W. Förstner, "Minimal Representations for Testing and Estimation in Projective Spaces", Z. f. Photogrammetrie, Fernerkundung und Geoinformation. Vol. 3, pp. 209-220. 2012.

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Testing and estimation using homogeneous coordinates and matrices has to cope with obstacles such as singularities of covariance matrices and redundant parametrizations. The paper proposes a representation of the uncertainty of all types of geometric entities which (1) only requires the minimum number of parameters, (2) is free of singularities, (3) enables to exploit the simplicity of homogeneous coordinates to represent geometric constraints and (4) allows to handle geometric entities which are at infinity or at least very far away. We develop the concept, discuss its usefulness for bundle adjustment and demonstrate its applicability for determining 3D lines from observed image line segments in a multi view setup.

@article{Forstner2012Minimal,
  author = {W. F\"orstner},
  title = {Minimal Representations for Testing and Estimation in Projective Spaces},
  journal = {Z. f. Photogrammetrie, Fernerkundung und Geoinformation},
  year = {2012},
  volume = {3},
  pages = {209--220},
  doi = {10.1127/1432-8364/2012/0112}
}

Ribana Roscher and Wolfgang Förstner and Björn Waske, "I2VM: Incremental import vector machines", Image and Vision Computing., May, 2012. Vol. 30(4-5), pp. 263-278. 2012.

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We introduce an innovative incremental learner called incremental import vector machines ((IVM)-V-2). The kernel-based discriminative approach is able to deal with complex data distributions. Additionally, the learner is sparse for an efficient training and testing and has a probabilistic output. We particularly investigate the reconstructive component of import vector machines, in order to use it for robust incremental teaming. By performing incremental update steps, we are able to add and remove data samples, as well as update the current set of model parameters for incremental learning. By using various standard benchmarks, we demonstrate how (IVM)-V-2 is competitive or superior to other incremental methods. It is also shown that our approach is capable of managing concept-drifts in the data distributions. (C) 2012 Elsevier B.V. All rights reserved.

@article{Roscher2012I2VM,
  author = {Roscher, Ribana and F\"orstner, Wolfgang and Waske, Bj\"orn},
  title = {I2VM: Incremental import vector machines},
  journal = {Image and Vision Computing},
  year = {2012},
  volume = {30},
  number = {4-5},
  pages = {263--278},
  doi = {10.1016/j.imavis.2012.04.004}
}

Ribana Roscher and Björn Waske and Wolfgang Förstner, "Incremental Import Vector Machines for Classifying Hyperspectral Data", IEEE Transactions on Geoscience and Remote Sensing., September, 2012. Vol. 50(9), pp. 3463-3473. 2012.

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In this paper, we propose an incremental learning strategy for import vector machines (IVM), which is a sparse kernel logistic regression approach. We use the procedure for the concept of self-training for sequential classification of hyperspectral data. The strategy comprises the inclusion of new training samples to increase the classification accuracy and the deletion of noninformative samples to be memory and runtime efficient. Moreover, we update the parameters in the incremental IVM model without retraining from scratch. Therefore, the incremental classifier is able to deal with large data sets. The performance of the IVM in comparison to support vector machines (SVM) is evaluated in terms of accuracy, and experiments are conducted to assess the potential of the probabilistic outputs of the IVM. Experimental results demonstrate that the IVM and SVM perform similar in terms of classification accuracy. However, the number of import vectors is significantly lower when compared to the number of support vectors, and thus, the computation time during classification can be decreased. Moreover, the probabilities provided by IVM are more reliable, when compared to the probabilistic information, derived from an SVM's output. In addition, the proposed self-training strategy can increase the classification accuracy. Overall, the IVM and its incremental version is worthwhile for the classification of hyperspectral data.

@article{Roscher2012Incremental,
  author = {Roscher, Ribana and Waske, Bj\"orn and F\"orstner, Wolfgang},
  title = {Incremental Import Vector Machines for Classifying Hyperspectral Data},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2012},
  volume = {50},
  number = {9},
  pages = {3463--3473},
  doi = {10.1109/TGRS.2012.2184292}
}

Johannes Schneider and Falko Schindler and Thomas Läbe and Wolfgang Förstner, "Bundle Adjustment for Multi-camera Systems with Points at Infinity", In ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. I-3, pp. 75-80. 2012.

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We present a novel approach for a rigorous bundle adjustment for omnidirectional and multi-view cameras, which enables an efficient maximum-likelihood estimation with image and scene points at infinity. Multi-camera systems are used to increase the resolution, to combine cameras with different spectral sensitivities (Z/I DMC, Vexcel Ultracam) or - like omnidirectional cameras - to augment the effective aperture angle (Blom Pictometry, Rollei Panoscan Mark III). Additionally multi-camera systems gain in importance for the acquisition of complex 3D structures. For stabilizing camera orientations - especially rotations - one should generally use points at the horizon over long periods of time within the bundle adjustment that classical bundle adjustment programs are not capable of. We use a minimal representation of homogeneous coordinates for image and scene points. Instead of eliminating the scale factor of the homogeneous vectors by Euclidean normalization, we normalize the homogeneous coordinates spherically. This way we can use images of omnidirectional cameras with single-view point like fisheye cameras and scene points, which are far away or at infinity. We demonstrate the feasibility and the potential of our approach on real data taken with a single camera, the stereo camera FinePix Real 3D W3 from Fujifilm and the multi-camera system Ladybug3 from Point Grey.

@inproceedings{Schneider2012Bundle,
  author = {Schneider, Johannes and Schindler, Falko and L\"abe, Thomas and F\"orstner, Wolfgang},
  title = {Bundle Adjustment for Multi-camera Systems with Points at Infinity},
  booktitle = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
  year = {2012},
  volume = {I-3},
  pages = {75--80},
  doi = {10.5194/isprsannals-I-3-75-2012}
}

Susanne Wenzel and Wolfgang 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). Vol. I-3( 2012), pp. 197-202. 2012.

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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,
  author = {Wenzel, Susanne and F\"orstner, Wolfgang},
  title = {Learning a compositional representation for facade object categorization},
  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},
  volume = {I-3},
  number = { 2012},
  pages = {197--202},
  doi = {10.5194/isprsannals-I-3-197-2012}
}

Wolfgang Förstner, "Minimal Representations for Testing and Estimation in Projective Spaces"(TR-IGG-P-2012-03) 2012.

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Testing and estimation using homogeneous coordinates and matrices has to cope with obstacles such as singularities of covariance matrices and redundant parametrizations. The paper proposes a representation of the uncertainty of all types of geometric entities which (1) only requires the minimum number of parameters, (2) is free of singularities, (3) enables to exploit the simplicity of homogeneous coordinates to represent geometric constraints and (4) allows to handle geometric entities which are at infinity or at least very far away. We develop the concept, discuss its usefulness for bundle adjustment and demonstrate its applicability for determining 3D lines from observed image line segments in a multi view setup.

@techreport{Forstner2012MinimalReport,
  author = {F\"orstner, Wolfgang},
  title = {Minimal Representations for Testing and Estimation in Projective Spaces},
  year = {2012},
  number = {TR-IGG-P-2012-03}
}

Ribana Roscher and Björn Waske and Wolfgang Förstner, "Evaluation of Import Vector Machines for Classifying Hyperspectral Data" 2012.

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We evaluate the performance of Import Vector Machines (IVM),a sparse Kernel Logistic Regression approach, for the classification of hyperspectral data. The IVM classifier is applied on two different data sets, using different number of training samples. The performance of IVM to Support Vector Machines (SVM) is compared in terms of accuracy and sparsity. Moreover, the impact of the training sample set on the accuracy and stability of IVM was investigated. The results underline that the IVM perform similar when compared to the popular SVM in terms of accuracy. Moreover, the number of import vectors from the IVM is significantly lower when compared to the number of support vectors from the SVM. Thus, the classification process of the IVM is faster. These findings are independent from the study site, the number of training samples and specific classes. Consequently, the proposed IVM approach is a promising classification method for hyperspectral imagery.

@techreport{Roscher2012Evaluation,
  author = {Roscher, Ribana and Waske, Bj\"orn and F\"orstner, Wolfgang},
  title = {Evaluation of Import Vector Machines for Classifying Hyperspectral Data},
  year = {2012}
}

Ribana Roscher and Jan Siegemund and Falko Schindler and Wolfgang Förstner, "Object Tracking by Segmentation Using Incremental Import Vector Machines" 2012.

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We propose a framework for object tracking in image sequences, following the concept of tracking-by-segmentation. The separation of object and background is achieved by a consecutive semantic superpixel segmentation of the images, yielding tight object boundaries. I.e., in the first image a model of the object's characteristics is learned from an initial, incomplete annotation. This model is used to classify the superpixels of subsequent images to object and background employing graph-cut. We assume the object boundaries to be tight-fitting and the object motion within the image to be affine. To adapt the model to radiometric and geometric changes we utilize an incremental learner in a co-training scheme. We evaluate our tracking framework qualitatively and quantitatively on several image sequences.

@techreport{Roscher2012Object,
  author = {Roscher, Ribana and Siegemund, Jan and Schindler, Falko and F\"orstner, Wolfgang},
  title = {Object Tracking by Segmentation Using Incremental Import Vector Machines},
  year = {2012}
}