by a Multi-Layer Conditional Mixed Markov Model.
C. Benedek and T. Szirányi. IEEE Trans. Geoscience and Remote Sensing, 47(10): pages 3416-3430, October 2009.
Keywords : mixed Markov models, Change detection, Aerial images, MAP estimation.
Copyright : IEEE
@ARTICLE{benedekTGRS09,
|
| author |
= |
{Benedek, C. and Szirányi, T.}, |
| title |
= |
{Change Detection in Optical Aerial Images by a Multi-Layer Conditional Mixed Markov Model}, |
| year |
= |
{2009}, |
| month |
= |
{October}, |
| journal |
= |
{IEEE Trans. Geoscience and Remote Sensing}, |
| volume |
= |
{47}, |
| number |
= |
{10}, |
| pages |
= |
{3416-3430}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?isnumber=5257398&arnumber=5169964&count=26&index=11}, |
| keyword |
= |
{mixed Markov models, Change detection, Aerial images, MAP estimation} |
| } |
Abstract :
In this paper we propose a probabilistic model for detecting relevant changes in registered aerial image pairs taken with the time differences of several years and in different seasonal conditions. The introduced approach, called the Conditional Mixed Markov model (CXM), is a combination of a mixed Markov model and a conditionally independent random field of signals. The model integrates global intensity statistics with local correlation and contrast features. A global energy optimization process ensures simultaneously optimal local feature selection and smooth, observation-consistent segmentation. Validation is given on real aerial image sets provided by the Hungarian Institute of Geodesy, Cartography and Remote Sensing and Google Earth. |
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Conference article |
1 - Adaptive Simulated Annealing for Energy Minimization Problem in a Marked Point Process Application.
G. Perrin and X. Descombes and J. Zerubia. In Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), St Augustine, Florida, USA, November 2005.
Keywords : Simulated Annealing, Marked point process, Stochastic geometry, MAP estimation, RJMCMC.
Copyright : Springer Verlag
@INPROCEEDINGS{perrin_emmcvpr05,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Adaptive Simulated Annealing for Energy Minimization Problem in a Marked Point Process Application}, |
| year |
= |
{2005}, |
| month |
= |
{November}, |
| booktitle |
= |
{Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, |
| address |
= |
{St Augustine, Florida, USA}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_emmcvpr.pdf}, |
| ps |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_emmcvpr.ps.gz}, |
| keyword |
= |
{Simulated Annealing, Marked point process, Stochastic geometry, MAP estimation, RJMCMC} |
| } |
Abstract :
We use marked point processes to detect an unknown number of trees from high resolution aerial images. This is in fact an energy minimization problem, where the energy contains a prior term which takes into account the geometrical properties of the objects, and a data term to match these objects to the image. This stochastic process is simulated via a Reversible Jump Markov Chain Monte Carlo procedure, which embeds a Simulated Annealing scheme to extract the best configuration of objects.
We compare here different cooling schedules of the Simulated Annealing algorithm which could provide some good minimization in a short time. We also study some adaptive proposition kernels. |
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2 Technical and Research Reports |
1 - Space non-invariant point-spread function and its estimation in fluorescence microscopy.
P. Pankajakshan and L. Blanc-Féraud and Z. Kam and J. Zerubia. Research Report 7157, INRIA, December 2009.
Keywords : Confocal Laser Scanning Microscopy, point spread function, Bayesian estimation, MAP estimation, Deconvolution, fluorescence microscopy.
@TECHREPORT{ppankajakshan09c,
|
| author |
= |
{Pankajakshan, P. and Blanc-Féraud, L. and Kam, Z. and Zerubia, J.}, |
| title |
= |
{Space non-invariant point-spread function and its estimation in fluorescence microscopy}, |
| year |
= |
{2009}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7157}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00438719/en/}, |
| keyword |
= |
{Confocal Laser Scanning Microscopy, point spread function, Bayesian estimation, MAP estimation, Deconvolution, fluorescence microscopy} |
| } |
Résumé :
Dans ce rapport de recherche, nous rappelons brièvement comment la nature limitée de diffraction de l'objectif d'un microscope optique, et le bruit
intrinsèque peuvent affecter la résolution d'une image observée. Un algorithme de déconvolution aveugle a été proposé en vue de restaurer les fréquences manquants au delà de la limite de diffraction. Cependant, sous d'autres conditions, l'approximation du systéme imageur l'imagerie sans aberration n'est plus valide et donc les aberrations de la phase du front d'onde émergeant d'un médium ne sont plus ignorées. Dans la deuxième partie de
ce rapport de recherche, nous montrons que la distribution d'intensité originelle et la localisation d'un objet peuvent être retrouvées uniquement en obtenant de la phase du front d'onde
réfracté, à partir d'images d'intensité observées. Nous démontrons cela par obtention de la fonction de ou a partir d'une microsphère imagée. Le bruit et l'infl uence de la taille de la
microsphère peuvent être diminués et parfois complètement supprimes des images observées en utilisant un estimateur maximum a posteriori. Néanmoins, a cause de l'incohérence du système d'acquisition, une récupération de phase a partir d'intensités observées n'est possible que si la restauration de la phase est contrainte. Nous avons utilisé l'optique géométrique
pour modéliser la phase du front d'onde réfracté, et nous avons teste l'algorithme sur des images simulées. |
Abstract :
In this research report, we recall briefly how the diffraction-limited nature of an optical microscope's objective, and the intrinsic noise can affect the observed images' resolution. A blind deconvolution algorithm can restore the lost frequencies beyond the diffraction limit. However, under other imaging conditions, the approximation of aberration-free imaging, is not applicable, and the phase aberrations of the emerging wavefront from a specimen immersion medium cannot be ignored any more. We show that an object's location and its original intensity distribution can be recovered by retrieving the refracted wavefront's phase from the observed intensity images. We demonstrate this by retrieving the point-spread function from an imaged microsphere. The noise and the influence of the microsphere size can be mitigated and sometimes completely removed from the observed images by using a maximum a posteriori estimate. However, due to the incoherent nature of the acquisition system, phase retrieval from the observed intensities will be possible only if the phase is constrained. We have used geometrical optics to model the phase of the refracted wavefront, and tested the algorithm on some simulated images. |
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2 - SAR Image Filtering Based on the Heavy-Tailed Rayleigh Model.
A. Achim and E.E. Kuruoglu and J. Zerubia. Research Report 5493, INRIA, France, February 2005.
Keywords : Synthetic Aperture Radar (SAR), MAP estimation, Alpha-stable distribution, Mellin transform.
@TECHREPORT{5493,
|
| author |
= |
{Achim, A. and Kuruoglu, E.E. and Zerubia, J.}, |
| title |
= |
{SAR Image Filtering Based on the Heavy-Tailed Rayleigh Model}, |
| year |
= |
{2005}, |
| month |
= |
{February}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5493}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00070514}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/70514/filename/RR-5493.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/05/14/PS/RR-5493.ps}, |
| keyword |
= |
{Synthetic Aperture Radar (SAR), MAP estimation, Alpha-stable distribution, Mellin transform} |
| } |
Résumé :
Les images issues d'un radar à synthèse d'ouverture (RSO) sont affectées de manière inhérente par un bruit dépendant du signal, généralement connu sous le nom de bruit de chatoiement et qui est dû à la cohérence de l'onde radar. Dans ce rapport, nous proposons un nouveau filtre adaptatif pour débruiter les images RSO et nous déduisons un estimateur du maximum a posteriori (MAP) pour la section efficace du diagramme de gain en radar. On utilise d'abord une transformée logarithmique afin de changer le bruit multiplicatif en bruit additif. Nous modélisons la section efficace à l'aide d'une densité de probabilité récemment introduite - la densité de Rayleigh à queue lourde, qui a été obtenue en supposant que les parties réelles et imaginaires du signal complexe reçu peuvent être mieux caractérisées à l'aide de la famille des distributions alpha-stables. Nous estimons les paramètres du modèle à partir d'observations bruitées en faisant appel à la théorie statistique de deuxième espèce qui est fondée sur la transformée de Mellin. Enfin, nous faisons la comparaison entre la méthode que nous proposons et d'autres filtres classiques pour le débruitage d'images RSO. Nos résultats expérimentaux démontrent que le filtre MAP homomorphique fondé sur le modèle de Rayleigh à queue lourde est parmi les meilleurs pour enlever le bruit de chatoiement. |
Abstract :
Synthetic aperture radar (SAR) images are inherently affected by a signal dependent noise known as speckle, which is due to the radar wave coherence. In this report, we propose a novel adaptive despeckling filter and derive a maximum a posteriori (MAP) estimator for the radar cross section (RCS). We first employ a logarithmic transformation to change the multiplicative speckle into additive noise. We model the RCS using the recently introduced heavy-tailed Rayleigh density function, which was derived based on the assumption that the real and imaginary parts of the received complex signal are best described using the alpha-stable family of distribution. We estimate model parameters from noisy observations by means of second-kind statistics theory, which relies on the Mellin transform. Finally, we compare our proposed algorithm with several classical speckle filters applied on actual SAR images. Experimental results show that the homomorphic MAP filter based on the heavy-tailed Rayleigh prior for the RCS is among the best for speckle removal. |
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