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Publications about Spatial point process
Result of the query in the list of publications :
Article |
1 - Geometric Feature Extraction by a Multi-Marked Point Process .
F. Lafarge and G. Gimel'farb and X. Descombes. IEEE Trans. Pattern Analysis and Machine Intelligence, 32(9): pages 1597-1609, September 2010.
Keywords : Shape extraction, Spatial point process, Stochastic geometry, fast optimization, Texture, remote sensing.
@ARTICLE{pami09b_lafarge,
|
| author |
= |
{Lafarge, F. and Gimel'farb, G. and Descombes, X.}, |
| title |
= |
{Geometric Feature Extraction by a Multi-Marked Point Process }, |
| year |
= |
{2010}, |
| month |
= |
{September}, |
| journal |
= |
{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
| volume |
= |
{32}, |
| number |
= |
{9}, |
| pages |
= |
{1597-1609}, |
| url |
= |
{http://dx.doi.org/10.1109/TPAMI.2009.152}, |
| keyword |
= |
{Shape extraction, Spatial point process, Stochastic geometry, fast optimization, Texture, remote sensing} |
| } |
Abstract :
This paper presents a new stochastic marked point process for describing images in terms of a finite library of geometric objects. Image analysis based on conventional marked point processes has already produced convincing results but at the expense of parameter tuning, computing time, and model specificity. Our more general multimarked point process has simpler parametric setting, yields notably shorter computing times, and can be applied to a variety of applications. Both linear and areal primitives extracted from a library of geometric objects are matched to a given image using a probabilistic Gibbs model, and a Jump-Diffusion process is performed to search for the optimal object configuration. Experiments with remotely sensed images and natural textures show that the proposed approach has good potential. We conclude with a discussion about the insertion of more complex object interactions in the model by studying the compromise between model complexity and efficiency. |
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Technical and Research Report |
1 - An adaptive simulated annealing cooling schedule for object detection in images.
M. Ortner and X. Descombes and J. Zerubia. Research Report 6336, INRIA, October 2007.
Keywords : Image procressing, Shape extraction, Spatial point process, Simulated Annealing, Adaptive cooling schedule.
@TECHREPORT{Ortner-Descombes,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{An adaptive simulated annealing cooling schedule for object detection in images}, |
| year |
= |
{2007}, |
| month |
= |
{October}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6336}, |
| url |
= |
{https://hal.inria.fr/inria-00181764}, |
| pdf |
= |
{https://hal.inria.fr/inria-00181764}, |
| keyword |
= |
{Image procressing, Shape extraction, Spatial point process, Simulated Annealing, Adaptive cooling schedule} |
| } |
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