Structural approach for building reconstruction from a single DSM

International audienceWe present a new approach for building reconstruction from a single Digital Surface Model (DSM). It treats buildings as an assemblage of simple urban structures extracted from a library of 3D parametric blocks (like a LEGO set). First, the 2D-supports of the urban structures are extracted either interactively or automatically. Then, 3D-blocks are placed on the 2D-supports using a Gibbs model which controls both the block assemblage and the fitting to data. A Bayesian decision finds the optimal configuration of 3D-blocks using a Markov Chain Monte Carlo sampler associated with original proposition kernels. This method has been validated on multiple data set in a wide-resolution interval such as 0.7 m satellite and 0.1 m aerial DSMs, and provides 3D representations on complex buildings and dense urban areas with various levels of detail

A Bayesian Approach on People Localization in Multicamera Systems

In this paper we introduce a Bayesian approach on multiple people localization in multi-camera systems. First, pixel-level features are extracted, which are based on physical properties of the 2-D image formation process, and provide information about the head and leg positions of the pedestrians, distinguishing standing and walking people, respectively. Then features from the multiple camera views are fused to create evidence for the location and height of people in the ground plane. This evidence accurately estimates the leg position even if either the area of interest is only a part of the scene, or the overlap ratio of the silhouettes from irrelevant outside motions with the monitored area is significant. Using this information we create a 3-D object configuration model in the real world. We also utilize a prior geometrical constraint, which describes the possible interactions between two pedestrians. To approximate the position of the people, we use a population of 3-D cylinder objects, which is realized by a Marked Point Process. The final configuration results are obtained by an iterative stochastic energy optimization algorithm. The proposed approach is evaluated on two publicly available datasets, and compared to a recent state-of-the-art technique. To obtain relevant quantitative test results, a 3-D Ground Truth annotation of the real pedestrian locations is prepared, while two different error metrics and various parameter settings are proposed and evaluated, showing the advantages of our proposed model

Moving Target Analysis in ISAR Image Sequences with a Multiframe Marked Point Process Model

In this paper we propose a Multiframe Marked Point Process model of line segments and point groups for automatic target structure extraction and tracking in Inverse Synthetic Aperture Radar (ISAR) image sequences. For the purpose of dealing with scatterer scintillations and high speckle noise in the ISAR frames, we obtain the resulting target sequence by an iterative optimization process, which simultaneously considers the observed image data and various prior geometric interaction constraints between the target appearances in the consecutive frames. A detailed quantitative evaluation is performed on 8 real ISAR image sequences of different carrier ship and airplane targets, using a test database containing 545 manually annotated frames

Transformation Model With Constraints for High Accuracy of 2D-3D Building Registration in Aerial Imagery

This paper proposes a novel rigorous transformation model for 2D-3D registration to address the difficult problem of obtaining a sufficient number of well-distributed ground control points (GCPs) in urban areas with tall buildings. The proposed model applies two types of geometric constraints, co-planarity and perpendicularity, to the conventional photogrammetric collinearity model. Both types of geometric information are directly obtained from geometric building structures, with which the geometric constraints are automatically created and combined into the conventional transformation model. A test field located in downtown Denver, Colorado, is used to evaluate the accuracy and reliability of the proposed method. The comparison analysis of the accuracy achieved by the proposed method and the conventional method is conducted. Experimental results demonstrated that: (1) the theoretical accuracy of the solved registration parameters can reach 0.47 pixels, whereas the other methods reach only 1.23 and 1.09 pixels; (2) the RMS values of 2D-3D registration achieved by the proposed model are only two pixels along the x and y directions, much smaller than the RMS values of the conventional model, which are approximately 10 pixels along the x and y directions. These results demonstrate that the proposed method is able to significantly improve the accuracy of 2D-3D registration with much fewer GCPs in urban areas with tall buildings