Issue No. 09 - September (2010 vol. 32)
Florent Lafarge , Ecole des ponts, Paris
Georgy Gimel'farb , University of Auckland, Auckland
Xavier Descombes , INRIA, Sophia Antipolis
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.
Object extraction, remote sensing, texture analysis, stochastic models, Monte Carlo simulations.
G. Gimel'farb, X. Descombes and F. Lafarge, "Geometric Feature Extraction by a Multimarked Point Process," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 32, no. , pp. 1597-1609, 2009.