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Convolutional neural networks (CNN) have shown great results for object-detection tasks by learning texture and pattern-extraction filters. However, object-level interactions are harder to grasp without increasing the complexity of the architectures. On the other hand, Point Process models propose to solve the detection of the configuration of objects as a whole, allowing the factoring in of the image data and the objects' prior interactions. In this paper, we propose combining the information extracted by a CNN with priors on objects within a Markov Marked Point Process framework. We also propose a method to learn the parameters of this Energy-Based Model. We apply this model to the detection of small vehicles in optical satellite imagery, where the image information needs to be complemented with object interaction priors because of noise and small object sizes. [ABSTRACT FROM AUTHOR]

Estimating building layouts using exterior radar measurements is a challenging task involving electromagnetic modeling, many unknown parameters, and a limited number of sensors. We propose using the jump-diffusion algorithm as a powerful stochastic tool that can be used to estimate the number of walls, their unknown positions and other parameters. We develop an efficient iterative procedure that first uses low-frequency transmissions to obtain rough estimates of the building layout. These estimates are then used to initialize the estimation at higher frequencies, thus obtaining more accurate results; We show that with proper frequency selection, the building layout can be estimated using radar measurements at only a few frequencies. Numerical examples are used to illustrate the performance of the proposed algorithm in practical situations. [ABSTRACT FROM AUTHOR]

This work presents a framework for automatic feature extraction from images using stochastic geometry. Features in images are modeled as realizations of a spatial point process of geometrical shapes. This framework allows the incorporation of a priori knowledge on the spatial repartition of features. More specifically, we present a model based on the superposition of a process of segments and a process of rectangles. The former is dedicated to the detection of linear networks of discontinuities, whereas the latter aims at segmenting homogeneous areas. An energy is defined, favoring connections of segments, alignments of rectangles, and a relevant interaction between both types of objects. The estimation is performed by minimizing the energy using a simulated annealing algorithm. The proposed model is applied to the analysis of Digital Elevation Models (DEMs). These images are raster data representing the altimetry of a dense urban area. We present results on real data provided by the French National Geographic Institute (IGN) consisting in low-quality DEMs of various types. [ABSTRACT FROM AUTHOR]

We develop a sequential detector for the release of a biochemical substance, with potential applications in environmental security. The proposed detector provides online detection of the appearance of a biochemical source in realistic complex environments. To obtain optimal performance, we use an integrated approach combining statistical analysis of measurements given by an array of biochemical sensors with a physical model of the dispersion, amenable to arbitrary geometries and wind turbulence. We first focus on formulating a detector that is applicable in the presence of unknown source parameters (e.g., release time, intensity, and location). We then derive a bound on the expected delay before a false detection in order to select the threshold of the test. For a fixed false-alarm rate, we obtain the detection probability of a release as a function of its location and initial concentration. Numerical examples illustrate the applicability of our methods to real-world scenarios of an urban area and indoor ventilation duct. [ABSTRACT FROM AUTHOR]

Early detection and estimation of the spread of a biochemical contaminant are major issues in many applications, such as homeland security and pollution monitoring. We present an integrated approach combining the measurements given by an array of biochemical sensors with a physical model of the dispersion and statistical analysis to solve these problems and provide system performance measures. We approximate the dispersion model of a contaminant in a realistic environment through numerical simulations of reflected stochastic diffusions describing the microscopic transport phenomena due to wind and chemical diffusion and use the Feynmann-Kac formula. We consider arbitrary complex geometries and account for wind turbulence. Numerical examples are presented for two real-world scenarios: an urban area and an indoor ventilation duct. Localizing the dispersive sources is useful for decontamination purposes and estimation of the cloud evolution. To solve the associated inverse problem, we propose a Bayesian framework based on a random field that is particularly powerful for localizing multiple sources with small amounts of measurements [ABSTRACT FROM PUBLISHER]

This work presents an automatic algorithm for extracting vectorial land registers from altimetric data in dense urban areas. We focus on elementary shape extraction and propose a method that extracts rectangular buildings. The result is a vectorial land register that can be used, for instance, to perform precise roof shape estimation. Using a spatial point process framework, we model towns as configurations of and unknown number of rectangles. An energy is defined, which takes into account both low level information provided by the altimetry of the scene, and geometric knowledge about the disposition of buildings in towns. Estimation is done by minimizing the energy using simulated annealing. We use an MCMC sampler that is a combination of general Metropolis Hastings Green techniques and the Geyer and Møller algorithm for point process sampling. We define some original proposition kernels, such as birth or death in a neighborhood and define the energy with respect to an inhomogeneous Poisson point process. We present results on real data provided by the IGN (French National Geographic Institute). Results were obtained automatically. These results consist of configurations of rectangles describing a dense urban area. [ABSTRACT FROM AUTHOR]

Pushbroom cameras are widely used for earth observation applications. This sensor acquires 1D images over time and uses the straight motion of the satellite to sweep out a region of space and build a 2D image. The stability of the satellite is critical during the pushbroom acquisition process. Therefore its attitude is assumed to be constant over time. However, the recent manufacture of smaller and lighter satellites to reduce launching cost has weakened this assumption. Small oscillations of the satellite’s attitude can result in noticeable warps in images, and geolocation information is lost as the satellite does not capture what it ought to. Current solutions use inertial sensors to control the attitude and correct the images, but they are costly and of limited precision. As the warped images do contain information about attitude variations, we suggest using image registration to estimate them. We exploit the geometry of the focal plane and the stationary nature of the disturbances to recover undistorted images. We embed the estimation in a Bayesian framework where image registration, a prior on attitude variations and a radiometric correction model are fused to retrieve the motion of the satellite. We illustrate the performance of our algorithm on four satellite datasets. [ABSTRACT FROM AUTHOR]