Boundary Detection Through Dynamic Polygons

Pievatolo, Antonio; Green, Peter J.

doi:10.1111/1467-9868.00143

Cited by 35 publications

(35 citation statements)

References 16 publications

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“…The marked point processes have been introduced in image processing by Baddeley and Van Lieshout [22], and developed and extended further in [16], [23], [24]. These stochastic models can be considered as an extension of conventional Markov random fields [25] such that random variables are associated not with pixel values but with geometrical shapes describing the image.…”

Section: Point Processes and Marksmentioning

confidence: 99%

Geometric Feature Extraction by a Multimarked Point Process

Lafarge

Gimel’farb

Descombes

2010

IEEE Trans. Pattern Anal. Mach. Intell.

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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 easy parameter tuning, short computing time, and unspecific models. Our more general multi-marked 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 JumpDiffusion process is performed to search for the optimal object configuration. Experiments with remotely sensed images and natural textures show 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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Section: Point Processes and Marksmentioning

confidence: 99%

Geometric Feature Extraction by a Multimarked Point Process

Lafarge

Gimel’farb

Descombes

2010

IEEE Trans. Pattern Anal. Mach. Intell.

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“…One, that of Green (1995), involves a prior on the number of changepoints, and then a conditional prior on their 2 position. The other is based on modelling the changepoint process by a point process (Pievatolo and Green, 1998), and is a special case of a product-partion model (Hartigan, 1990). This indirectly specifies a joint prior on the number and position of the changepoints.…”

Section: Introductionmentioning

confidence: 99%

Exact and efficient Bayesian inference for multiple changepoint problems

2006

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Summary We demonstrate how to perform direct simulation from the posterior distribution of a class of multiple changepoint models where the number of changepoints is unknown. The class of models assumes independence between the posterior distribution of the parameters associated with segments of data between successive changepoints. This approach is based on the use of recursions, and is related to work on product partition models. The computational complexity of the approach is quadratic in the number of observations, but an approximate version, which introduces negligible error, and whose computational cost is roughly linear in the number of observations, is also possible. Our approach can be useful, for example within an MCMC algorithm, even when the independence assumptions do not hold. We demonstrate our approach on well-log data. Our method can cope with a range of models for this data, and exact simulation from the posterior distribution is possible in a matter of minutes.

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“…The marked point processes are stochastic tools which have been introduced in signal and image processing by Baddeley and Van Lieshout [21], and extended further in [22], [36], [37]. These models can be considered as an extension of conventional Markov Random Fields [38] such that random variables are associated not with signal values but with parametrical functions describing the signal.…”

Section: Marked Point Processesmentioning

confidence: 99%

A Marked Point Process for Modeling Lidar Waveforms

Mallet¹,

Lafarge

Roux

et al. 2010

IEEE Trans. on Image Process.

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Abstract-Lidar waveforms are 1D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based on a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence of parametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a Reversible Jump Markov Chain Monte Carlo sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported.

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Boundary Detection Through Dynamic Polygons

Cited by 35 publications

References 16 publications

Geometric Feature Extraction by a Multimarked Point Process

Geometric Feature Extraction by a Multimarked Point Process

Exact and efficient Bayesian inference for multiple changepoint problems

A Marked Point Process for Modeling Lidar Waveforms

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