X. Descombes - Academia.edu (original) (raw)
Papers by X. Descombes
Lecture Notes in Computer Science, 2011
Proceedings of the Second International Conference on Computer Vision Theory and Applications, 2007
IEEE International Conference on Image Processing 2005, 2005
2004 12th European Signal Processing Conference, 2004
In this paper we aim at extracting tree crowns from remotely sensed images. Our approach is to co... more In this paper we aim at extracting tree crowns from remotely sensed images. Our approach is to consider that these images are some realizations of a marked point process. The first step is to define the geometrical objects that design the trees, and the density of the process. Then, we use a Reversible Jump MCMC1 dynamics and a simulated annealing to get the maximum a posteriori estimator of the tree crown distribution on the image. Transitions of the Markov chain are managed by some specific proposition kernels. Results are shown on aerial images of poplars provided by IFN.
Computational Imaging XI, 2013
2006 International Conference on Image Processing, 2006
2010 IEEE International Conference on Image Processing, 2010
2013 IEEE International Conference on Image Processing, 2013
2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008
Journal of Mathematical Imaging and Vision, 2008
ISPRS Journal of Photogrammetry and Remote Sensing, 2008
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
IEEE Transactions on Medical Imaging, 2004
IEEE Transactions on Medical Imaging, 2008
IEEE Geoscience and Remote Sensing Letters, 2009
Lecture Notes in Computer Science, 2011
Proceedings of the Second International Conference on Computer Vision Theory and Applications, 2007
IEEE International Conference on Image Processing 2005, 2005
2004 12th European Signal Processing Conference, 2004
In this paper we aim at extracting tree crowns from remotely sensed images. Our approach is to co... more In this paper we aim at extracting tree crowns from remotely sensed images. Our approach is to consider that these images are some realizations of a marked point process. The first step is to define the geometrical objects that design the trees, and the density of the process. Then, we use a Reversible Jump MCMC1 dynamics and a simulated annealing to get the maximum a posteriori estimator of the tree crown distribution on the image. Transitions of the Markov chain are managed by some specific proposition kernels. Results are shown on aerial images of poplars provided by IFN.
Computational Imaging XI, 2013
2006 International Conference on Image Processing, 2006
2010 IEEE International Conference on Image Processing, 2010
2013 IEEE International Conference on Image Processing, 2013
2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008
Journal of Mathematical Imaging and Vision, 2008
ISPRS Journal of Photogrammetry and Remote Sensing, 2008
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
IEEE Transactions on Medical Imaging, 2004
IEEE Transactions on Medical Imaging, 2008
IEEE Geoscience and Remote Sensing Letters, 2009