Publications

The Whole Is More Than Its Parts? From Explicit to Implicit Pose Normalization
Marcel Simon and Erik Rodner and Trevor Darell and Joachim Denzler.
IEEE Transactions on Pattern Analysis and Machine Intelligence. 42 (3): pages 749-763. 2020. (Pre-print published in 2019.)
[bibtex] [pdf] [web] [abstract]

Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection
Björn Barz and Erik Rodner and Yanira Guanche Garcia and Joachim Denzler.
IEEE Transactions on Pattern Analysis and Machine Intelligence. 41 (5): pages 1088-1101. 2019. (Pre-print published in 2018.)
[bibtex] [pdf] [web] [code] [abstract]

Fully Convolutional Networks in Multimodal Nonlinear Microscopy Images for Automated Detection of Head and Neck Carcinoma: A Pilot Study
Erik Rodner and Thomas Bocklitz and Ferdinand von Eggeling and Günther Ernst and Olga Chernavskaia and Jürgen Popp and Joachim Denzler and Orlando Guntinas-Lichius.
Head \& Neck. 41 (1): pages 116-121. 2019.
[bibtex] [web] [abstract]

Active Learning for Regression Tasks with Expected Model Output Changes
Christoph Käding and Erik Rodner and Alexander Freytag and Oliver Mothes and Björn Barz and Joachim Denzler.
British Machine Vision Conference (BMVC). 2018.
[bibtex] [pdf] [code] [supplementary] [abstract]

HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues
Talha Qaiser and Abhik Mukherjee and Chaitanya Reddy PB and Sai D Munugoti and Vamsi Tallam and Tomi Pitkaho and Taina Lehtimki and Thomas Naughton and Matt Berseth and Anbal Pedraza and Ramakrishnan Mukundan and Matthew Smith and Abhir Bhalerao and Erik Rodner and Marcel Simon and Joachim Denzler and Chao-Hui Huang and Gloria Bueno and David Snead and Ian O Ellis and Mohammad Ilyas and Nasir Rajpoot.
Histopathology. 72 (2): pages 227-238. 2018.
[bibtex] [web] [abstract]

Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques
Milan Flach and Fabian Gans and Alexander Brenning and Joachim Denzler and Markus Reichstein and Erik Rodner and Sebastian Bathiany and Paul Bodesheim and Yanira Guanche and Sebasitan Sippel and Miguel D. Mahecha.
Earth System Dynamics. 8 (3): pages 677-696. 2017.
[bibtex] [pdf] [web] [abstract]

Abstract: Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no "gold standard" for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies in Earth system science data.

Generalized orderless pooling performs implicit salient matching
Marcel Simon and Yang Gao and Trevor Darrell and Joachim Denzler and Erik Rodner.
International Conference on Computer Vision (ICCV). Pages 4970-4979. 2017.
[bibtex] [pdf]

Large-Scale Gaussian Process Inference with Generalized Histogram Intersection Kernels for Visual Recognition Tasks
Erik Rodner and Alexander Freytag and Paul Bodesheim and Björn Fröhlich and Joachim Denzler.
International Journal of Computer Vision (IJCV). 121 (2): pages 253-280. 2017.
[bibtex] [pdf] [web] [abstract]

Deep bilinear features for Her2 scoring in digital pathology
Erik Rodner and Marcel Simon and Joachim Denzler.
Current Directions in Biomedical Engineering. 3 (2): pages 811-814. 2017.
[bibtex] [pdf]

Fast Learning and Prediction for Object Detection using Whitened CNN Features
Björn Barz and Erik Rodner and Christoph Käding and Joachim Denzler.
arXiv preprint arXiv:1704.02930. 2017.
[bibtex] [pdf] [web]

Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning
Marc Aubreville and Christian Knipfer and Nicolai Oetter and Christian Jaremenko and Erik Rodner and Joachim Denzler and Christopher Bohr and Helmut Neumann and Florian Stelzle and Andreas Maier.
Scientific Reports. 7 (1): pages 41598-017. 2017.
[bibtex] [web]

Maximally Divergent Intervals for Extreme Weather Event Detection
Björn Barz and Yanira Guanche and Erik Rodner and Joachim Denzler.
MTS/IEEE OCEANS Conference Aberdeen. Pages 1-9. 2017.
[bibtex] [pdf] [abstract]

Semantic Volume Segmentation with Iterative Context Integration for Bio-medical Image Stacks
Sven Sickert and Erik Rodner and Joachim Denzler.
Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications (PRIA). 26 (1): pages 197-204. 2016.
[bibtex] [pdf] [abstract]

Fine-tuning Deep Neural Networks in Continuous Learning Scenarios
Christoph Käding and Erik Rodner and Alexander Freytag and Joachim Denzler.
ACCV Workshop on Interpretation and Visualization of Deep Neural Nets (ACCV-WS). 2016.
[bibtex] [pdf] [web] [supplementary] [abstract]

Large-scale Active Learning with Approximated Expected Model Output Changes
Christoph Käding and Alexander Freytag and Erik Rodner and Andrea Perino and Joachim Denzler.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 179-191. 2016.
[bibtex] [pdf] [web] [code] [supplementary] [abstract]

Convolutional Neural Networks as a Computational Model for the Underlying Processes of Aesthetics Perception
Joachim Denzler and Erik Rodner and Marcel Simon.
ECCV Workshop on Computer Vision for Art Analysis. 2016.
[bibtex] [pdf] [web]

Potential of new machine learning methods for understanding long-term interannual variability of carbon and energy fluxes and states from site to global scale
Markus Reichstein and Martin Jung and Paul Bodesheim and Miguel D. Mahecha and Fabian Gans and Erik Rodner and Gustau Camps-Valls and Dario Papale and Gianluca Tramontana and Joachim Denzler and Dennis D. Baldocchi.
American Geophysical Union Fall Meeting (AGU): Abstract + Oral Presentation. 2016.
[bibtex] [web] [abstract]

Using Statistical Process Control for detecting anomalies in multivariate spatiotemporal Earth Observations
Milan Flach and Miguel Mahecha and Fabian Gans and Erik Rodner and Paul Bodesheim and Yanira Guanche-Garcia and Alexander Brenning and Joachim Denzler and Markus Reichstein.
European Geosciences Union General Assembly (EGU): Abstract + Oral Presentation. 2016.
[bibtex] [pdf] [web] [abstract]

Abstract: The number of available Earth observations (EOs) is currently substantially increasing. Detecting anomalous pat-terns in these multivariate time series is an important step in identifying changes in the underlying dynamicalsystem. Likewise, data quality issues might result in anomalous multivariate data constellations and have to beidentified before corrupting subsequent analyses. In industrial application a common strategy is to monitor pro-duction chains with several sensors coupled to some statistical process control (SPC) algorithm. The basic ideais to raise an alarm when these sensor data depict some anomalous pattern according to the SPC, i.e. the produc-tion chain is considered out of control. In fact, the industrial applications are conceptually similar to the on-linemonitoring of EOs. However, algorithms used in the context of SPC or process monitoring are rarely consideredfor supervising multivariate spatio-temporal Earth observations. The objective of this study is to exploit the poten-tial and transferability of SPC concepts to Earth system applications. We compare a range of different algorithmstypically applied by SPC systems and evaluate their capability to detect e.g. known extreme events in land sur-face processes. Specifically two main issues are addressed: (1) identifying the most suitable combination of datapre-processing and detection algorithm for a specific type of event and (2) analyzing the limits of the individual ap-proaches with respect to the magnitude, spatio-temporal size of the event as well as the datas signal to noise ratio.Extensive artificial data sets that represent the typical properties of Earth observations are used in this study. Ourresults show that the majority of the algorithms used can be considered for the detection of multivariate spatiotem-poral events and directly transferred to real Earth observation data as currently assembled in different projectsat the European scale, e.g. http://baci-h2020.eu/index.php/ and http://earthsystemdatacube.net/. Known anomaliessuch as the Russian heatwave are detected as well as anomalies which are not detectable with univariate methods.

Neither Quick Nor Proper -- Evaluation of QuickProp for Learning Deep Neural Networks
Clemens-Alexander Brust and Sven Sickert and Marcel Simon and Erik Rodner and Joachim Denzler. 2016. Technical Report TR-FSU-INF-CV-2016-01
[bibtex] [pdf] [abstract]

ImageNet pre-trained models with batch normalization
Marcel Simon and Erik Rodner and Joachim Denzler.
arXiv preprint 1612.01452. 2016.
[bibtex] [pdf] [web] [abstract]

Watch, Ask, Learn, and Improve: A Lifelong Learning Cycle for Visual Recognition
Christoph Käding and Erik Rodner and Alexander Freytag and Joachim Denzler.
European Symposium on Artificial Neural Networks (ESANN). Pages 381-386. 2016.
[bibtex] [pdf] [code] [presentation] [abstract]

Vegetation segmentation in cornfield images using bag of words
Yerania Campos and Erik Rodner and Joachim Denzler and Humberto Sossa and Gonzalo Pajares.
Advanced Concepts for Intelligent Vision Systems (Acivs). Pages 193-204. 2016.
[bibtex] [pdf] [web] [abstract]

Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches
Erik Rodner and Marcel Simon and Bob Fisher and Joachim Denzler.
British Machine Vision Conference (BMVC). 2016.
[bibtex] [pdf] [supplementary]

SeaCLEF 2016: Object Proposal Classification for Fish Detection in Underwater Videos
Jonas Jäger and Erik Rodner and Joachim Denzler and Viviane Wolff and Klaus Fricke-Neuderth.
Working Notes of CLEF 2016 - Conference and Labs of the Evaluation forum. Pages 481-489. 2016.
[bibtex] [pdf]

Detecting Multivariate Biosphere Extremes
Yanira Guanche Garcia and Erik Rodner and Milan Flach and Sebastian Sippel and Miguel Mahecha and Joachim Denzler.
International Workshop on Climate Informatics (CI). Pages 9-12. 2016.
[bibtex] [web] [abstract]

Hot spots of multivariate extreme anomalies in Earth observations
Milan Flach and Sebastian Sippel and Paul Bodesheim and Alexander Brenning and Joachim Denzler and Fabian Gans and Yanira Guanche and Markus Reichstein and Erik Rodner and Miguel D. Mahecha.
American Geophysical Union Fall Meeting (AGU): Abstract + Oral Presentation. 2016.
[bibtex] [web] [abstract]

Maximally Divergent Intervals for Anomaly Detection
Erik Rodner and Björn Barz and Yanira Guanche and Milan Flach and Miguel Mahecha and Paul Bodesheim and Markus Reichstein and Joachim Denzler.
Workshop on Anomaly Detection (ICML-WS). 2016. Best Paper Award
[bibtex] [pdf] [web] [code] [abstract]

Impatient DNNs - Deep Neural Networks with Dynamic Time Budgets
Manuel Amthor and Erik Rodner and Joachim Denzler.
British Machine Vision Conference (BMVC). 2016.
[bibtex] [pdf]

Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes
Christoph Käding and Erik Rodner and Alexander Freytag and Joachim Denzler.
NIPS Workshop on Continual Learning and Deep Networks (NIPS-WS). 2016.
[bibtex] [pdf] [web] [abstract]

Chimpanzee Faces in the Wild: Log-Euclidean CNNs for Predicting Identities and Attributes of Primates
Alexander Freytag and Erik Rodner and Marcel Simon and Alexander Loos and Hjalmar Kühl and Joachim Denzler.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 51-63. 2016.
[bibtex] [pdf] [web] [supplementary] [abstract]

Active Learning and Discovery of Object Categories in the Presence of Unnameable Instances
Christoph Käding and Alexander Freytag and Erik Rodner and Paul Bodesheim and Joachim Denzler.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pages 4343-4352. 2015.
[bibtex] [pdf] [web] [code] [presentation] [supplementary] [abstract]

Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding
Clemens-Alexander Brust and Sven Sickert and Marcel Simon and Erik Rodner and Joachim Denzler.
International Conference on Computer Vision Theory and Applications (VISAPP). Pages 510-517. 2015.
[bibtex] [pdf] [abstract]

Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks
Marcel Simon and Erik Rodner.
International Conference on Computer Vision (ICCV). Pages 1143-1151. 2015.
[bibtex] [pdf] [web] [abstract]

Automated analysis of confocal laser endomicroscopy images to detect head and neck cancer
Andreas Dittberner and Erik Rodner and Wolfgang Ortmann and Joachim Stadler and Carsten Schmidt and Iver Petersen and Andreas Stallmach and Joachim Denzler and Orlando Guntinas-Lichius.
Head \& Neck. 38 (1): 2015.
[bibtex] [web]

Beyond Thinking in Common Categories: Predicting Obstacle Vulnerability using Large Random Codebooks
Johannes Rühle and Erik Rodner and Joachim Denzler.
Machine Vision Applications (MVA). Pages 198-201. 2015.
[bibtex] [pdf] [web] [abstract]

Fine-grained Recognition Datasets for Biodiversity Analysis
Erik Rodner and Marcel Simon and Gunnar Brehm and Stephanie Pietsch and J. Wolfgang Wägele and Joachim Denzler.
CVPR Workshop on Fine-grained Visual Classification (CVPR-WS). 2015.
[bibtex] [pdf] [web]

Analysis and Classification of Microscopy Images with Cell Border Distance Statistics
Erik Rodner and Wolfgang Ortmann and Andreas Dittberner and Joachim Stadler and Carsten Schmidt and Iver Petersen and Andreas Stallmach and Joachim Denzler and Orlando Guntinas-Lichius.
Jahrestagung der Deutschen Gesellschaft für Medizinische Physik (DGMP). 2015.
[bibtex] [pdf]

Efficient Convolutional Patch Networks for Scene Understanding
Clemens-Alexander Brust and Sven Sickert and Marcel Simon and Erik Rodner and Joachim Denzler.
CVPR Workshop on Scene Understanding (CVPR-WS). 2015. Poster presentation and extended abstract
[bibtex] [pdf] [abstract]

Local Novelty Detection in Multi-class Recognition Problems
Paul Bodesheim and Alexander Freytag and Erik Rodner and Joachim Denzler.
IEEE Winter Conference on Applications of Computer Vision (WACV). Pages 813-820. 2015.
[bibtex] [pdf] [web] [supplementary] [abstract]

Fine-grained Classification of Identity Document Types with Only One Example
Marcel Simon and Erik Rodner and Joachim Denzler.
Machine Vision Applications (MVA). Pages 126-129. 2015.
[bibtex] [pdf] [web] [abstract]

Understanding Object Descriptions in Robotics by Open-vocabulary Object Retrieval and Detection
Sergio Guadarrama and Erik Rodner and Kate Saenko and Trevor Darrell.
International Journal of Robotics Research (IJRR). 35 (1-3): pages 265-280. 2015.
[bibtex] [pdf] [web]

Instance-weighted Transfer Learning of Active Appearance Models
Daniel Haase and Erik Rodner and Joachim Denzler.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pages 1426-1433. 2014.
[bibtex] [pdf] [abstract]

Open-vocabulary Object Retrieval
Sergio Guadarrama and Erik Rodner and Kate Saenko and Ning Zhang and Ryan Farrell and Jeff Donahue and Trevor Darrell.
Robotics Science and Systems (RSS). Pages 41, ISBN 978-0-9923747-0-9. 2014. Awarded with an AAAI invited talk
[bibtex] [pdf] [web] [abstract]

Seeing through bag-of-visual-word glasses: towards understanding quantization effects in feature extraction methods
Alexander Freytag and Johannes Rühle and Paul Bodesheim and Erik Rodner and Joachim Denzler. 2014. Technical Report TR-FSU-INF-CV-2014-01
[bibtex] [pdf] [code] [abstract]

Interactive Adaptation of Real-Time Object Detectors
Daniel Göhring and Judy Hoffman and Erik Rodner and Kate Saenko and Trevor Darrell.
International Conference on Robotics and Automation (ICRA). Pages 1282-1289. 2014.
[bibtex] [pdf] [web] [abstract]

Birds of a Feather Flock Together - Local Learning of Mid-level Representations for Fine-grained Recognition
Alexander Freytag and Erik Rodner and Joachim Denzler.
ECCV Workshop on Parts and Attributes (ECCV-WS). 2014.
[bibtex] [pdf] [web] [code] [presentation]

Seeing through bag-of-visual-word glasses: towards understanding quantization effects in feature extraction methods
Alexander Freytag and Johannes Rühle and Paul Bodesheim and Erik Rodner and Joachim Denzler.
ICPR Workshop on Features and Structures (FEAST): Extended Abstract + Poster Presentation. 2014. Best Poster Award
[bibtex] [pdf] [code] [presentation]

Nonparametric Part Transfer for Fine-grained Recognition
Christoph Göring and Erik Rodner and Alexander Freytag and Joachim Denzler.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pages 2489-2496. 2014.
[bibtex] [pdf] [web] [code] [presentation] [abstract]

Semantic Volume Segmentation with Iterative Context Integration
Sven Sickert and Erik Rodner and Joachim Denzler.
Open German-Russian Workshop on Pattern Recognition and Image Understanding (OGRW). Pages 220-225. 2014.
[bibtex] [pdf] [web] [abstract]

ARTOS -- Adaptive Real-Time Object Detection System
Björn Barz and Erik Rodner and Joachim Denzler.
arXiv preprint arXiv:1407.2721. 2014.
[bibtex] [pdf] [web] [code] [abstract]

Selecting Influential Examples: Active Learning with Expected Model Output Changes
Alexander Freytag and Erik Rodner and Joachim Denzler.
European Conference on Computer Vision (ECCV). Pages 562-577. 2014.
[bibtex] [pdf] [presentation] [supplementary] [abstract]

Part Detector Discovery in Deep Convolutional Neural Networks
Marcel Simon and Erik Rodner and Joachim Denzler.
Asian Conference on Computer Vision (ACCV). Pages 162-177. 2014.
[bibtex] [pdf] [code] [abstract]

Part Localization by Exploiting Deep Convolutional Networks
Marcel Simon and Erik Rodner and Joachim Denzler.
ECCV Workshop on Parts and Attributes (ECCV-WS). 2014.
[bibtex] [pdf] [web]

Bildverarbeitung und Objekterkennung: Computer Vision in Industrie und Medizin
Herbert Süße and Erik Rodner. 2014. Neues umfangreiches Lehrbuch im Bereich Bildverarbeitung und maschinelles Lernen
[bibtex] [web] [abstract]

Exemplar-specific Patch Features for Fine-grained Recognition
Alexander Freytag and Erik Rodner and Trevor Darrell and Joachim Denzler.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 144-156. 2014.
[bibtex] [pdf] [code] [supplementary] [abstract]

Asymmetric and Category Invariant Feature Transformations for Domain Adaptation
Judy Hoffman and Erik Rodner and Jeff Donahue and Brian Kulis and Kate Saenko.
International Journal of Computer Vision (IJCV). 109 (1-2): pages 28-41. 2014.
[bibtex] [pdf] [web] [abstract]

Transform-based Domain Adaptation for Big Data
Erik Rodner and Judy Hoffman and Jeff Donahue and Trevor Darrell and Kate Saenko.
NIPS Workshop on New Directions in Transfer and Multi-Task Learning (NIPS-WS). 2013. abstract version of arXiv:1308.4200
[bibtex] [pdf] [abstract]

Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations
Erik Rodner and Judy Hoffman and Jeff Donahue and Trevor Darrell and Kate Saenko.
arXiv preprint arXiv:1308.4200. 2013.
[bibtex] [pdf]

Fine-grained Categorization - Short Summary of our Entry for the ImageNet Challenge 2012
Christoph Göring and Alexander Freytag and Erik Rodner and Joachim Denzler.
arXiv preprint arXiv:1310.4759. 2013.
[bibtex] [pdf] [web]

Semi-Supervised Domain Adaptation with Instance Constraints
Jeff Donahue and Judy Hoffman and Erik Rodner and Kate Saenko and Trevor Darrell.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pages 668 - 675. 2013.
[bibtex] [pdf]

Approximations of Gaussian Process Uncertainties for Visual Recognition Problems
Paul Bodesheim and Alexander Freytag and Erik Rodner and Joachim Denzler.
Scandinavian Conference on Image Analysis (SCIA). Pages 182-194. 2013.
[bibtex] [pdf] [web] [abstract]

An Efficient Approximation for Gaussian Process Regression
Paul Bodesheim and Alexander Freytag and Erik Rodner and Joachim Denzler. 2013. Technical Report TR-FSU-INF-CV-2013-01
[bibtex] [pdf]

I Want To Know More: Efficient Multi-Class Incremental Learning Using Gaussian Processes
Alexander Lütz and Erik Rodner and Joachim Denzler.
Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications (PRIA). 23 (3): pages 402-407. 2013.
[bibtex] [pdf]

Automatic Identification of Novel Bacteria using Raman Spectroscopy and Gaussian Processes
Michael Kemmler and Erik Rodner and Petra Rösch and Jürgen Popp and Joachim Denzler.
Analytica Chimica Acta. 794: pages 29-37. 2013.
[bibtex] [pdf] [web] [supplementary]

Scalable Transform-based Domain Adaptation
Erik Rodner and Judy Hoffman and Jeff Donahue and Trevor Darrell and Kate Saenko.
ICCV Workshop on Visual Domain Adaptation (ICCV-WS). 2013.
[bibtex] [pdf]

Beyond the closed-world assumption: The importance of novelty detection and open set recognition
Joachim Denzler and Erik Rodner and Paul Bodesheim and Alexander Freytag.
GCPR/DAGM Workshop on Unsolved Problems in Pattern Recognition and Computer Vision (GCPR-WS): Extended Abstract + Oral Presentation. 2013.
[bibtex] [pdf] [web]

Efficient Learning of Domain-invariant Image Representations
Judy Hoffman and Erik Rodner and Jeff Donahue and Trevor Darrell and Kate Saenko.
International Conference on Learning Representations (ICLR). 2013.
[bibtex] [pdf]

One-class Classification with Gaussian Processes
Michael Kemmler and Erik Rodner and Esther-Sabrina Wacker and Joachim Denzler.
Pattern Recognition. 46 (12): pages 3507-3518. 2013.
[bibtex] [pdf]

Segmentation of Microorganism in Complex Environments
Michael Kemmler and Björn Fröhlich and Erik Rodner and Joachim Denzler.
Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications (PRIA). 23 (4): pages 512-517. 2013.
[bibtex] [pdf]

Labeling examples that matter: Relevance-Based Active Learning with Gaussian Processes
Alexander Freytag and Erik Rodner and Paul Bodesheim and Joachim Denzler.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 282-291. 2013.
[bibtex] [pdf] [web] [code] [supplementary] [abstract]

Kernel Null Space Methods for Novelty Detection
Paul Bodesheim and Alexander Freytag and Erik Rodner and Michael Kemmler and Joachim Denzler.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pages 3374-3381. 2013.
[bibtex] [pdf] [web] [code] [presentation] [abstract]

Large-Scale Gaussian Process Multi-Class Classification for Semantic Segmentation and Facade Recognition
Björn Fröhlich and Erik Rodner and Michael Kemmler and Joachim Denzler.
Machine Vision and Applications. 24 (5): pages 1043-1053. 2013.
[bibtex] [pdf]

Large-Scale Gaussian Process Classification using Random Decision Forests
Björn Fröhlich and Erik Rodner and Michael Kemmler and Joachim Denzler.
Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications (PRIA). 22 (1): pages 113-120. 2012.
[bibtex] [pdf]

Semantic Segmentation with Millions of Features: Integrating Multiple Cues in a Combined Random Forest Approach
Björn Fröhlich and Erik Rodner and Joachim Denzler.
Asian Conference on Computer Vision (ACCV). Pages 218-231. 2012.
[bibtex] [pdf]

Beyond Classification - Large-scale Gaussian Process Inference and Uncertainty Prediction
Alexander Freytag and Erik Rodner and Paul Bodesheim and Joachim Denzler.
Big Data Meets Computer Vision: First International Workshop on Large Scale Visual Recognition and Retrieval (NIPS-WS). 2012. This workshop article is a short version of our ACCV 2012 paper.
[bibtex] [pdf] [abstract]

Rapid Uncertainty Computation with Gaussian Processes and Histogram Intersection Kernels
Alexander Freytag and Erik Rodner and Paul Bodesheim and Joachim Denzler.
Asian Conference on Computer Vision (ACCV). Pages 511-524. 2012. Best Paper Honorable Mention Award
[bibtex] [pdf] [web] [presentation] [abstract]

Large-Scale Gaussian Process Classification with Flexible Adaptive Histogram Kernels
Erik Rodner and Alexander Freytag and Paul Bodesheim and Joachim Denzler.
European Conference on Computer Vision (ECCV). Pages 85-98. 2012.
[bibtex] [pdf] [web] [supplementary] [abstract]

As Time Goes By: Anytime Semantic Segmentation with Iterative Context Forests
Björn Fröhlich and Erik Rodner and Joachim Denzler.
Symposium of the German Association for Pattern Recognition (DAGM). Pages 1-10. 2012.
[bibtex] [pdf]

Lernen mit wenigen Beispielen für die visuelle Objekterkennung
Erik Rodner.
Ausgezeichnete Informatikdissertationen 2011. 2012. in german
[bibtex] [pdf] [web]

Efficient Semantic Segmentation with Gaussian Processes and Histogram Intersection Kernels
Alexander Freytag and Björn Fröhlich and Erik Rodner and Joachim Denzler.
International Conference on Pattern Recognition (ICPR). Pages 3313-3316. 2012.
[bibtex] [pdf]

Multi-Person Tracking-by-Detection based on Calibrated Multi-Camera Systems
Xiaoyan Jiang and Erik Rodner and Joachim Denzler.
International Conference on Computer Vision and Graphics. Pages 743-751. 2012.
[bibtex] [pdf]

Divergence-Based One-Class Classification Using Gaussian Processes
Paul Bodesheim and Erik Rodner and Alexander Freytag and Joachim Denzler.
British Machine Vision Conference (BMVC). Pages 50.1-50.11. 2012.
[bibtex] [pdf] [web] [presentation] [abstract]

Efficient Multi-Class Incremental Learning Using Gaussian Processes
Alexander Lütz and Erik Rodner and Joachim Denzler.
Open German-Russian Workshop on Pattern Recognition and Image Understanding (OGRW). Pages 182-185. 2011.
[bibtex] [pdf] [abstract]

One-Class Classification for Anomaly Detection in Wire Ropes with Gaussian Processes in a Few Lines of Code
Erik Rodner and Esther-Sabrina Wacker and Michael Kemmler and Joachim Denzler.
Machine Vision Applications (MVA). Pages 219-222. 2011.
[bibtex] [pdf]

Detection of Microorganisms in Complex Microscopy Images
Michael Kemmler and Björn Fröhlich and Erik Rodner and Joachim Denzler.
Open German-Russian Workshop on Pattern Recognition and Image Understanding (OGRW). Pages 115-118. 2011.
[bibtex] [pdf]

Learning with Few Examples for Binary and Multiclass Classification Using Regularization of Randomized Trees
Erik Rodner and Joachim Denzler.
Pattern Recognition Letters. 32 (2): pages 244-251. 2011.
[bibtex] [pdf]

Efficient Gaussian process classification using random decision forests
Björn Fröhlich and Erik Rodner and Michael Kemmler and Joachim Denzler.
Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications (PRIA). 21: pages 184-187. 2011. 10.1134/S1054661811020337
[bibtex] [pdf]

Learning from Few Examples for Visual Recognition Problems
Erik Rodner. 2011.
[bibtex] [pdf] [web]

Efficient Gaussian Process Classification using Random Decision Forests
Björn Fröhlich and Erik Rodner and Michael Kemmler and Joachim Denzler.
International Conference on Pattern Recognition and Image Analysis (PRIA), St. Petersburg, Russia. Pages 93-96. 2010.
[bibtex] [pdf]

A Fast Approach for Pixelwise Labeling of Facade Images
Björn Fröhlich and Erik Rodner and Joachim Denzler.
International Conference on Pattern Recognition (ICPR). Pages 3029-3032. 2010.
[bibtex] [pdf]

One-Class Classification with Gaussian Processes
Michael Kemmler and Erik Rodner and Joachim Denzler.
Asian Conference on Computer Vision (ACCV). Pages 489-500. 2010.
[bibtex] [pdf] [presentation]

One-Shot Learning of Object Categories using Dependent Gaussian Processes
Erik Rodner and Joachim Denzler.
Symposium of the German Association for Pattern Recognition (DAGM). Pages 232-241. 2010.
[bibtex] [pdf]

Multiple Kernel Gaussian Process Classification for Generic 3D Object Recognition From Time-of-Flight Images
Erik Rodner and Doaa Hegazy and Joachim Denzler.
International Conference on Image and Vision Computing. Pages 1-8. 2010.
[bibtex] [pdf] [web]

Learning with Few Examples by Transferring Feature Relevance
Erik Rodner and Joachim Denzler.
Symposium of the German Association for Pattern Recognition (DAGM). Pages 252-261. 2009.
[bibtex] [pdf]

Randomized Probabilistic Latent Semantic Analysis for Scene Recognition
Erik Rodner and Joachim Denzler.
Iberoamerican Congress on Pattern Recognition (CIARP). Pages 945-953. 2009.
[bibtex] [pdf]

Global Context Extraction for Object Recognition Using a Combination of Range and Visual Features
Michael Kemmler and Erik Rodner and Joachim Denzler.
Dynamic 3D Imaging Workshop. Pages 96-109. 2009.
[bibtex] [pdf]

On Fusion of Range and Intensity Information Using Graph-Cut for Planar Patch Segmentation
Olaf Kähler and Erik Rodner and Joachim Denzler.
International Journal of Intelligent Systems Technologies and Applications. 5 (3/4): pages 365-373. 2008.
[bibtex] [pdf] [abstract]

Learning with Few Examples using a Constrained Gaussian Prior on Randomized Trees
Erik Rodner and Joachim Denzler.
Vision, Modelling, and Visualization Workshop (VMV). Pages 159-168. 2008.
[bibtex] [pdf]

Difference of Boxes Filters Revisited: Shadow Suppression and Efficient Character Segmentation
Erik Rodner and Herbert Süße and Wolfgang Ortmann and Joachim Denzler.
IAPR Workshop on Document Analysis Systems. Pages 263-269. 2008.
[bibtex] [pdf] [presentation] [abstract]

On Fusion of Range and Intensity Information Using Graph-Cut for Planar Patch Segmentation
Olaf Kähler and Erik Rodner and Joachim Denzler.
Proceedings Dynamic 3D Imaging Workshop. Pages 113-121. 2007. also appeared in International Journal of Intelligent Systems Technologies and Applications, Vol. 5, No. 3/4, pp.365-373
[bibtex] [abstract]