Due to the impressive advances in bio-medical imaging (fluorescence microscopy, Raman spectroscopy, endomicroscopy etc.), research in the life sciences has access to plenty of visual data in high resolution. Automatic processing and analysis of this data requires efficient and powerful image processing and machine learning algorithms.

Example for a semantic labeling automatically obtained (work with Sven Sickert).

2016

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.
German Conference on Pattern Recognition (GCPR). 51-63. 2016. BibTeX pdf www supplementary more ...

Abstract: In this paper, we investigate how to predict attributes of chimpanzees such as identity, age, age group, and gender. We build on convolutional neural networks, which lead to significantly superior results compared with previous state-of-the-art on hand-crafted recognition pipelines. In addition, we show how to further increase discrimination abilities of CNN activations by the Log-Euclidean framework on top of bilinear pooling. We finally introduce two curated datasets consisting of chimpanzee faces with detailed meta-information to stimulate further research. Our results can serve as the foundation for automated large-scale animal monitoring and analysis.
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). 193-204. 2016. BibTeX pdf www more ...

Abstract: We provide an alternative methodology for vegetation segmentation in cornfield images. The process includes two main steps, which makes the main contribution of this approach: (a) a low-level segmentation and (b) a class label assignment using Bag of Words (BoW) representation in conjunction with a supervised learning framework. The experimental results show our proposal is adequate to extract green plants in images of maize fields. The accuracy for classification is 95.3 % which is comparable to values in current literature.
Large-scale Active Learning with Approximated Expected Model Output Changes.
Christoph Käding and Alexander Freytag and Erik Rodner and Andrea Perino and Joachim Denzler.
German Conference on Pattern Recognition (GCPR). 179-191. 2016. BibTeX pdf www supplementary more ...

Abstract: Incremental learning of visual concepts is one step towards reaching human capabilities beyond closed-world assumptions. Besides recent progress, it remains one of the fundamental challenges in computer vision and machine learning. Along that path, techniques are needed which allow for actively selecting informative examples from a huge pool of unlabeled images to be annotated by application experts. Whereas a manifold of active learning techniques exists, they commonly suffer from one of two drawbacks: (i) either they do not work reliably on challenging real-world data or (ii) they are kernel-based and not scalable with the magnitudes of data current vision applications need to deal with. Therefore, we present an active learning and discovery approach which can deal with huge collections of unlabeled real-world data. Our approach is based on the expected model output change principle and overcomes previous scalability issues. We present experiments on the large-scale MS-COCO dataset and on a dataset provided by biodiversity researchers. Obtained results reveal that our technique clearly improves accuracy after just a few annotations. At the same time, it outperforms previous active learning approaches in academic and real-world scenarios.
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. 481-489. 2016. BibTeX pdf

2015

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 and Neck. 2015. in press BibTeX www
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). 510-517. 2015. BibTeX pdf www code more ...

Abstract: Classifying single image patches is important in many different applications, such as road detection or scene understanding. In this paper, we present convolutional patch networks, which are convolutional networks learned to distinguish different image patches and which can be used for pixel-wise labeling. We also show how to incorporate spatial information of the patch as an input to the network, which allows for learning spatial priors for certain categories jointly with an appearance model. In particular, we focus on road detection and urban scene understanding, two application areas where we are able to achieve state-of-the-art results on the KITTI as well as on the LabelMeFacade dataset. Furthermore, our paper offers a guideline for people working in the area and desperately wandering through all the painstaking details that render training CNs on image patches extremely difficult.
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. BibTeX pdf code presentation more ...

Abstract: In this paper, we present convolutional patch networks, which are convolutional (neural) networks (CNN) learned to distinguish different image patches and which can be used for pixel-wise labeling. We show how to easily learn spatial priors for certain categories jointly with their appearance. Experiments for urban scene understanding demonstrate state-of-the-art results on the LabelMeFacade dataset. Our approach is implemented as a new CNN framework especially designed for semantic segmentation with fully-convolutional architectures.
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

2014

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 www more ...

Abstract: Dieses Buch erlaeutert, wie Informationen automatisch aus Bildern extrahiert werden. Mit dieser sehr aktuellen Frage beschaeftigt sich das Buch mittels eines Streifzuges durch die Bildverarbeitung. Dabei werden sowohl die mathematischen Grundlagen vieler Verfahren der 2D- und 3D Bildanalyse vermittelt als auch deren Nutzen anhand von Problemstellungen aus vielen Bereichen (Medizin, industrielle Bildverarbeitung, Objekterkennung) erlaeutert. Das Buch eignet sich sowohl fuer Studierende der Informatik, Mathematik und Ingenieurwissenschaften als auch fuer Anwender aus der industriellen Bildverarbeitung.
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). 220-225. 2014. BibTeX pdf www more ...

Abstract: Automatic recognition of biological structures like membranes or synapses is important to analyze organic processes and to understand their functional behavior. To achieve this, volumetric images taken by electron microscopy or computed tomography have to be segmented into meaningful regions. We are extending iterative context forests which were developed for 2D image data for image stack segmentation. In particular, our method s able to learn high order dependencies and import contextual information, which often can not be learned by conventional Markov random field approaches usually used for this task. Our method is tested for very different and challenging medical and biological segmentation tasks.

2013

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): 512-517. 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. 29-37. 2013. BibTeX pdf www supplementary
One-class Classification with Gaussian Processes.
Michael Kemmler and Erik Rodner and Esther-Sabrina Wacker and Joachim Denzler.
Pattern Recognition. 3507-3518. 2013. BibTeX pdf

2011

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). 115-118. 2011. BibTeX pdf