Ehemaliger Mitarbeiter des Institutes
Professur für Photogrammetrie
Nussallee 15
53115 Bonn


E-Mail:


Wissenschaftlicher Mitarbeiter

am Institut tätig

von 2005 bis 2009

.

Lebenslauf

.

Forschungsinteressen

  • Bildsegmentierung, Merkmalsextraktion, Merkmalsauswahl
  • Algorithmische, Digitale und Projektive Geometrie
  • Gebäudedetektion (aus Luft- und Fassadenbildern), Anwendungen in der Fernerkundung
.
.

Publikationen

2011

Martin Drauschke, "Ein hierarchischer Ansatz zur Interpretation von Gebäudeaufnahmen". Thesis at: Institute of Photogrammetry, University of Bonn. 2011.

Summary
The recent progress in 3D visualization of landscapes and urban spaces has increased the demand for very detailed 3D city models with semantics. Therefore, the interpretation of digital imagery is an important step for gaining information used to enrich building models or for updating them. Due to the amount of data, image interpretation should be done automatically as far as possible for a fast and affordable delivery of results. Interpreting building images means to derive a symbolic map for a given color image where each pixel is visualized by its semantic. This procedure is extremely difficult if no additional 3D information about the objects in the scene is used. Regarding buildings, their parts often appear too similar, especially w. r. t. their shape or the color of the representing pixels. Furthermore, the large variability among buildings w. r. t. their size, nature and style as well as the kind and number of facade parts and roof structures and their spatial arrangement. In the approach of this work, the capability of detecting objects and their parts in building images is analysed with a focus on the integration of object hierarchies. The relations between the objects and their parts are mapped into a hierarchy of segmented image regions which is further used to construct a Bayesian network. Features of these regions are determined, thus the Bayesian network can be formulated as a conditional one. The overall probability of this Conditional Bayesian Network can be efficiently determined as product of two terms. The first term is derived from classification results based on the observed region-specific features, the second term is derived from the co-occurrence of class targets defined by neighbored regions in the region hierarchy. The following methods are combined within this approach: segmentation of geometrically precise regions at several image scales and their hierarchical arrangement, choosing stable regions for further processes, comparison between automatically segmented image regions with manually labeled annotations, extraction and selection of appropriate features and classification of the regions. The stable regions of the hierarchical image segmentation are suitable for detecting complex objects and their parts. The relations of the object hierarchy are mostly mapped into the region hierarchy. The class-specific distributions of the region-specific features can be distinguished from each other and, therefore, can be used for scale-dependent classification of the stable regions. This is done by applying alternating decision trees, which have been extended from binary to multiclass classification. The results are used for initializing the states of the random variables of the Conditional Bayesian Network. A simple algorithm can be applied for propagating the information through the net because of its tree-like structure. The image interpretation yields slightly better results for region classification after applying the Bayesian network. The approach has been tested and evaluated on four data sets with a total of 600 images. Obtained results on image segmentation and region hierarchy construction are satisfying since 70 to 90% of all objects of interest are represented by at least one of the segmented regions, and the relations between the regions reflect the object hierarchy. On the benchmark dataset of Korč & Förstner (2009), the overall classification result of 54.9% is obtained when classifying the regions without considering the hierarchical relations and belonging to seven classes. But the three classes vegetation, building and window which appear in images more often than other classes are favored by the clasifier. After propagating the information through the Conditional Bayesian Network the classification rate is increased to 60.7% but the obvious differences regarding classification mistakes remain.
Zusammenfassung
Durch die Fortschritte bei der 3D- Visualisierung von Landschaften und urbanen Räumen in den vergangenen Jahren wächst die Nachfrage nach semantischen und detailgenauen 3D Stadtmodellen. Die Interpretation von Digitalbildern ist dabei ein wesentlicher Schritt zur Informationsgewinnung für die Verfeinerung und Aktualisierung der Gebäudemodelle. Wegen der großen Datenmenge muss diese Bildinterpretation soweit möglich automatisiert durchgeführt werden, um kostengünstig und zeitnah Ergebnisse liefern zu können. Bei der Interpretation eines Gebäudebildes wird das gegebene Farbbild in eine Klassifikationskarte überführt, in der die Bildpixel entsprechend ihrer Semantik eingefärbt werden. Dieser Vorgang ist wegen der fehlenden 3D-Information über das Gebäude und seine Umgebung sowie der starken Ähnlichkeiten zwischen einigen Gebäudeteilen besonders schwierig. Hinzu kommt noch die große Vielfalt der Gebäude in Bezug auf deren Größe, Beschaffenheit und Stil sowie in der Art und Anzahl der Fassadenbestandteile und Dachaufbauten und deren räumlicher Anordnung. Das in der vorliegenden Arbeit vorgestellte Verfahren untersucht die Leistungsfähigkeit der Erkennung von Objekten und Objektteilen in Gebäudeaufnahmen bei Verwendung der Bestandteilshierarchien. Diese werden in eine Hierarchie von segmentierten Bildregionen abgebildet und zur Konstruktion eines Bedingten Bayes Netzes verwendet. Die Gesamtwahrscheinlichkeit dieses Bedingten Bayes-Netzes wird effizient als Produkt von zwei Termen bestimmt, die einerseits durch die Klassifikation der Regionen auf der Basis regionenspezifischer Merkmale und andererseits aus den Klassenzugehörigkeiten von in der Regionenhierarchie benachbarten Regionen bestimmt werden. Für diesen Ansatz werden folgende Teilschritte zusammengeführt: die Segmentierung von geometrisch präzisen Regionen in mehreren Maßstabsebenen des Bildes und deren hierarchische Anordnung, die Auswahl stabiler Regionen, der Vergleich von Regionen mit manuell angefertigten Annotationen, die Extraktion und Selektion geeigneter Merkmale sowie die Klassifikation der Regionen. Die stabilen Regionen der hierarchischen Bildsegmentierung eignen sich gut zur Detektion von komplexen Objekten und ihren Bestandteilen und bilden die Relationen der Bestandteilshierarchie von Objekten in die Regionenhierarchie ab. Die klassenspezifischen Verteilungen der regionenspezifischen Merkmale unterscheiden sich und eignen sich für eine maßstabsabhängige Klassifikation der Regionen. Dazu wird die Methode der Alternierenden Entscheidungsbäume vom Zwei- auf den Mehrklassenfall verallgemeinert. Diese Klassifikationsergebnisse werden als initiale Belegung der Zufallsvariablen in das Bedingte Bayes-Netz integriert. Für die anschließende Inferenz der Information kann wegen der baumartigen Struktur des Bayes-Netzes ein sehr einfacher Algorithmus verwendet werden, der lediglich einen zweimaligen Durchlauf durch den Baum vorsieht . Die Bildinterpretation nach Anwendung des Bayes-Netzes liefert leicht verbesserte Ergebnisse für die Klassifikation der Regionen. Das Verfahren wurde auf insgesamt vier Datensätzen mit zusammen über 600 Bildern evaluiert. Bei der Segmentierung und Konstruktion der Regionenhierarchie werden sehr zufrieden stellende Ergebnisse erzielt, da zu 70 bis 90% der vorhandenen Objekte passende Regionen und Relationen zwischen den Regionen gefunden werden können. Auf dem Benchmark-Datensatz von Korč & Förstner (2009) erzielen wir bei der Klassifikation ohne Nutzung der Bestandteilshierarchie mit sieben Klassen einen akzeptablen Erfolg von 54.9%. Allerdings werden die drei besonders häufig vorkommenden Klassen Gebäude, Fenster und Vegetation extrem durch den Klassifikator bevorzugt. Durch die Propagierung der Information im Bedingten Bayes-Netz wird die Erfolgsrate auf 60.7% verbessert, aber die starken Unterschiede bei der Fehlklassifikationen bleiben bestehen. 

@phdthesis{Drauschke2011Ein,
  author = {Drauschke, Martin},
  title = {Ein hierarchischer Ansatz zur Interpretation von Geb\"audeaufnahmen},
  school = {Institute of Photogrammetry, University of Bonn},
  year = {2011}
}

2010

Michael Ying Yang and Wolfgang Förstner and Martin Drauschke, "Hierarchical Conditional Random Field for Multi-class Image Classification", In International Conference on Computer Vision Theory and Applications (VISSAPP)., pp. 464-469. 2010.

Multi-class image classification has made significant advances in recent years through the combination of local and global features. This paper proposes a novel approach called hierarchical conditional random field (HCRF) that explicitly models region adjacency graph and region hierarchy graph structure of an image. This allows to set up a joint and hierarchical model of local and global discriminative methods that augments conditional random field to a multi-layer model. Region hierarchy graph is based on a multi-scale watershed segmentation. 

@inproceedings{Yang2010Hierarchical,
  author = {Yang, Michael Ying and F\"orstner, Wolfgang and Drauschke, Martin},
  title = {Hierarchical Conditional Random Field for Multi-class Image Classification},
  booktitle = {International Conference on Computer Vision Theory and Applications (VISSAPP)},
  year = {2010},
  pages = {464--469}
}

2009

Martin Drauschke and Ribana Roscher and Thomas Läbe and Wolfgang Förstner, "Improving Image Segmentation using Multiple View Analysis", In Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms and Evaluatin (CMRT09)., pp. 211-216. 2009.

In our contribution, we improve image segmentation by integrating depth information from multi-view analysis. We assume the object surface in each region can be represented by a low order polynomial, and estimate the best fitting parameters of a plane using those points of the point cloud, which are mapped to the specific region. We can merge adjacent image regions, which cannot be distinguished geometrically. We demonstrate the approach for finding spatially planar regions on aerial images. Furthermore, we discuss the possibilities of extending of our approach towards segmenting terrestrial facade images. 

@inproceedings{Drauschke2009Improving,
  author = {Drauschke, Martin and Roscher, Ribana and L\"abe, Thomas and F\"orstner, Wolfgang},
  title = {Improving Image Segmentation using Multiple View Analysis},
  booktitle = {Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms and Evaluatin (CMRT09)},
  year = {2009},
  pages = {211-216}
}

Martin Drauschke, "An Irregular Pyramid for Multi-scale Analysis of Objects and their Parts", In 7th IAPR-TC-15 Workshop on Graph-based Representations in Pattern Recognition. Venice, Italy, pp. 293-303. 2009.

We present an irregular image pyramid which is derived from multi-scale analysis of segmented watershed regions. Our framework is based on the development of regions in the Gaussian scale-space, which is represented by a region hierarchy graph. Using this structure, we are able to determine geometrically precise borders of our segmented regions using a region focusing. In order to handle the complexity, we select only stable regions and regions resulting from a merging event, which enables us to keep the hierarchical structure of the regions. Using this framework, we are able to detect objects of various scales in an image. Finally, the hierarchical structure is used for describing these detected regions as aggregations of their parts. We investigate the usefulness of the regions for interpreting images showing building facades with parts like windows, balconies or entrances. 

@inproceedings{Drauschke2009Irregular,
  author = {Drauschke, Martin},
  title = {An Irregular Pyramid for Multi-scale Analysis of Objects and their Parts},
  booktitle = {7th IAPR-TC-15 Workshop on Graph-based Representations in Pattern Recognition},
  year = {2009},
  pages = {293--303},
  doi = {10.1007/978-3-642-02124-4_30}
}

Martin Drauschke and Wolfgang Förstner and Ansgar Brunn, "Multidodging: Ein effizienter Algorithmus zur automatischen Verbesserung von digitalisierten Luftbildern", In Publikationen der DGPF, Band 18: Zukunft mit Tradition. Jena, pp. 61-68. 2009.

Wir haben ein effizientes, automatisches Verfahren zur Verbesserung von digitalisierten Luftbildern entwickelt. Das Verfahren MULTIDODGING dient im Kontext der visuellen Aufbereitung von historischen Aufnahmen aus dem 2. Weltkrieg. Bei der Bildverbesserung mittels MULTIDODGING wird das eingescannte Bild zunächst in sich nicht überlappende rechteckige Bildausschnitte unterteilt. In jedem Bildausschnitt wird eine Histogrammverebnung durchgeführt, die im Allgemeinen zu einer Verstärkung des Kontrasts führt. Durch die regionale Veränderung des Bildes entstehen sichtbare Grenzen zwischen den Bildausschnitten, die durch eine Interpolation entfernt werden. In der Anwendung des bisherigen Verfahrens hat sich gezeigt, dass der Kontrast in vielen lokalen Stellen zu stark ist. Deshalb kann zum Abschluss die Spannweite der Grauwerte zusätzlich reduziert werden, wobei diese Kontrastanpassung regional aus den Gradienten im Bildausschnitt berechnet wird. Dieser Beitrag beschreibt und analysiert das Verfahren im Detail. 

@inproceedings{Drauschke2009Multidodging,
  author = {Drauschke, Martin and F\"orstner, Wolfgang and Brunn, Ansgar},
  title = {Multidodging: Ein effizienter Algorithmus zur automatischen Verbesserung von digitalisierten Luftbildern},
  booktitle = {Publikationen der DGPF, Band 18: Zukunft mit Tradition},
  year = {2009},
  pages = {61--68}
}

Martin Drauschke, "Documentation: Segmentation and Graph Construction of HMRF"(TR-IGG-P-2009-03) 2009.

This is a technical report for presenting a documentation on the segmentation and the graph construction of a Hierarchical Markov random eld (HMRF). The segmentation is based on multiscale analysis and watershed regions as presented in [Drauschke et al., 2006]. The region's development is tracked over the scales, which de nes a region hierarchy graph. This graph is used to improve the segmentation by reforming the regions geometrically more precisely. This work is taken from [Drauschke, 2009]. Furthermore, we determine a region adjacency graph from each image partition of all scales. The detected image regions, their adjacent regions and their hierarchical neighbors are saved into an xml- fille for a convenient output. 

@techreport{Drauschke2009Documentation,
  author = {Drauschke, Martin},
  title = {Documentation: Segmentation and Graph Construction of HMRF},
  year = {2009},
  number = {TR-IGG-P-2009-03}
}

2008

Susanne Wenzel and Martin Drauschke and Wolfgang Förstner, "Detection of repeated structures in facade images", Pattern Recognition and Image Analysis., September, 2008. Vol. 18(3), pp. 406-411. 2008.

We present a method for detecting repeated structures, which is applied on facade images for describing the regularity of their windows. Our approach finds and explicitly represents repetitive structures and thus gives initial representation of facades. No explicit notion of a window is used; thus, the method also appears to be able to identify other manmade structures, e.g., paths with regular tiles. A method for detection of dominant symmetries is adapted for detection of multiply repeated structures. A compact description of the repetitions is derived from the detected translations in the image by a heuristic search method and the criterion of the minimum description length. 

@article{Wenzel2008Detection,
  author = {Wenzel, Susanne and Drauschke, Martin and F\"orstner, Wolfgang},
  title = {Detection of repeated structures in facade images},
  journal = {Pattern Recognition and Image Analysis},
  year = {2008},
  volume = {18},
  number = {3},
  pages = {406--411},
  doi = {10.1134/S1054661808030073}
}

Martin Drauschke and Wolfgang Förstner, "Comparison of Adaboost and ADTboost for Feature Subset Selection", In PRIS 2008. Barcelona, Spain, pp. 113-122. 2008.

This paper addresses the problem of feature selection within classification processes. We present a comparison of a feature subset selection with respect to two boosting methods, Adaboost and ADTboost. In our evaluation, we have focused on three different criteria: the classification error and the efficiency of the process depending on the number of most appropriate features and the number of training samples. Therefore, we discuss both techniques and sketch their functionality, where we restrict both boosting approaches to linear weak classifiers. We propose a feature subset selection method, which we evaluate on
synthetic and on benchmark data sets. 

@inproceedings{Drauschke2008Comparison,
  author = {Drauschke, Martin and F\"orstner, Wolfgang},
  title = {Comparison of Adaboost and ADTboost for Feature Subset Selection},
  booktitle = {PRIS 2008},
  year = {2008},
  pages = {113--122}
}

Martin Drauschke and Wolfgang Förstner, "Selecting appropriate features for detecting buildings and building parts", In 21st Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS). Beijing, China, pp. 447-452 Part B3b-1. 2008.

The paper addresses the problem of feature selection during classification of image regions within the context of interpreting images showing highly structured objects such as buildings. We present a feature selection scheme that is connected with the classification framework Adaboost, cf. (Schapire and Singer, 1999). We constricted our weak learners on threshold classification on a single feature. Our experiments showed that the classification with Adaboost is based on relatively small subsets of features. Thus, we are able to find sets of appropriate features. We present our results on manually annotated and automatically segmented regions from facade images of the eTRIMS data base, where our focus were the object classes facade, roof, windows and window panes. 

@inproceedings{Drauschke2008Selecting,
  author = {Drauschke, Martin and F\"orstner, Wolfgang},
  title = {Selecting appropriate features for detecting buildings and building parts},
  booktitle = {21st Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS)},
  year = {2008},
  pages = {447--452 Part B3b-1}
}

Martin Drauschke, "Description of Stable Regions IPM", March, 2008.(TR-IGG-P-2008-03) 2008.

The Stable Regions Image Processing Module is a low-level region detector. It delivers image parts of interest without any further interpretation. These image parts are all regions of an image which do not change much over a certain range in scale space of the image. The output of this IPM is a list of polygons of any shape and their rectangular bounding boxes, which both are saved into an xml-file. 

@techreport{Drauschke2008Description,
  author = {Drauschke, Martin},
  title = {Description of Stable Regions IPM},
  year = {2008},
  number = {TR-IGG-P-2008-03}
}

Martin Drauschke, "Feature Subset Selection with Adaboost and ADTboost", March, 2008.(TR-IGG-P-2008-04) 2008.

This technical report presents feature subset selection methods for two boosting classi cation frameworks: Adaboost and ADTboost. 

@techreport{Drauschke2008Feature,
  author = {Drauschke, Martin},
  title = {Feature Subset Selection with Adaboost and ADTboost},
  year = {2008},
  number = {TR-IGG-P-2008-04}
}

Martin Drauschke, "Multi-class ADTboost", August, 2008.(TR-IGG-P-2008-06) 2008.

This technical report gives a short review on boosting with alternating decision trees (ADTboost), which has been proposed by Freund & Mason (1999) and refined by De Comite et al. (2001). This approach is designed for two-class problems, and we extend it towards multi-class classification. The advantage of a multi-class boosting algorithm is its usage in scene interpretation with various kinds of objects. In these cases, two-class approaches will lead to several one class versus background (the other classes) classifications, where we must solve unappropriate results like "always background" or "two or more valid classes" for a sample. 

@techreport{Drauschke2008Multi,
  author = {Drauschke, Martin},
  title = {Multi-class ADTboost},
  year = {2008},
  number = {TR-IGG-P-2008-06}
}

Martin Drauschke, "Verbesserung des Multi-Dodgings mittels bikubischer Interpolation"(TR-IGG-P-2008-07) 2008.

Aufgabenstellung: Digitalisierte 16-Bit-Luftbilder sollen automatisch verbessert werden. Dazu haben wir in (1) und (2) den Multi-Dodging-Ansatz vorgeschlagen. In diesem Verfahren wird ein Bild in sich nicht überlappende Ausschnitte (Patches) zerlegt. Dann wird in jedem dieser Bildausschnitte eine Histogrammverebnung durchgeführt. Da dieses Vorgehen die Patchgrenzen im verbesserten Bild hinterlässt, wurde abschließend zwischen den Patches bilinear interpoliert. In dieser Arbeit wird untersucht, ob die Verwendung einer bikubischen Interpolation an Stelle der bilinearen zu besseren Ergebnissen führt. 

@techreport{Drauschke2008Verbesserung,
  author = {Drauschke, Martin},
  title = {Verbesserung des Multi-Dodgings mittels bikubischer Interpolation},
  year = {2008},
  number = {TR-IGG-P-2008-07}
}

2007

Susanne Wenzel and Martin Drauschke and Wolfgang Förstner, "Detection and Description of Repeated Structures in Rectified Facade Images", Photogrammetrie, Fernerkundung, Geoinformation (PFG). Vol. 7, pp. 481-490. 2007.

We present a method for detecting repeated structures, which is applied on facade images for describing the regularity of their windows. Our approach finds and explicitly represents repetitive structures and thus gives initial representation of facades. No explicit notion of a window is used, thus the method also appears to be able to identify other man made structures, e.g. paths with regular tiles. A method for detection of dominant symmetries is adapted for detection of multiple repeated structures. A compact description of repetitions is derived from translations detected in an image by a heuristic search method and the model selection criterion of the minimum description length. 

@article{Wenzel2007Detection,
  author = {Wenzel, Susanne and Drauschke, Martin and F\"orstner, Wolfgang},
  title = {Detection and Description of Repeated Structures in Rectified Facade Images},
  journal = {Photogrammetrie, Fernerkundung, Geoinformation (PFG)},
  year = {2007},
  volume = {7},
  pages = {481--490}
}

Martin Drauschke and Ansgar Brunn and Kai Kulschewski and Wolfgang Förstner, "Automatic Dodging of Aerial Images", In Publikationen der DGPF: Von der Medizintechnik bis zur Planetenforschung - Photogrammetrie und Fernerkundung für das 21. Jahrhundert. Seyfert, Eckhardt (Eds.) Muttenz, Basel, June, 2007. Vol. 16, pp. 173-180. DGPF. 2007.

We present an automated approach for the dodging of images, with which we edit digital images as it is usually done with analogue images in dark-rooms.
Millions of aerial images of all battle fields were taken during the Second World War. They were intensively used, e.g. for the observation of military movements, the documentation of success and failure of military operations and further planning. Today, the information of these images supports the removal of explosives of the Second World War and the identi-fication of dangerous waste in the soil. In North Rhine-Westphalia, approximately 300.000 aerial images are scanned to handle the huge amount of available data efficiently. The scanning is done with a gray value depth of 12 bits and a pixel size of 21 mum to gain both, a high radiometric and a high geometric resolution of the images. Due to the photographic process used in the 1930s and 1940s and several reproductions, the digitized images are exposed locally very differently. Therefore, the images shall be improved by automated dodging.
Global approaches mostly returned unsatisfying results. Therefore, we present a new approach, which is based on local histogram equalization. Other methods as spreading the histogram or linear transformations of the histogram manipulate the images either too much or not enough. For the implementation of our approach, we focus not only on the quality of the resulting images, but also on robustness and performance of the algorithm. Thus, the technique can also be used for other applications concerning image improvements. 

@inproceedings{Drauschke2007Automatic,
  author = {Drauschke, Martin and Brunn, Ansgar and Kulschewski, Kai and F\"orstner, Wolfgang},
  editor = {Seyfert, Eckhardt},
  title = {Automatic Dodging of Aerial Images},
  booktitle = {Publikationen der DGPF: Von der Medizintechnik bis zur Planetenforschung - Photogrammetrie und Fernerkundung f\"ur das 21. Jahrhundert},
  publisher = {DGPF},
  year = {2007},
  volume = {16},
  pages = {173--180}
}

Susanne Wenzel and Martin Drauschke and Wolfgang Förstner, "Detection of repeated structures in facade images", In Proceedings of the OGRW-7-2007, 7th Open German/Russian Workshop on Pattern Recognition and Image Understanding. August 20-23, 2007. Ettlingen, Germany. 2007.

We present a method for detecting repeated structures, which is applied on facade images for describing the regularity of their windows. Our approach finds and explicitly represents repetitive structures and thus gives initial representation of facades. No explicit notion of a window is used, thus the method also appears to be able to identify other man made structures, e.g. paths with regular tiles. A method for detection of dominant symmetries is adapted for detection of multiply repeated structures. A compact description of the repetitions is derived from the detected translations in the image by a heuristic search method and the criterion of the minimum description length. 

@inproceedings{Wenzel2007Detectiona,
  author = {Wenzel, Susanne and Drauschke, Martin and F\"orstner, Wolfgang},
  title = {Detection of repeated structures in facade images},
  booktitle = {Proceedings of the OGRW-7-2007, 7th Open German/Russian Workshop on Pattern Recognition and Image Understanding. August 20-23, 2007. Ettlingen, Germany},
  year = {2007},
  doi = {10.1134/S1054661808030073}
}

Susanne Wenzel and Martin Drauschke and Wolfgang Förstner, "Detektion wiederholter und symmetrischer Strukturen in Fassadenbildern", In Publikationen der DGPF: Von der Medizintechnik bis zur Planetenforschung - Photogrammetrie und Fernerkundung für das 21. Jahrhundert. Seyfert, Eckhardt (Eds.) Muttenz, Basel, June, 2007. Vol. 16, pp. 119-126. DGPF. 2007.

Regelmäßige Strukturen und Symmetrien kennzeichnen viele Gebäudefassaden oder Objekte im Umfeld von Gebäuden. Für die automatisierte Bildinterpretation weisen diese Strukturen auf künstliche Objekte hin, führen aber auch zu Schwierigkeiten bei klassischen Bildzuordnungsverfahren. Die Suche und Gruppierung zusammengehöriger Merkmale kann daher sowohl zur Identifikation künstlicher Objekte als auch zur Verbesserung von Zuordnungsverfahren dienen. Für die Analyse von entzerrten Fassadenaufnahmen haben wir das Verfahren von [LOY 2006] zur Detektion symmetrischer Bildstrukturen zu einem Verfahren zur Detektion verschiedener, sich wiederholender Bildstrukturen erweitert und aus den detektierten wiederholten Objekten eine minimale Beschreibung der Struktur der Fassadenelemente in Form von achsenparallelen Basiselementen abgeleitet. 

@inproceedings{Wenzel2007Detektion,
  author = {Wenzel, Susanne and Drauschke, Martin and F\"orstner, Wolfgang},
  editor = {Seyfert, Eckhardt},
  title = {Detektion wiederholter und symmetrischer Strukturen in Fassadenbildern},
  booktitle = {Publikationen der DGPF: Von der Medizintechnik bis zur Planetenforschung - Photogrammetrie und Fernerkundung f\"ur das 21. Jahrhundert},
  publisher = {DGPF},
  year = {2007},
  volume = {16},
  pages = {119-126}
}

2006

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.

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.

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}
}

Martin Drauschke, "Automatisches Dodging von Luftbildern"(TR-IGG-P-2006-01) 2006.

Das Problem stellt sich wie folgt dar: Die Luftbilder wurden mit einem Vexcel-Scanner digitalisiert und als 16-Bit-Bilder abgespeichert. Die Bilder sollen automatisch nachbereitet werden. 

@techreport{Drauschke2006Automatisches,
  author = {Drauschke, Martin},
  title = {Automatisches Dodging von Luftbildern},
  year = {2006},
  number = {TR-IGG-P-2006-01}
}
.

Weitere Publikationen

  • Tiago Mauricio Francoy, Dieter Wittmann, Volker Steinhage, Martin Drauschke, Stephan Müller, D. R. Cunha, A. M. Nascimento, V. L. C. Figueiredo, Z. L. P. Simoes, David de Jong, M. C. Arias, Lionel Segui Goncalves
    Morphometric and Genetic Changes in a Population of Apis mellifera after 34 years of Africanization. In: Genetics and Molecular Research, Vol. 8 (2), pp. 709-717, 2009.
  • Tiago Mauricio Francoy, Dieter Wittmann, Martin Drauschke, Stephan Müller, Volker Steinhage, Marcela A. F. Bezerra-Laure, David de Jong, Lionel Segui Goncalves
    Identification of Africanized honey bees through wing morphometrics: two fast and efficient procedures. In: Apidologie, Vol. 39 (5), pp. 488-494, 2008.
  • Martin Drauschke, Volker Steinhage, Artur Pogoda de la Vega, Stephan Müller, Tiago Mauricio Francoy, Dieter Wittmann
    Reliable Biometrical Analysis in Biodiversity Information Systems. Pattern Recognition in Information Systems (PRIS 2007), pp. 25-36, Funchal, Portugal, 2007.
    bibtex
  • David Seim, Stephan Müller, Martin Drauschke, Volker Steinhage, Stefan Schröder
    Management and Visualization of Biodiversity Knowledge. Proceedings of the 20th International Conference on Informatics for Environmental Protection (EnviroInfo 2006), pp. 429-432, Graz, Austria, 2006.
    bibtex
  • Martin Drauschke
    Implementierung von J?ABIS: Ein plattformunabhängiges System zur Mustererkennung in biometrischen Daten. Thesis (Diplomarbeit), Supervisors: Volker Steinhage and Rainer Manthey, Institute of Computer Science III, University of Bonn, 2005.
    bibtex
.

Supervision

  • Thorsten Volkmann
    Segmentierung und Klassifikation von Oozytenbildern. Bachelorarbeit, 2009.
  • Laura Jensen
    Automatische Detektion von Bombentrichtern. Bachelorarbeit, 2008.
  • Kerstin Herms
    Exploration des Skalenraues bezüglich der Gebäudeextraktion in terrestrischen Farbbildern. Diplomarbeit, 2007.
  • Susanne Wenzel
    Detektion wiederholter und symmetrischer Strukturen von Objekten in Bildern. Diplomarbeit, 2006.
  • Jan Thielmann
    Entwurf und Evaluierung eines Verfahrens zur Detektion wiederholter Bildstrukturen. Diplomarbeit, 2006.
  • Betreute Studentische / Wissenschaftliche Hilfskräfte:
    Kerstin Herms, Laura Jensen, Frank Münster, Marco Pilger, Marisa Röder-Sorge, Ribana Roscher, Jörg Schittenhelm, Cornelia Schmitz, Rebekka Schultz, Susanne Wenzel
.

Lehre


.
.