Mathias Ortner
Airbus Defence and Space
14 Papers
50 Citations
Mathias Ortner is an academic researcher from Airbus Defence and Space. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 3, co-authored 7 publications. Previous affiliations of Mathias Ortner include French Institute for Research in Computer Science and Automation.
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Papers
A Marked Point Process of Rectangles and Segments for Automatic Analysis of Digital Elevation Models
TL;DR: This work presents a framework for automatic feature extraction from images using stochastic geometry based on the superposition of a Process of segments and a process of rectangles, dedicated to the detection of linear networks of discontinuities and segmenting homogeneous areas.
Building extraction from digital elevation models
Mathias Ortner,Xavier Descombes,Josiane Zerubia +2 more
- 06 Apr 2003
TL;DR: This work defines a point process whose points represent buildings and defines a density for this point process which is split into two parts, consisting in an "internal field" that allows us to model the prior knowledge the authors have on patterns of buildings in urban areas and an external field that makes the point process fit the data.
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Adaptive Birth for the GLMB Filter for Object Tracking in Satellite Videos
Camilo Aguilar,Mathias Ortner,Josiane Zerubia +2 more
- 22 Aug 2022
TL;DR: In this article , an adaptive version of the generalized labeled multi-Bernoulli (GLMB) filter was proposed to estimate the initial velocities of new-born targets.
3
Point Processes of Segments and Rectangles for Building Extraction from Digital Elevation Models
Mathias Ortner,Xavier Descombes,Josiane Zerubia +2 more
- 14 May 2006
TL;DR: A new model based on stochastic geometry for extracting features from images, based on two interacting spatial point processes, which allows the incorporation of a prior knowledge on the interactions between features within the extraction process.
3
Enhanced GM-PHD Filter for Real Time Satellite Multi-Target Tracking
Camilo Aguilar,Mathias Ortner,Josiane Zerubia +2 more
- 04 Jun 2023
TL;DR: Ayana et al. as mentioned in this paper presented a real-time multi-object tracker using an enhanced version of the Gaussian mixture probability hypothesis density (GM-PHD) filter to track detections of a state-of-the-art CNN.
3