Martin Drauschke and Hanns-Florian Schuster and Wolfgang Förstner, "Detectibility of Buildings in Aerial Images over Scale Space", In Symposium of ISPRS Commission III: Photogrammetric Computer Vision. Wolfgang Förstner and Richard Steffen (Eds.) Bonn, September, 2006. Vol. XXXVI(Part 3), pp. 7-12. ISPRS. 2006.

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Automatic scene interpretation of aerial images is a major purpose of photogrammetry. Therefore, we want to improve building detection by exploring the "life-time" of stable and relevant image features in scale space. We use watersheds for feature extraction to gain a topologically consistent map. We will show that characteristic features for building detection can be found in all considered scales, so that no optimal scale can be selected for building recognition. Nevertheless, many of these features "live" in a wide scale interval, so that a combination of a small number of scales can be used for automatic building detection.

@inproceedings{Drauschke2006Detectibility,
  author = {Drauschke, Martin and Schuster, Hanns-Florian and F\"orstner, Wolfgang},
  editor = {Wolfgang F\"orstner and Richard Steffen},
  title = {Detectibility of Buildings in Aerial Images over Scale Space},
  booktitle = {Symposium of ISPRS Commission III: Photogrammetric Computer Vision},
  publisher = {ISPRS},
  year = {2006},
  volume = {XXXVI},
  number = {Part 3},
  pages = {7--12}
}

Martin Drauschke and Hanns-Florian Schuster and Wolfgang Förstner, "Stabilität von Regionen im Skalenraum", In Publikationen der DGPF: Geoinformatik und Erdbeobachtung. Eckhardt Seyfert (Eds.) Berlin, Septermber, 2006. Vol. 15, pp. 29-36. DGPF. 2006.

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Für die automatische Erfassung von Gebäuden aus Luftbildern ist es nützlich, Bildstrukturen im Skalenraum, d. h. über mehrere Auflösungsstufen zu beobachten, um für die Objekterkennung hinderliche Details ausblenden zu können. Große Bedeutung messen wir dabei den homogenen Regionen sowie deren Nachbarschaften zu. Regionen betrachten wir als stabil, wenn sie über einen mehrere Skalenstufen invariant bleiben. Sie haben spezielle Eigenschaften: Beim Vergrössern der Skala verschmelzen benachbarte Regionen, wobei eine Region immer vollständig in der anderen aufgeht. Diese speziellen Eigenschaft erleichtert das Bestimmen der Nachbarschaften in einer vorgegeben Skala, denn der Regionennachbarschaftsgraph (RNG) muss nur einmal auf der untersten Ebene des Skalenraums berechnet werden. Die RNGs in den anderen Ebenen können leicht aus der untersten Ebene berechnet werden.

@inproceedings{Drauschke2006Stabilitat,
  author = {Drauschke, Martin and Schuster, Hanns-Florian and F\"orstner, Wolfgang},
  editor = {Eckhardt Seyfert},
  title = {Stabilit\"at von Regionen im Skalenraum},
  booktitle = {Publikationen der DGPF: Geoinformatik und Erdbeobachtung},
  publisher = {DGPF},
  year = {2006},
  volume = {15},
  pages = {29--36}
}

Jochen Meidow and Hanns-Florian Schuster, "Voxel-based Quality Evaluation of Photogrammetic Buildingacquisitions" 2005.

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Automatic quality evaluation of photogrammetric building acquisitions is important to realize deficiencies of acquisition approaches, tocompare different acquisitions approaches and to check the keeping of contractual specifications. For the decision-makers a procedure will be suggested taking a few, good interpretable quality measures into account. Therefore, useful quality measures have to be identifiedby the formulation of criteria. These quantities can be derived from the comparison of a test data set and a reference data set capturing the same scene. The acquired topology is usually uncertain as for instance two adjacent buildings may be acquired as one building ortwo buildings. Thus a screening of the registered area is suggested to compute the quantities. The approach is independent of the used acquisition method. For the application of large data sets the corresponding data structures will be explained. In experimental tests thebuildings registered by two commercial acquisition systems will be compared by the quality measures determined in 2D and 3D.

@inproceedings{Meidow2005Voxel,
  author = {Meidow, Jochen and Schuster, Hanns-Florian},
  title = {Voxel-based Quality Evaluation of Photogrammetic Buildingacquisitions},
  year = {2005}
}

Hanns-Florian Schuster, "Detection of man-made-objects based on spatial aggregations"(TR-IGG-P-2006-01) 2005.

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This paper presents a method for detecting complex man-made-objects in images. The detection model is a bayesian net that aggregates cliques of image regions which may cover a complex object. Observable attributes of the regions are derived from a rich symbolic image description containing points, lines and regions as basic features including their relations. The model captures the dependency of the region aggregates on the features and their relations with respect to observability due to occlusions and to perspective deformations. Cliques are classified using MAP estimation. Up to now, the model captures cliques with one, two and three regions which is sufficient for detecting polyhedral objects. The model allows to detect and locate multiple appearances of object classes. The joint distribution of the Bayesian net is determined in a supervised learning step based on images with annotated regions. The method is realized and demonstrated for the detection of building roofs in aerial images.

@techreport{Schuster2005Detection,
  author = {Schuster,Hanns-Florian},
  title = {Detection of man-made-objects based on spatial aggregations},
  year = {2005},
  number = {TR-IGG-P-2006-01}
}

Hanns-Florian Schuster, "Segmentation Of LIDAR Data Using The Tensor Voting Framework", In Proc. 20th ISPRS Congress, Istanbul, Turkey. Istanbul, Turkey, pp. 1073-1078. 2004.

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We present an investigation on the use of Tensor Voting for categorizing LIDAR data into outliers, line elements (e.g. high-voltage power lines), surface patches (e.g. roofs) and volumetric elements (e.g. vegetation). The Reconstruction of man-made objects is a main task of photogrammetry. With the increasing quality and availability of LIDAR sensors, range data is becoming more and more important. With LIDAR sensors it is possible to quickly aquire huge amounts of data. But in contrast to classical systems, where the measurement points are chosen by an operator, the data points do not explicitly correspond to meaningful points of the object, i.e. edges, corners, junctions. To extract these features it is necessary to segment the data into homogeneous regions wich can be processed afterwards. Our approach consists of a two step segmentation. The first one uses the Tensor Voting algorithm. It encodes every data point as a particle which sends out a vector field. This can be used to categorize the pointness, edgeness and surfaceness of the data points. After the categorization of the given LIDAR data points also the regions between the data points are rated. Meaningful regions like edges and junctions, given by the inherent structure of the data, are extracted. In a second step the so labeled points are merged due to a similarity constraint. This similarity constraint is based on a minimum description length principle, encoding and comparing different geometrical models. The output of this segmentation consists of non overlapping geometric objects in three dimensional space. The aproach is evaluated with some examples of Lidar data.

@inproceedings{Schuster2004Segmentation,
  author = {Schuster, Hanns-Florian},
  title = {Segmentation Of LIDAR Data Using The Tensor Voting Framework},
  booktitle = {Proc. 20th ISPRS Congress, Istanbul, Turkey},
  year = {2004},
  pages = {1073--1078}
}

Hanns-Florian Schuster and Wolfgang Förstner, "Segmentierung, Rekonstruktion und Datenfusion bei der Objekterfassung mit Entfernungsdaten - ein Überblick", In Proceedings 2. Oldenburger 3D-Tage. Oldenburg 2003.

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Mit dem Aufkommen von flächig erfaßten Entfernungsdaten im Vermessungswesen steht ein Paradigmenwechsel in der Auswertung und Verarbeitung dieser Daten an, vergleichbar dem Übergang von der analytischen zur digitalen Photogrammetrie mit der Verfügbarkeit digitaler bzw. digitalisierter Bilder. Der vorliegende Beitrag gibt einen Überblick über Verfahren zur Fusion und Segmentierung von Entfernungsdaten und verdeutlicht Potentiale zur weiteren Automatisierung

@inproceedings{Schuster2003Segmentierung,
  author = {Schuster, Hanns-Florian and F\"orstner, Wolfgang},
  title = {Segmentierung, Rekonstruktion und Datenfusion bei der Objekterfassung mit Entfernungsdaten - ein \"Uberblick},
  booktitle = {Proceedings 2. Oldenburger 3D-Tage},
  year = {2003}
}

Hanns-Florian Schuster, "Bildsegmentierung mit stochastischen Bildpyramiden". Thesis at: Institute of Photogrammetry, University of Bonn. 2002.

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Die Bildsegmentierung ist ein unverzichtbarer Schritt zur Vorverarbeitung von Bildern für die Objekterkennung. Die Stochastische Bildpyramide ist ein hierarchischer Ansatz der Segmentierung, der auf einer durch einen Zufallsprozess gesteuerten, unregelmäßigen Pyramide beruht. Aufgabe des Diplomanden ist es, die theoretischen Grundlagen der stochastischen Bildpyramide aufzuarbeiten und zu untersuchen, in wieweit sich diese Struktur in Verbindung mit etablierten Segmentierungsalgorithmen zur Bildsegmentierung einsetzen läßt.

@mastersthesis{Schuster2002Bildsegmentierung,
  author = {Schuster, Hanns-Florian},
  title = {Bildsegmentierung mit stochastischen Bildpyramiden},
  school = {Institute of Photogrammetry, University of Bonn},
  year = {2002},
  note = {Betreuung: Prof. Dr.-Ing. Wolfgang F\"orstner, Dr.-Ing. Ansgar Brunn}
}