StachnissLab

Research Interests

• Pattern recognition and image interpretation
• Machine Learning
• Deep Learning
• Markov Marked Point Processes
• Hierarchical image features
• Symmetries and repeated structures in images

Short CV

Susanne Wenzel is a postdoctoral researcher and teaching assistant at the Photogrammetry group. Besides she is a scientific coordinator at the Forschungszentrum Jülich.
In February 2018 she joined the Big Data Analytics Group at Institute of Neuroscience and Medicine, Structural and functional organisation of the brain (INM-1) as a scientific coordinator. In the summer term of 2018 she will give the lecture in Photogrammetry I before she finally moves to Forschungszentrum Jülich. From Oktober 2015 to February 2018 she was managing the genesis of a new Master program at IGG, entitled Geodetic Engineering, which has been started in October 2017.
Susanne finished her PhD in 2016 entitled “High-Level Facade Image Interpretation using Marked Point Processes” supervised by Wolfgang Förstner at University of Bonn. She studied Geodesy at the Technical University of Berlin and University of Bonn. Before studying, she passed the professional training for surveying technician at the Berlin Senate of Urban Development, Department for Geodesy and Geoinformation and worked there as a technical assistant.
In her research, she aims at the semantic interpretation of facade images, focusing on the combination of bottom-up image interpretation and high-level image interpretation methods from top-down, especially Markov marked point processes.

Detailed CV

Awards

• Faculty Teaching Award 2014
• Faculty Award for the best student in 2007 in Geodesy and Geoinformation
• Turbo-Preis 2007 of Society for Geodesy, Geoinformation and Land Management (DVW)

Teaching

• Lectures and Exercises Photogrammetrie I, SS18
• Lectures and Exercises Photogrammetrie II, WS18/19
• Exercises BA Photogrammetrie I and II since 2009
• Lectures and Exercises MA Photogrammetrie und Fernerkundung, WS13/14
• Exercises MA Photogrammetrie und Fernerkundung, WS12/13
• Lectures Photogrammetrie II, WS09/10
• Exercises 3D-Koordinatensysteme, WS07/08
• Exercises Projektive Geometrie, SS08

Supervision

• Katharina Franz, Ocean Eddy Identification and Tracking using Neural Networks, Master’s Thesis, 2017/2018 – in progress
• Anne Braakmann-Folgmann, Sea Surface Height Prediction using Recurrent Neural Networks, Master’s Thesis, 2017
• Anika Bettge, Deep Self-taught Learning for Remote Sensing Image Classification, Master’s Thesis, 2017
• Jana Kierdorf, Spektrales Clustering mittels Sparse Representation-basierten Graphen, Bachelorarbeit, 2017
• Martin Obersheimer, Untersuchung von Form-Deskriptoren als Merkmale für die Detektion der Stämme von Unkräutern und Nutzpflanzen, Bachelorarbeit, 2017
• Johannes Kinast, Untersuchung latenter Spuren auf variablen Spurenträgern mit hyperspektralen Bildgebungsverfahren, Bachelorarbeit, 2016
• Philipp Lottes, Bildbasierte Klassifikation von Zuckerrüben und Unkräutern für mobile Roboter, Masterarbeit, 2015
• Till Schubert, Investigation of Latent Traces Using Hyperspectral Imaging, Bachelor’s Thesis, SS 2015
• Mareike Flick, Localization using Open Street Map Data, Bachelor’s Thesis, SS 2014
• Katharina Franz, Bestimmung der Trajektorie des ATV-4 bei der Separation von der Ariane-5 Oberstufe aus einer Stereo-Bildsequenz, Bachelorarbeit, SS 2014
• Eva Börgens, Anke Sausen, Relative Orientierung aus Kreisen, Masterprojekt Projektive Geometrie und Statistik, SS 2013
• Annemarie Kunkel, Detektion von Beeren in Bildern zur Ableitung phänotypischer Merkmale, Masterprojekt, SS 2012 – WS 2012/2013
• Christiane Staat, Klassifikation und Detektion von Weinbeeren in Bildern über Dictionary Learning, Masterprojekt, SS 2012 – WS 2012/2013
• Philip Alexander Becker, 3D Rekonstruktion symmetrischer Objekte aus Tiefenbildern, Bachelorarbeit, SS 2012
• Bernd Uebbing, Untersuchung zur Nutzung wiederholter Strukturen für die 3D Rekonstruktion aus Einzelaufnahmen, Bachelorarbeit, 2011.
• Johannes Schneider, Untersuchung von Farbinvarianten für die Erzeugung von Merkmalsdeskriptoren, Bachelorarbeit, 2009.

Publications

2018

• L. Drees, R. Roscher, and S. Wenzel, “Archetypal Analysis for Sparse Representation-based Hyperspectral Sub-Pixel Quantification,” Photogrammetric Engineering & Remote Sensing, 2018.
  [BibTeX] [PDF]

  @Article{drees2018arxiv,
  author = {Drees, L. and Roscher, R. and Wenzel, S.},
  title = {Archetypal Analysis for Sparse Representation-based Hyperspectral Sub-Pixel Quantification},
  journal = {Photogrammetric Engineering \& Remote Sensing},
  year = {2018},
  note = {accepted},
  url = {https://arxiv.org/abs/1802.02813},
  }

• K. Franz, R. Roscher, A. Milioto, S. Wenzel, and J. Kusche, “Ocean Eddy Identification and Tracking using Neural Networks,” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2018.
  [BibTeX] [PDF]

  @InProceedings{franz2018ocean,
  author = {Franz, K. and Roscher, R. and Milioto, A. and Wenzel, S. and Kusche, J.},
  title = {Ocean Eddy Identification and Tracking using Neural Networks},
  booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
  year = {2018},
  note = {accepted},
  url = {https://arxiv.org/abs/arXiv:1803.07436},
  }

2017

• D. Bulatov, S. Wenzel, G. Häufel, and J. Meidow, “Chain-Wise Generalization of Road Nerworks Using Model Selection,” in ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017-06-08 2017, p. 59–66. doi:10.5194/isprs-annals-IV-1-W1-59-2017
  [BibTeX] [PDF]
  

  Streets are essential entities of urban terrain and their automatized extraction from airborne sensor data is cumbersome because of a complex interplay of geometric, topological and semantic aspects. Given a binary image, representing the road class, centerlines of road segments are extracted by means of skeletonization. The focus of this paper lies in a well-reasoned representation of these segments by means of geometric primitives, such as straight line segments as well as circle and ellipse arcs. We propose the fusion of raw segments based on similarity criteria; the output of this process are the so-called chains which better match to the intuitive perception of what a street is. Further, we propose a two-step approach for chain-wise generalization. First, the chain is pre-segmented using circlePeucker and finally, model selection is used to decide whether two neighboring segments should be fused to a new geometric entity. Thereby, we consider both variance-covariance analysis of residuals and model complexity. The results on a complex data-set with many traffic roundabouts indicate the benefits of the proposed procedure.

  @InProceedings{bulatov2017isprs,
  title = {Chain-Wise Generalization of Road Nerworks Using Model Selection},
  author = {Bulatov, D. and Wenzel, S. and H\"aufel, G. and Meidow, J.},
  booktitle = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
  year = {2017},
  pages = {59--66},
  volume = {IV-1/W1},
  abstract = {Streets are essential entities of urban terrain and their automatized extraction from airborne sensor data is cumbersome because of a complex interplay of geometric, topological and semantic aspects. Given a binary image, representing the road class, centerlines of road segments are extracted by means of skeletonization. The focus of this paper lies in a well-reasoned representation of these segments by means of geometric primitives, such as straight line segments as well as circle and ellipse arcs. We propose the fusion of raw segments based on similarity criteria; the output of this process are the so-called chains which better match to the intuitive perception of what a street is. Further, we propose a two-step approach for chain-wise generalization. First, the chain is pre-segmented using circlePeucker and finally, model selection is used to decide whether two neighboring segments should be fused to a new geometric entity. Thereby, we consider both variance-covariance analysis of residuals and model complexity. The results on a complex data-set with many traffic roundabouts indicate the benefits of the proposed procedure.},
  date = {2017-06-08},
  doi = {10.5194/isprs-annals-IV-1-W1-59-2017},
  url = {https://www.ipb.uni-bonn.de/pdfs/Bulaton2017Chain-Wise.pdf},
  }

• A. Bettge, R. Roscher, and S. Wenzel, “Deep self-taught learning for remote sensing image classification,” in Proc. Conf. on Big Data from Space, 2017. doi:10.2760/383579
  [BibTeX] [PDF]
  

  This paper addresses the land cover classification task for remote sensing images by deep self-taught learning. Our self-taught learning approach learns suitable feature representations of the input data using sparse representation and undercomplete dictionary learning. We propose a deep learning framework which extracts representations in multiple layers and use the output of the deepest layer as input to a classification algorithm. We evaluate our approach using a multispectral Landsat 5 TM image of a study area in the North of Novo Progresso (South America) and the Zurich Summer Data Set provided by the University of Zurich. Experiments indicate that features learned by a deep self-taught learning framework can be used for classification and improve the results compared to classification results using the original feature representation.

  @InProceedings{bettge2017bids,
  author = {Bettge, A. and Roscher, R. and Wenzel, S.},
  title = {Deep self-taught learning for remote sensing image classification},
  booktitle = {Proc. Conf. on Big Data from Space},
  year = {2017},
  abstract = {This paper addresses the land cover classification task for remote sensing images by deep self-taught learning. Our self-taught learning approach learns suitable feature representations of the input data using sparse representation and undercomplete dictionary learning. We propose a deep learning framework which extracts representations in multiple layers and use the output of the deepest layer as input to a classification algorithm. We evaluate our approach using a multispectral Landsat 5 TM image of a study area in the North of Novo Progresso (South America) and the Zurich Summer Data Set provided by the University of Zurich. Experiments indicate that features learned by a deep self-taught learning framework can be used for classification and improve the results compared to classification results using the original feature representation.},
  doi = {10.2760/383579},
  url = {https://publications.jrc.ec.europa.eu/repository/bitstream/JRC108361/jrc180361_procbids17.pdf},
  }

• A. Braakmann-Folgmann, R. Roscher, S. Wenzel, B. Uebbing, and J. Kusche, “Sea level anomaly prediction using recurrent neural networks,” in Proc. of the Conf. on Big Data from Space, 2017. doi:10.2760/383579
  [BibTeX] [PDF]
  

  Sea level change, one of the most dire impacts of anthropogenic global warming, will affect a large amount of the world’s population. However, sea level change is not uniform in time and space, and the skill of conventional prediction methods is limited due to the ocean’s internal variabi-lity on timescales from weeks to decades. Here we study the potential of neural network methods which have been used successfully in other applications, but rarely been applied for this task. We develop a combination of a convolutional neural network (CNN) and a recurrent neural network (RNN) to analyse both the spatial and the temporal evolution of sea level and to suggest an independent, accurate method to predict interannual sea level anomalies (SLA). We test our method for the northern and equatorial Pacific Ocean, using gridded altimeter-derived SLA data. We show that the used network designs outperform a simple regression and that adding a CNN improves the skill significantly. The predictions are stable over several years.

  @InProceedings{braakmann-folgmann2017bids,
  author = {Braakmann-Folgmann, A. and Roscher, R. and Wenzel, S. and Uebbing, B. and Kusche, J.},
  title = {Sea level anomaly prediction using recurrent neural networks},
  booktitle = {Proc. of the Conf. on Big Data from Space},
  year = {2017},
  abstract = {Sea level change, one of the most dire impacts of anthropogenic global warming, will affect a large amount of the world's population. However, sea level change is not uniform in time and space, and the skill of conventional prediction methods is limited due to the ocean's internal variabi-lity on timescales from weeks to decades. Here we study the potential of neural network methods which have been used successfully in other applications, but rarely been applied for this task. We develop a combination of a convolutional neural network (CNN) and a recurrent neural network (RNN) to analyse both the spatial and the temporal evolution of sea level and to suggest an independent, accurate method to predict interannual sea level anomalies (SLA). We test our method for the northern and equatorial Pacific Ocean, using gridded altimeter-derived SLA data. We show that the used network designs outperform a simple regression and that adding a CNN improves the skill significantly. The predictions are stable over several years.},
  doi = {10.2760/383579},
  url = {https://publications.jrc.ec.europa.eu/repository/bitstream/JRC108361/jrc180361_procbids17.pdf},
  }

• R. Roscher, L. Drees, and S. Wenzel, “Sparse representation-based archetypal graphs for spectral clustering,” in IEEE International Geoscience and Remote Sensing Symposium, 2017.
  [BibTeX] [PDF]

  @InProceedings{roscher2017igrss,
  author = {Roscher, R. and Drees, L. and Wenzel, S.},
  title = {Sparse representation-based archetypal graphs for spectral clustering},
  booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
  year = {2017},
  owner = {ribana},
  timestamp = {2017.12.08},
  url = {https://www.researchgate.net/publication/321680475_Sparse_representation-based_archetypal_graphs_for_spectral_clustering},
  }

2016

• R. Roscher, S. Wenzel, and B. Waske, “Discriminative Archetypal Self-taught Learning for Multispectral Landcover Classification,” in Proc. of Pattern Recogniton in Remote Sensing 2016 (PRRS), Workshop at ICPR; to appear in IEEE Xplore, 2016.
  [BibTeX] [PDF]
  

  Self-taught learning (STL) has become a promising paradigm to exploit unlabeled data for classification. The most commonly used approach to self-taught learning is sparse representation, in which it is assumed that each sample can be represented by a weighted linear combination of elements of a unlabeled dictionary. This paper proposes discriminative archetypal self-taught learning for the application of landcover classification, in which unlabeled discriminative archetypal samples are selected to build a powerful dictionary. Our main contribution is to present an approach which utilizes reversible jump Markov chain Monte Carlo method to jointly determine the best set of archetypes and the number of elements to build the dictionary. Experiments are conducted using synthetic data, a multi-spectral Landsat 7 image of a study area in the Ukraine and the Zurich benchmark data set comprising 20 multispectral Quickbird images. Our results confirm that the proposed approach can learn discriminative features for classification and show better classification results compared to self-taught learning with the original feature representation and compared to randomly initialized archetypal dictionaries.

  @InProceedings{roscher2016discriminative,
  title = {Discriminative Archetypal Self-taught Learning for Multispectral Landcover Classification},
  author = {Roscher, R. and Wenzel, S. and Waske, B.},
  booktitle = {Proc. of Pattern Recogniton in Remote Sensing 2016 (PRRS), Workshop at ICPR; to appear in IEEE Xplore},
  year = {2016},
  abstract = {Self-taught learning (STL) has become a promising paradigm to exploit unlabeled data for classification. The most commonly used approach to self-taught learning is sparse representation, in which it is assumed that each sample can be represented by a weighted linear combination of elements of a unlabeled dictionary. This paper proposes discriminative archetypal self-taught learning for the application of landcover classification, in which unlabeled discriminative archetypal samples are selected to build a powerful dictionary. Our main contribution is to present an approach which utilizes reversible jump Markov chain Monte Carlo method to jointly determine the best set of archetypes and the number of elements to build the dictionary. Experiments are conducted using synthetic data, a multi-spectral Landsat 7 image of a study area in the Ukraine and the Zurich benchmark data set comprising 20 multispectral Quickbird images. Our results confirm that the proposed approach can learn discriminative features for classification and show better classification results compared to self-taught learning with the original feature representation and compared to randomly initialized archetypal dictionaries.},
  url = {https://www.ipb.uni-bonn.de/pdfs/Roscher2016Discriminative.pdf},
  }

• T. Schubert, S. Wenzel, R. Roscher, and C. Stachniss, “Investigation of Latent Traces Using Infrared Reflectance Hyperspectral Imaging,” in ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, p. 97–102. doi:10.5194/isprs-annals-III-7-97-2016
  [BibTeX] [PDF]
  

  The detection of traces is a main task of forensic science. A potential method is hyperspectral imaging (HSI) from which we expect to capture more fluorescence effects than with common Forensic Light Sources (FLS). Specimen of blood, semen and saliva traces in several dilution steps are prepared on cardboard substrate. As our key result we successfully make latent traces visible up to highest available dilution (1:8000). We can attribute most of the detectability to interference of electromagnetic light with the water content of the traces in the Shortwave Infrared region of the spectrum. In a classification task we use several dimensionality reduction methods (PCA and LDA) in combination with a Maximum Likelihood (ML) classifier assuming normally distributed data. Random Forest builds a competitive approach. The classifiers retrieve the exact positions of labeled trace preparation up to highest dilution and determine posterior probabilities. By modeling the classification with a Markov Random Field we obtain smoothed results.

  @InProceedings{schubert2016investigation,
  title = {{Investigation of Latent Traces Using Infrared Reflectance Hyperspectral Imaging}},
  author = {Schubert, Till and Wenzel, Susanne and Roscher, Ribana and Stachniss, Cyrill},
  booktitle = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
  year = {2016},
  pages = {97--102},
  volume = {III-7},
  abstract = {The detection of traces is a main task of forensic science. A potential method is hyperspectral imaging (HSI) from which we expect to capture more fluorescence effects than with common Forensic Light Sources (FLS). Specimen of blood, semen and saliva traces in several dilution steps are prepared on cardboard substrate. As our key result we successfully make latent traces visible up to highest available dilution (1:8000). We can attribute most of the detectability to interference of electromagnetic light with the water content of the traces in the Shortwave Infrared region of the spectrum. In a classification task we use several dimensionality reduction methods (PCA and LDA) in combination with a Maximum Likelihood (ML) classifier assuming normally distributed data. Random Forest builds a competitive approach. The classifiers retrieve the exact positions of labeled trace preparation up to highest dilution and determine posterior probabilities. By modeling the classification with a Markov Random Field we obtain smoothed results.},
  doi = {10.5194/isprs-annals-III-7-97-2016},
  url = {https://www.ipb.uni-bonn.de/pdfs/Schubert2016Investigation.pdf},
  }

• S. Wenzel, “High-Level Facade Image Interpretation using Marked Point Processes,” PhD Thesis, 2016.
  [BibTeX] [PDF]
  

  In this thesis, we address facade image interpretation as one essential ingredient for the generation of high-detailed, semantic meaningful, three-dimensional city-models. Given a single rectified facade image, we detect relevant facade objects such as windows, entrances, and balconies, which yield a description of the image in terms of accurate position and size of these objects. Urban digital three-dimensional reconstruction and documentation is an active area of research with several potential applications, e.g., in the area of digital mapping for navigation, urban planning, emergency management, disaster control or the entertainment industry. A detailed building model which is not just a geometric object enriched with texture, allows for semantic requests as the number of floors or the location of balconies and entrances. Facade image interpretation is one essential step in order to yield such models. In this thesis, we propose the interpretation of facade images by combining evidence for the occurrence of individual object classes which we derive from data, and prior knowledge which guides the image interpretation in its entirety. We present a three-step procedure which generates features that are suited to describe relevant objects, learns a representation that is suited for object detection, and that enables the image interpretation using the results of object detection while incorporating prior knowledge about typical configurations of facade objects, which we learn from training data. According to these three sub-tasks, our major achievements are: We propose a novel method for facade image interpretation based on a marked point process. Therefor, we develop a model for the description of typical configurations of facade objects and propose an image interpretation system which combines evidence derived from data and prior knowledge about typical configurations of facade objects. In order to generate evidence from data, we propose a feature type which we call shapelets. They are scale invariant and provide large distinctiveness for facade objects. Segments of lines, arcs, and ellipses serve as basic features for the generation of shapelets. Therefor, we propose a novel line simplification approach which approximates given pixel-chains by a sequence of lines, circular, and elliptical arcs. Among others, it is based on an adaption to Douglas-Peucker’s algorithm, which is based on circles as basic geometric elements We evaluate each step separately. We show the effects of polyline segmentation and simplification on several images with comparable good or even better results, referring to a state-of-the-art algorithm, which proves their large distinctiveness for facade objects. Using shapelets we provide a reasonable classification performance on a challenging dataset, including intra-class variations, clutter, and scale changes. Finally, we show promising results for the facade interpretation system on several datasets and provide a qualitative evaluation which demonstrates the capability of complete and accurate detection of facade objects.

  @PhDThesis{wenzel2016high-level,
  title = {High-Level Facade Image Interpretation using Marked Point Processes},
  author = {Wenzel, Susanne},
  school = {Department of Photogrammetry, University of Bonn},
  year = {2016},
  abstract = {In this thesis, we address facade image interpretation as one essential ingredient for the generation of high-detailed, semantic meaningful, three-dimensional city-models. Given a single rectified facade image, we detect relevant facade objects such as windows, entrances, and balconies, which yield a description of the image in terms of accurate position and size of these objects. Urban digital three-dimensional reconstruction and documentation is an active area of research with several potential applications, e.g., in the area of digital mapping for navigation, urban planning, emergency management, disaster control or the entertainment industry. A detailed building model which is not just a geometric object enriched with texture, allows for semantic requests as the number of floors or the location of balconies and entrances. Facade image interpretation is one essential step in order to yield such models. In this thesis, we propose the interpretation of facade images by combining evidence for the occurrence of individual object classes which we derive from data, and prior knowledge which guides the image interpretation in its entirety. We present a three-step procedure which generates features that are suited to describe relevant objects, learns a representation that is suited for object detection, and that enables the image interpretation using the results of object detection while incorporating prior knowledge about typical configurations of facade objects, which we learn from training data. According to these three sub-tasks, our major achievements are: We propose a novel method for facade image interpretation based on a marked point process. Therefor, we develop a model for the description of typical configurations of facade objects and propose an image interpretation system which combines evidence derived from data and prior knowledge about typical configurations of facade objects. In order to generate evidence from data, we propose a feature type which we call shapelets. They are scale invariant and provide large distinctiveness for facade objects. Segments of lines, arcs, and ellipses serve as basic features for the generation of shapelets. Therefor, we propose a novel line simplification approach which approximates given pixel-chains by a sequence of lines, circular, and elliptical arcs. Among others, it is based on an adaption to Douglas-Peucker's algorithm, which is based on circles as basic geometric elements We evaluate each step separately. We show the effects of polyline segmentation and simplification on several images with comparable good or even better results, referring to a state-of-the-art algorithm, which proves their large distinctiveness for facade objects. Using shapelets we provide a reasonable classification performance on a challenging dataset, including intra-class variations, clutter, and scale changes. Finally, we show promising results for the facade interpretation system on several datasets and
  provide a qualitative evaluation which demonstrates the capability of complete and accurate detection of facade objects.},
  city = {Bonn},
  url = {https://hss.ulb.uni-bonn.de/2016/4412/4412.htm},
  }

• S. Wenzel and W. Förstner, “Facade Interpretation Using a Marked Point Process,” in ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, p. 363–370. doi:10.5194/isprs-annals-III-3-363-2016
  [BibTeX] [PDF]
  

  Our objective is the interpretation of facade images in a top-down manner, using a Markov marked point process formulated as a Gibbs process. Given single rectified facade images we aim at the accurate detection of relevant facade objects as windows and entrances, using prior knowledge about their possible configurations within facade images. We represent facade objects by a simplified rectangular object model and present an energy model which evaluates the agreement of a proposed configuration with the given image and the statistics about typical configurations which we learned from training data. We show promising results on different datasets and provide a quantitative evaluation, which demonstrates the capability of complete and accurate detection of facade objects.

  @InProceedings{wenzel2016facade,
  title = {{Facade Interpretation Using a Marked Point Process}},
  author = {Wenzel, Susanne and F{\" o}rstner, Wolfgang},
  booktitle = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
  year = {2016},
  pages = {363--370},
  volume = {III-3},
  abstract = {Our objective is the interpretation of facade images in a top-down manner, using a Markov marked point process formulated as a Gibbs process. Given single rectified facade images we aim at the accurate detection of relevant facade objects as windows and entrances, using prior knowledge about their possible configurations within facade images. We represent facade objects by a simplified rectangular object model and present an energy model which evaluates the agreement of a proposed configuration with the given image and the statistics about typical configurations which we learned from training data. We show promising results on different datasets and provide a quantitative evaluation, which demonstrates the capability of complete and accurate detection of facade objects.},
  doi = {10.5194/isprs-annals-III-3-363-2016},
  url = {https://www.ipb.uni-bonn.de/pdfs/Wenzel2016Facade.pdf},
  }

2013

• S. Wenzel and W. Förstner, “Finding Poly-Curves of Straight Line and Ellipse Segments in Images,” Photogrammetrie, Fernerkundung, Geoinformation (PFG), vol. 4, p. 297–308, 2013. doi:10.1127/1432-8364/2013/0178
  [BibTeX]
  

  Simplification of given polygons has attracted many researchers. Especially, finding circular and elliptical structures in images is relevant in many applications. Given pixel chains from edge detection, this paper proposes a method to segment them into straight line and ellipse segments. We propose an adaption of Douglas-Peucker’s polygon simplification algorithm using circle segments instead of straight line segments and partition the sequence of points instead the sequence of edges. It is robust and decreases the complexity of given polygons better than the original algorithm. In a second step, we further simplify the poly-curve by merging neighbouring segments to straight line and ellipse segments. Merging is based on the evaluation of variation of entropy for proposed geometric models, which turns out as a combination of hypothesis testing and model selection. We demonstrate the results of {\tt circlePeucker} as well as merging on several images of scenes with significant circular structures and compare them with the method of {\sc Patraucean} et al. (2012).

  @Article{wenzel2013finding,
  title = {Finding Poly-Curves of Straight Line and Ellipse Segments in Images},
  author = {Wenzel, Susanne and F\"orstner, Wolfgang},
  journal = {Photogrammetrie, Fernerkundung, Geoinformation (PFG)},
  year = {2013},
  pages = {297--308},
  volume = {4},
  abstract = {Simplification of given polygons has attracted many researchers. Especially, finding circular and elliptical structures in images is relevant in many applications. Given pixel chains from edge detection, this paper proposes a method to segment them into straight line and ellipse segments. We propose an adaption of Douglas-Peucker's polygon simplification algorithm using circle segments instead of straight line segments and partition the sequence of points instead the sequence of edges. It is robust and decreases the complexity of given polygons better than the original algorithm. In a second step, we further simplify the poly-curve by merging neighbouring segments to straight line and ellipse segments. Merging is based on the evaluation of variation of entropy for proposed geometric models, which turns out as a combination of hypothesis testing and model selection. We demonstrate the results of {\tt circlePeucker} as well as merging on several images of scenes with significant circular structures and compare them with the method of {\sc Patraucean} et al. (2012).},
  doi = {10.1127/1432-8364/2013/0178},
  file = {Technical Report:Wenzel2013Finding.pdf},
  }

• S. Wenzel and W. Förstner, “Finding Poly-Curves of Straight Line and Ellipse Segments in Images,” Department of Photogrammetry, University of Bonn, TR-IGG-P-2013-02, 2013.
  [BibTeX] [PDF]
  

  Simplification of given polygons has attracted many researchers. Especially, finding circular and elliptical structures in images is relevant in many applications. Given pixel chains from edge detection, this paper proposes a method to segment them into straight line and ellipse segments. We propose an adaption of Douglas-Peucker’s polygon simplification algorithm using circle segments instead of straight line segments and partition the sequence of points instead the sequence of edges. It is robust and decreases the complexity of given polygons better than the original algorithm. In a second step, we further simplify the poly-curve by merging neighbouring segments to straight line and ellipse segments. Merging is based on the evaluation of variation of entropy for proposed geometric models, which turns out as a combination of hypothesis testing and model selection. We demonstrate the results of {\tt circlePeucker} as well as merging on several images of scenes with significant circular structures and compare them with the method of {\sc Patraucean} et al. (2012).

  @TechReport{wenzel2013findingtr,
  title = {Finding Poly-Curves of Straight Line and Ellipse Segments in Images},
  author = {Wenzel, Susanne and F\"orstner, Wolfgang},
  institution = {Department of Photogrammetry, University of Bonn},
  year = {2013},
  month = {July},
  number = {TR-IGG-P-2013-02},
  abstract = {Simplification of given polygons has attracted many researchers. Especially, finding circular and elliptical structures in images is relevant in many applications. Given pixel chains from edge detection, this paper proposes a method to segment them into straight line and ellipse segments. We propose an adaption of Douglas-Peucker's polygon simplification algorithm using circle segments instead of straight line segments and partition the sequence of points instead the sequence of edges. It is robust and decreases the complexity of given polygons better than the original algorithm. In a second step, we further simplify the poly-curve by merging neighbouring segments to straight line and ellipse segments. Merging is based on the evaluation of variation of entropy for proposed geometric models, which turns out as a combination of hypothesis testing and model selection. We demonstrate the results of {\tt circlePeucker} as well as merging on several images of scenes with significant circular structures and compare them with the method of {\sc Patraucean} et al. (2012).},
  url = {https://www.ipb.uni-bonn.de/pdfs/Wenzel2013Finding.pdf},
  }

2012

• S. Wenzel and W. Förstner, “Learning a compositional representation for facade object categorization,” in ISPRS Annals of Photogrammetry, Remote Sensing and the Spatial Information Sciences; Proc. of 22nd Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS), 2012, p. 197–202. doi:10.5194/isprsannals-I-3-197-2012
  [BibTeX] [PDF]
  

  Our objective is the categorization of the most dominant objects in facade images, like windows, entrances and balconies. In order to execute an image interpretation of complex scenes we need an interaction between low level bottom-up feature detection and highlevel inference from top-down. A top-down approach would use results of a bottom-up detection step as evidence for some high-level inference of scene interpretation. We present a statistically founded object categorization procedure that is suited for bottom-up object detection. Instead of choosing a bag of features in advance and learning models based on these features, it is more natural to learn which features best describe the target object classes. Therefore we learn increasingly complex aggregates of line junctions in image sections from man-made scenes. We present a method for the classification of image sections by using the histogram of diverse types of line aggregates.

  @InProceedings{wenzel2012learning,
  title = {Learning a compositional representation for facade object categorization},
  author = {Wenzel, Susanne and F\"orstner, Wolfgang},
  booktitle = {ISPRS Annals of Photogrammetry, Remote Sensing and the Spatial Information Sciences; Proc. of 22nd Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS)},
  year = {2012},
  number = { 2012},
  pages = {197--202},
  volume = {I-3},
  abstract = {Our objective is the categorization of the most dominant objects in facade images, like windows, entrances and balconies. In order to execute an image interpretation of complex scenes we need an interaction between low level bottom-up feature detection and highlevel inference from top-down. A top-down approach would use results of a bottom-up detection step as evidence for some high-level inference of scene interpretation. We present a statistically founded object categorization procedure that is suited for bottom-up object detection. Instead of choosing a bag of features in advance and learning models based on these features, it is more natural to learn which features best describe the target object classes. Therefore we learn increasingly complex aggregates of line junctions in image sections from man-made scenes. We present a method for the classification of image sections by using the histogram of diverse types of line aggregates.},
  city = {Melbourne},
  doi = {10.5194/isprsannals-I-3-197-2012},
  proceeding = {ISPRS Annals of Photogrammetry, Remote Sensing and the Spatial Information Sciences; Proc. of 22nd Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS)},
  url = {https://www.ipb.uni-bonn.de/pdfs/Wenzel2012Learning.pdf},
  }

2010

• S. Wenzel and L. Hotz, “The Role of Sequences for Incremental Learning,” in ICAART 2010 – Proc. of the International Conf. on Agents and Artificial Intelligence, Valencia, Spain, 2010, p. 434–439.
  [BibTeX] [PDF]
  

  In this paper, we point out the role of sequences of samples for training an incremental learning method. We define characteristics of incremental learning methods to describe the influence of sample ordering on the performance of a learned model. We show the influence of sequence for two different types of incremental learning. One is aimed on learning structural models, the other on learning models to discriminate object classes. In both cases, we show the possibility to find good sequences before starting the training.

  @InProceedings{wenzel2010role,
  title = {The Role of Sequences for Incremental Learning},
  author = {Wenzel, Susanne and Hotz, Lothar},
  booktitle = {ICAART 2010 - Proc. of the International Conf. on Agents and Artificial Intelligence},
  year = {2010},
  address = {Valencia, Spain},
  editor = {Joaquim Filipe and Ana L. N. Fred and Bernadette Sharp},
  month = jan,
  pages = {434--439},
  publisher = {INSTICC Press},
  volume = {1},
  abstract = {In this paper, we point out the role of sequences of samples for training an incremental learning method. We define characteristics of incremental learning methods to describe the influence of sample ordering on the performance of a learned model. We show the influence of sequence for two different types of incremental learning. One is aimed on learning structural models, the other on learning models to discriminate object classes. In both cases, we show the possibility to find good sequences before starting the training.},
  isbn = {978-989-674-021-4},
  timestamp = {2011.01.18},
  url = {https://www.ipb.uni-bonn.de/pdfs/Wenzel2010Role.pdf},
  }

2009

• S. Wenzel and W. Förstner, “The Role of Sequences for Incremental Learning,” Department of Photogrammetry, University of Bonn, TR-IGG-P-2009-04, 2009.
  [BibTeX] [PDF]
  

  This report points out the role of sequences of samples for training an incremental learning method. We define characteristics of incremental learning methods to describe the influence of sample ordering on the performance of a learned model. Different types of experiments evaluate these properties for two different datasets and two different incremental learning methods. We show how to find sequences of classes for training just based on the data to get always best possible error rates. This is based on the estimation of Bayes error bounds.

  @TechReport{wenzel2009role,
  title = {The Role of Sequences for Incremental Learning},
  author = {Wenzel, Susanne and F\"orstner, Wolfgang},
  institution = {Department of Photogrammetry, University of Bonn},
  year = {2009},
  month = oct,
  number = {TR-IGG-P-2009-04},
  abstract = {This report points out the role of sequences of samples for training an incremental learning method. We define characteristics of incremental learning methods to describe the influence of sample ordering on the performance of a learned model. Different types of experiments evaluate these properties for two different datasets and two different incremental learning methods. We show how to find sequences of classes for training just based on the data to get always best possible error rates. This is based on the estimation of Bayes error bounds.},
  url = {https://www.ipb.uni-bonn.de/pdfs/Wenzel2009Role.pdf},
  }

2008

• S. Wenzel, M. Drauschke, and W. Förstner, “Detection of repeated structures in facade images,” Pattern Recognition and Image Analysis, vol. 18, iss. 3, p. 406–411, 2008. doi:10.1134/S1054661808030073
  [BibTeX] [PDF]
  

  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,
  title = {Detection of repeated structures in facade images},
  author = {Wenzel, Susanne and Drauschke, Martin and F\"orstner, Wolfgang},
  journal = {Pattern Recognition and Image Analysis},
  year = {2008},
  month = sep,
  number = {3},
  pages = {406--411},
  volume = {18},
  abstract = {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.},
  doi = {10.1134/S1054661808030073},
  url = {https://www.ipb.uni-bonn.de/pdfs/Wenzel2008Detection.pdf},
  }

• S. Wenzel and W. Förstner, “Semi-supervised incremental learning of hierarchical appearance models,” in 21st Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS), Beijing, China, 2008, p. 399–404 Part B3b-2.
  [BibTeX] [PDF]
  

  We propose an incremental learning scheme for learning a class hierarchy for objects typically occurring multiple in images. Given one example of an object that appears several times in the image, e.g. is part of a repetitive structure, we propose a method for identifying prototypes using an unsupervised clustering procedure. These prototypes are used for building a hierarchical appearance based model of the envisaged class in a supervised manner. For classification of new instances detected in new images we use linear subspace methods that combine discriminative and reconstructive properties. The used methods are chosen to be capable for an incremental update. We test our approach on facade images with repetitive windows and balconies. We use the learned object models to find new instances in other images, e. g. the neighbouring facade and update already learned models with the new instances.

  @InProceedings{wenzel2008semi,
  title = {Semi-supervised incremental learning of hierarchical appearance models},
  author = {Wenzel, Susanne and F\"orstner, Wolfgang},
  booktitle = {21st Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS)},
  year = {2008},
  address = {Beijing, China},
  pages = {399--404 Part B3b-2},
  abstract = {We propose an incremental learning scheme for learning a class hierarchy for objects typically occurring multiple in images. Given one example of an object that appears several times in the image, e.g. is part of a repetitive structure, we propose a method for identifying prototypes using an unsupervised clustering procedure. These prototypes are used for building a hierarchical appearance based model of the envisaged class in a supervised manner. For classification of new instances detected in new images we use linear subspace methods that combine discriminative and reconstructive properties. The used methods are chosen to be capable for an incremental update. We test our approach on facade images with repetitive windows and balconies. We use the learned object models to find new instances in other images, e. g. the neighbouring facade and update already learned models with the new instances.},
  url = {https://www.ipb.uni-bonn.de/pdfs/Wenzel2008Semi.pdf},
  }

2007

• S. Wenzel, “Spiegelung und Zuordnung der SIFT-Feature Deskriptoren für die Detektion von Symmetrien und wiederholten Strukturen in Bildern,” Department of Photogrammetry, University of Bonn, TR-IGG-P-2007-04, 2007.
  [BibTeX] [PDF]
  

  This report describes the details for mirroring the descriptors of the SIFT-features. We show how the mirrored versions are derived by simply resorting the descriptor elements. Furthermore, we describe the matching of features within an image. The peculiarity of this task is the search for more than one – the best – match within an single image. The presented methods are based on the work of (Wenzel2006,Detektion). After the introduction the functionallity of the SIFT-feature detector is drafted and the development of the descriptors is described in detail. The following sections describe the details of mirroring and matching the features. Dieser Bericht geht auf die Details zur Spiegelung von SIFT-Feature Deskritoren ein. Es wird gezeigt, wie durch einfaches Umsortieren der Elemente des Feature Deskriptors gespiegelte Versionen der Deskriptoren erlangt werden können. Des Weiteren wird erläutert, wie Features innerhalb eines Bildes zugeordnet werden können. Die Besonderheit dieser Aufgabenstellung liegt in der gesuchten Zuordnung nicht eines – des besten – Matches, sondern in der Zuordnung aller Matches in einem Bild. Die vorgestellten Methoden basieren auf (Wenzel2006,Detektion).

  @TechReport{wenzel2007spiegelung,
  title = {Spiegelung und Zuordnung der SIFT-Feature Deskriptoren f\"ur die Detektion von Symmetrien und wiederholten Strukturen in Bildern},
  author = {Wenzel, Susanne},
  institution = {Department of Photogrammetry, University of Bonn},
  year = {2007},
  month = aug,
  number = {TR-IGG-P-2007-04},
  abstract = {This report describes the details for mirroring the descriptors of the SIFT-features. We show how the mirrored versions are derived by simply resorting the descriptor elements. Furthermore, we describe the matching of features within an image. The peculiarity of this task is the search for more than one - the best - match within an single image. The presented methods are based on the work of (Wenzel2006,Detektion). After the introduction the functionallity of the SIFT-feature detector is drafted and the development of the descriptors is described in detail. The following sections describe the details of mirroring and matching the features. Dieser Bericht geht auf die Details zur Spiegelung von SIFT-Feature Deskritoren ein. Es wird gezeigt, wie durch einfaches Umsortieren der Elemente des Feature Deskriptors gespiegelte Versionen der Deskriptoren erlangt werden k\"onnen. Des Weiteren wird erl\"autert, wie Features innerhalb eines Bildes zugeordnet werden k\"onnen. Die Besonderheit dieser Aufgabenstellung liegt in der gesuchten Zuordnung nicht eines - des besten - Matches, sondern in der Zuordnung aller Matches in einem Bild. Die vorgestellten Methoden basieren auf (Wenzel2006,Detektion).},
  url = {https://www.ipb.uni-bonn.de/pdfs/Wenzel2007Spiegelung.pdf},
  }

• S. Wenzel, M. Drauschke, and W. 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, Muttenz, Basel, 2007, pp. 119-126.
  [BibTeX] [PDF]
  

  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,
  title = {Detektion wiederholter und symmetrischer Strukturen in Fassadenbildern},
  author = {Wenzel, Susanne and Drauschke, Martin and F\"orstner, Wolfgang},
  booktitle = {Publikationen der DGPF: Von der Medizintechnik bis zur Planetenforschung - Photogrammetrie und Fernerkundung f\"ur das 21. Jahrhundert},
  year = {2007},
  address = {Muttenz, Basel},
  editor = {Seyfert, Eckhardt},
  month = jun,
  pages = {119-126},
  publisher = {DGPF},
  volume = {16},
  abstract = {Regelm\"a{\ss}ige Strukturen und Symmetrien kennzeichnen viele Geb\"audefassaden oder Objekte im Umfeld von Geb\"auden. F\"ur die automatisierte Bildinterpretation weisen diese Strukturen auf k\"unstliche Objekte hin, f\"uhren aber auch zu Schwierigkeiten bei klassischen Bildzuordnungsverfahren. Die Suche und Gruppierung zusammengeh\"origer Merkmale kann daher sowohl zur Identifikation k\"unstlicher Objekte als auch zur Verbesserung von Zuordnungsverfahren dienen. F\"ur 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.},
  url = {https://www.ipb.uni-bonn.de/pdfs/Wenzel2007Detektion.pdf},
  }

• S. Wenzel, M. Drauschke, and W. Förstner, “Detection and Description of Repeated Structures in Rectified Facade Images,” Photogrammetrie, Fernerkundung, Geoinformation (PFG), vol. 7, p. 481–490, 2007.
  [BibTeX] [PDF]
  

  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,
  title = {Detection and Description of Repeated Structures in Rectified Facade Images},
  author = {Wenzel, Susanne and Drauschke, Martin and F\"orstner, Wolfgang},
  journal = {Photogrammetrie, Fernerkundung, Geoinformation (PFG)},
  year = {2007},
  pages = {481--490},
  volume = {7},
  abstract = {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.},
  url = {https://www.ipb.uni-bonn.de/pdfs/Wenzel2007Detectiona.pdf},
  }

• S. Wenzel, M. Drauschke, and W. Förstner, “Detection of repeated structures in facade images,” in Proc. of the OGRW-7-2007, 7th Open German/Russian Workshop on Pattern Recognition and Image Understanding. August 20-23, 2007. Ettlingen, Germany, 2007. doi:10.1134/S1054661808030073
  [BibTeX] [PDF]
  

  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,
  title = {Detection of repeated structures in facade images},
  author = {Wenzel, Susanne and Drauschke, Martin and F\"orstner, Wolfgang},
  booktitle = {Proc. of the OGRW-7-2007, 7th Open German/Russian Workshop on Pattern Recognition and Image Understanding. August 20-23, 2007. Ettlingen, Germany},
  year = {2007},
  abstract = {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.},
  doi = {10.1134/S1054661808030073},
  url = {https://www.ipb.uni-bonn.de/pdfs/Wenzel2007Detection.pdf},
  }

2006

• S. Wenzel, “Detektion wiederholter und symmetrischer Strukturen von Objekten in Bildern,” Diplomarbeit Master Thesis, 2006.
  [BibTeX] [PDF]
  

  Sich wiederholende bzw. symmetrische Strukturen sind Hinweise auf künstliche Objekte, führen aber auch zu Schwierigkeiten bei klassischen Bildzuordnungsverfahren. Die Suche und Gruppierung zusammengehöriger Features kann daher zur Identifikation künstlicher Objekte oder zur Verbesserung von Zuordnungsverfahren dienen. Darüber hinaus kann man aus einem Bild eines im Raum symmetrischen Objekts auf die 3D-Struktur dieses Objekts schließen. Die Diplomarbeit soll das von Loy und Eklundh auf der ECCV 2006 vorgestellte Verfahren zur Detektion symmetrischer und wiederholter Bildbereiche implementieren und hinsichtlich seiner Verwendbarkeit für photogrammetrische Gebäudeaufnahmen überprüfen. Insbesondere geht es um die Detektierbarkeit regelmäßiger Fassadenstrukturen in Abhängigkeit von ihrer Komplexität. Darüber hinaus ist zu klären, wie mehrfache Symmetrien identifiziert und ggf. für die 3D-Rekonstruktion des regelmä\ssigen Teils der Fassadenstruktur genutzt werden können.

  @MastersThesis{wenzel2006detektion,
  title = {Detektion wiederholter und symmetrischer Strukturen von Objekten in Bildern},
  author = {Wenzel, Susanne},
  school = {Institute of Photogrammetry, University of Bonn},
  year = {2006},
  note = {Betreuung: Prof. Dr.-Ing. Wolfgang F\"orstner, Dipl.-Inform. Martin Drauschke},
  type = {Diplomarbeit},
  abstract = {Sich wiederholende bzw. symmetrische Strukturen sind Hinweise auf k\"unstliche Objekte, f\"uhren aber auch zu Schwierigkeiten bei klassischen Bildzuordnungsverfahren. Die Suche und Gruppierung zusammengeh\"origer Features kann daher zur Identifikation k\"unstlicher Objekte oder zur Verbesserung von Zuordnungsverfahren dienen. Dar\"uber hinaus kann man aus einem Bild eines im Raum symmetrischen Objekts auf die 3D-Struktur dieses Objekts schlie{\ss}en. Die Diplomarbeit soll das von Loy und Eklundh auf der ECCV 2006 vorgestellte Verfahren zur Detektion symmetrischer und wiederholter Bildbereiche implementieren und hinsichtlich seiner Verwendbarkeit f\"ur photogrammetrische Geb\"audeaufnahmen \"uberpr\"ufen. Insbesondere geht es um die Detektierbarkeit regelm\"a{\ss}iger Fassadenstrukturen in Abh\"angigkeit von ihrer Komplexit\"at. Dar\"uber hinaus ist zu kl\"aren, wie mehrfache Symmetrien identifiziert und ggf. f\"ur die 3D-Rekonstruktion des regelm\"a\ssigen Teils der Fassadenstruktur genutzt werden k\"onnen.},
  city = {Bonn},
  url = {https://www.ipb.uni-bonn.de/pdfs/Wenzel2006Detektion.pdf},
  }