@TECHREPORT{yang*10:plane, author = {Yang, M. Y., Foerstner, W.}, title = {{Plane Detection in Point Cloud Data}}, institution = {Department of Photogrammetry, University of Bonn}, year = {2010}, number = {TR-IGG-P-2010-01 }

@inproceedings{LNCS58150305, editor = {Mario Fritz and Bernt Schiele and Justus H. Piater}, booktitle = {Computer Vision Systems}, publisher = {Springer}, location = {Heidelberg}, series = {Lecture Notes in Computer Science}, volume = {5815}, year = {2009}, isbn = {978-3-642-04666-7}, author = {Timo Dickscheid and Wolfgang F\"{o}rstner}, title = {Evaluating the Suitability of Feature Detectors for Automatic Image Orientation Systems}, pages = {305--314} }

@inproceedings{drauschke*20:multidodging:, author = "Drauschke, Martin and Förstner, Wolfgang and Brunn, Ansgar", title = "Multidodging: Ein effizienter Algorithmus zur automatischen Verbesserung von digitalisierten Luftbildern", year = "20", pages = "61-68", city = "Bonn", proceeding = "Publikationen der DGPF, Band 18: Zukunft mit Tradition",}

@inproceedings{drauschke*20:improving, author = "Drauschke, Martin and Roscher, Ribana and Läbe, Thomas and Förstner, Wolfgang", title = "Improving Image Segmentation using Multiple View Analysis", year = "20", pages = "211-216", city = "Paris", proceeding = "Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms and Evaluatin (CMRT09)",}

Photogrammetry has significantly been influenced by its two neigbouring fields, namely Computer Vision and Remote Sensing. Today, Photogrammetry has been become a part of Remote Sensing. The paper reflects its growing relations with Computer Vision, based on a more than 25 years experience of the author with the fascinating field between cognitive, natural and engineering science, which stimulated his own research and transferred him into a wanderer between two worlds.

@inproceedings{förstner09:computer, author = "Förstner, Wolfgang", title = "Computer Vision and Remote Sensing - Lessons Learned", year = "09", pages = "241-249", city = "Stuttgart", proceeding = "Photogrammetric Week 2009",}

@inproceedings{förstner20:mustererkennung, author = "Förstner, Wolfgang", title = "Mustererkennung in der Fernerkundung", year = "20", pages = "129-136", city = "Bonn", proceeding = "Publikationen der DGPF, Band 18: Zukunft mit Tradition",}

@inproceedings{foerstner*09:detecting, author={W. F\"orstner and T. Dickscheid and F. Schindler}, title={Detecting Interpretable and Accurate Scale-Invariant Keypoints}, booktitle={12th IEEE International Conference on Computer Vision (ICCV'09)}, address={Kyoto, Japan}, year={2009} pages={2256--2263} }

@inproceedings{foerstner*09:completeness, author={W. F\"orstner and T. Dickscheid and F. Schindler}, title={{On the Completeness of Coding with Image Features}}, booktitle={20th British Machine Vision Conference}, address={London, UK}, year={2009} }

We describe ground truth data that we provide to serve as a basis for evaluation and comparison of supervised learning approaches to image interpretation. The provided ground truth, the eTRIMS Image Database, is a collection of annotated images of real world street scenes. Typical objects in these images are variable in shape and appearance, in the number of its parts and appear in a variety of congurations. The domain of man-made scenes is thus well suited for evaluation and comparison of a variety of interpretation approaches, including those that employ structure models. The provided pixelwise ground truth assigns each image pixel both with a class label and an object label and offers thus ground truth annotation both on the level of pixels and regions. While we believe that such ground truth is of general interest in supervised learning, such data may be of further relevance in emerging real world applications involving automation of man-made scene interpretation.

@techreport{ korc-forstner-tr09-etrims, author = "Kor{\v c}, F. and F{\" o}rstner, W.", title = "{eTRIMS} {I}mage {D}atabase for Interpreting Images of Man-Made Scenes", number = "TR-IGG-P-2009-01", month = "April", year = "2009", institute = "Dept. of Photogrammetry, University of Bonn", url = "http://www.ipb.uni-bonn.de/projects/etrims_db/" }

@ARTICLE{meidow*09:reasoning, author = "Meidow, Jochen and Beder, Christian and Förstner, Wolfgang", title = "Reasoning with uncertain points, straight lines, and straight line segments in 2D", year = "2009", pages = "125-139", city = "Bonn",}

Well known estimation techniques in computational geometry usually deal only with single geometric entities as unknown parameters and do not account for constrained observations within the estimation. The estimation model proposed in this paper is much more general, as it can handle multiple homogeneous vectors as well as multiple constraints. Furthermore, it allows the consistent handling of arbitrary covariance matrices for the observed and the estimated entities. The major novelty is the proper handling of singular observation covariance matrices made possible by additional constraints within the estimation. These properties are of special interest for instance in the calculus of algebraic projective geometry, where singular covariance matrices arise naturally from the non-minimal parameterizations of the entities. The validity of the proposed adjustment model will be demonstrated by the estimation of a fundamental matrix from synthetic data and compared to heteroscedastic regression [?], which is considered as state-ofthe- art estimator for this task. As the latter is unable to simultaneously estimate multiple entities, we will also demonstrate the usefulness and the feasibility of our approach by the constrained estimation of three vanishing points from observed uncertain image line segments.

@inproceedings{meidow*09:optimal, author={Meidow, J. and F{\" o}rstner, W. and Beder, C.}, title={{Optimal Parameter Estimation with Homogeneous Entities and Arbitrary Constraints}}, booktitle={Pattern Recognition (Symposium of DAGM) '09}, editor = {Denzler, J. and Notni, G.}, year={2009}, publisher = {Springer}, series = {LNCS} }

@TECHREPORT{Roscher2009, author = {Roscher, R., Foerstner, W.}, title = {{Multiclass Bounded Logistic Regression -- Efficient Regularization with Interior Point Method}}, institution = {Department of Photogrammetry, University of Bonn}, year = {2009}, number = {TR-IGG-P-2009-02} }

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{wenzel09*:role, author = {Susanne Wenzel and Wolfgang F{\"o}rstner}, title = {The Role of Sequences for Incremental Learning}, institution = {Department of Photogrammetry, University of Bonn}, year = {2009}, number = {TR-IGG-P-2009-04}, month = {October} }