Extracting buildings from aerial images using hierachical aggregation in 2D and 3D
Computer Vision and Image Understanding
Automatic object extraction from aerial imagery—a survey focusing on buildings
Computer Vision and Image Understanding
A Gibbs Point Process for Road Extraction from Remotely Sensed Images
International Journal of Computer Vision
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Spatial Statistics for Tumor Cell Counting and Classification
Proceedings of the 31st DAGM Symposium on Pattern Recognition
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Crowd detection with a multiview sampler
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Road surface marking classification based on a hierarchical markov model
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
Detection of soldering defects in Printed Circuit Boards with Hierarchical Marked Point Processes
Pattern Recognition Letters
Detecting parametric objects in large scenes by Monte Carlo sampling
International Journal of Computer Vision
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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, while the latter aims at segmenting homogeneous areas. An energy is defined, favoring connections of segments, alignments of rectangles, as well as 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 IGN (French National Geographic Institute) consisting in low quality DEMs of various types.