Localization, Reconstruction and Mapping Research at the Department of Photogrammetry 1 Department of Photogrammetry Institute for Geodesy and Geoinformation Universtity Bonn Situation Photogrammetry and its neighbours 2
Topics of Photogrammetry Methods: Extracting information from 2D images Aerial Terrestrial LIDAR Image sequences Applications Mapping Data capture for Geoinformation systems Monitoring High precision inverse engineering 3 Search in Virtual Earth How m anyfloors has thisbuilding? 4 Istherea balcony in the 7 th floor? Show m e the entrance
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Photogrammetry at Bonn currently Making basic tools available to non specialists Orientation Calibration (Stereo) 9 Exploring ML techniques for interpretation Efficient tools Incremental learning structures Application areas: Urban mapping Agricultural sciences Topics Orientation and Reconstruction Complementarity of Keypoints (Timo Dickscheid) Keypoints in Scalespace (Falko Schindler) Segmentation (Michael Ying Yang) Mapping Classification of Leaf Desease (Sabine Bauer) Interpreting Road Surfaces (Jan Siegemund) Learning of CRF for Facade Interpretation (Filip Korč) Logistic Regression for CRF s (Ribana Roscher) E-Training (Susanne Wenzel) 10
Keypoints for Weakly Textured Scenes Given: set of images Constraints: perspective images Task: determine relative pose Problems: 11 Too few keypoints Æ Scale space junction detector (Falko Schindler) Complementarity of keypoints Æ Feature detection as image coding (Timo Dickscheid) 8. October 2009 CV Bonn Poorly textured room 12 8. October 2009 CV Bonn
Complementary: blobs and junctions Lowe (2004): Distinctive image features from scale-invariant keypoints 13 Förstner/Dickscheidt/Schindler (2009): Detecting Interpretable and Accurate Scale-Invariant Keypoints Result of automatic orientation 14 Only possible w ith junctions
SAR-Segmentation Given: SAR image Constraints: known type of speckle noise Unknown: multi-region segmentation Problem: competetition between regions Solution level-set approach (Michael Yang) 15 Leaf Disease Classification Given: Multi-view multi-spectral images of leaf Constraint: calibrated cameras Unkown: Distribution of leaf desease Problem: Identify adequate features > 95 % acc. Solution: exploit spectral and spatial information Gaussian Mixture Model on Texture (Sabine Bauer) 16
Interpreting road surfaces Intention: 17 Reconstruction of road surfaces Detection and reconstruction of road borders (curbs) Detection of road discontinuities (potholes) Given: Stereo Image sequence Constraints: ego-motion known Goal: identify street boarders, pot holes Problem: Model of road surface Solution: gliding MRF (Jan Siegemund) 18 Videos
Learning for Markoff-Fields Given: set of annotated images Constraint: probabilistic model adequate 19 Task: learn model parameters w and v Fast Good classification Problem: NP complete approximate learning Psudolikelihood learning: ML, MAP (Filip Korč) Results 20
Logistic Regression for CRF Classification Given: training and test images Constraint: probabilistic model for class posteriors Logistic regression (LR) as input for CRF Goal: Multiple-Class-Segmentation Problem: L 2 -norm regularization too crisp labeling Solution: bounded LR regulariz. (Ribana Roscher) 21 E-Training Given: a large set of training images Constraint: known classes Goal: rules for providing machine-learner with examples Fast learning Accurate learning Problems: unknown permutation of images and classes Solution: learn separability and locally minimize Bayes error (Susanne Wenzel) 22
Training eight window classes mean sam ple 23 Outlook Exploit structures Use structures for learning Learn structures for interpretation Include 3D features, time features 24 Integrate image interpretation into mapping pipeline Exploitation of Bonn s expertise, e.g. Cropsense
Thanks for you attention 25 Please have a look at the posters