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2020 – today
- 2022
[j18]Valerie Vaquet
, Patrick Menz, Udo Seiffert, Barbara Hammer:
Investigating intensity and transversal drift in hyperspectral imaging data. Neurocomputing 505: 68-79 (2022)- [c67]Johannes Brinkrolf, Valerie Vaquet, Fabian Hinder, Patrick Menz, Udo Seiffert, Barbara Hammer:
Federated learning vector quantization for dealing with drift between nodes. ESANN 2022 - [c66]Patrick Menz, Valerie Vaquet, Barbara Hammer, Udo Seiffert:
From hyperspectral to multispectral sensing - from simulation to reality: A comprehensive approach for calibration model transfer. ESANN 2022 - 2021
- [j17]Paul Herzig, Peter Borrmann, Uwe Knauer, Hans-Christian Klück, David Kilias, Udo Seiffert, Klaus Pillen, Andreas Maurer:
Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding. Remote. Sens. 13(14): 2670 (2021) - [c65]Valerie Vaquet, Patrick Menz, Udo Seiffert, Barbara Hammer:
Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data. ESANN 2021 - 2020
- [j16]Michiel Straat, Marika Kaden, Matthias Gay, Thomas Villmann, Alexander Lampe, Udo Seiffert, Michael Biehl, Friedrich Melchert:
Learning vector quantization and relevances in complex coefficient space. Neural Comput. Appl. 32(24): 18085-18099 (2020) - [j15]Friedrich Melchert, Gabriele Bani, Udo Seiffert, Michael Biehl:
Adaptive basis functions for prototype-based classification of functional data. Neural Comput. Appl. 32(24): 18213-18223 (2020) - [j14]Nele Bendel, Anna Kicherer, Andreas Backhaus, Janine Köckerling, Michael Maixner, Elvira Bleser, Hans-Christian Klück, Udo Seiffert, Ralf T. Voegele, Reinhard Töpfer:
Detection of Grapevine Leafroll-Associated Virus 1 and 3 in White and Red Grapevine Cultivars Using Hyperspectral Imaging. Remote. Sens. 12(10): 1693 (2020) - [j13]Nele Bendel, Andreas Backhaus, Anna Kicherer, Janine Köckerling, Michael Maixner, Barbara Jarausch, Sandra Biancu, Hans-Christian Klück, Udo Seiffert, Ralf T. Voegele, Reinhard Töpfer:
Detection of Two Different Grapevine Yellows in Vitis vinifera Using Hyperspectral Imaging. Remote. Sens. 12(24): 4151 (2020)
2010 – 2019
- 2019
- [j12]Uwe Knauer, Cornelius Styp von Rekowski, Marianne Stecklina, Tilman Krokotsch, Tuan Pham Minh, Viola Hauffe, David Kilias, Ina Ehrhardt, Herbert Sagischewski, Sergej Chmara, Udo Seiffert:
Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers. Remote. Sens. 11(23): 2788 (2019) - [c64]Patrick Menz, Andreas Backhaus, Udo Seiffert:
Transfer Learning for transferring machine-learning based models among hyperspectral sensors. ESANN 2019 - 2017
- [j11]Anna Kicherer, Katja Herzog, Nele Bendel, Hans-Christian Klück, Andreas Backhaus, Markus Wieland, Johann Christian Rose, Lasse Klingbeil, Thomas Läbe, Christian Hohl, Willi Petry, Heiner Kuhlmann, Udo Seiffert, Reinhard Töpfer:
Phenoliner: A New Field Phenotyping Platform for Grapevine Research. Sensors 17(7): 1625 (2017) - [c63]Michiel Straat, Marika Kaden, Matthias Gay, Thomas Villmann, Alexander Lampe, Udo Seiffert, Michael Biehl, Friedrich Melchert:
Prototypes and matrix relevance learning in complex fourier space. WSOM 2017: 139-144 - [c62]Gabriele Bani, Udo Seiffert, Michael Biehl, Friedrich Melchert:
Adaptive basis functions for prototype-based classification of functional data. WSOM 2017: 145-152 - [c61]Marika Kaden, David Nebel, Friedrich Melchert, Andreas Backhaus, Udo Seiffert, Thomas Villmann:
Data dependent evaluation of dissimilarities in nearest prototype vector quantizers regarding their discriminating abilities. WSOM 2017: 220-226 - 2016
- [c60]Friedrich Melchert, Udo Seiffert, Michael Biehl:
Functional Representation of Prototypes in LVQ and Relevance Learning. WSOM 2016: 317-327 - 2015
- [j10]Uwe Knauer, Andreas Backhaus, Udo Seiffert:
Fusion trees for fast and accurate classification of hyperspectral data with ensembles of γ-divergence-based RBF networks. Neural Comput. Appl. 26(2): 253-262 (2015) - [c59]Uwe Knauer, Andreas Backhaus, Udo Seiffert:
Evaluation of Fusion Methods for Gamma-Divergence-Based Neural Network Ensembles. SSCI 2015: 322-327 - 2014
- [j9]Udo Seiffert:
ANNIE - Artificial Neural Network-based Image Encoder. Neurocomputing 125: 229-235 (2014) - [j8]Andreas Backhaus, Udo Seiffert:
Classification in high-dimensional spectral data: Accuracy vs. interpretability vs. model size. Neurocomputing 131: 15-22 (2014) - [c58]Uwe Knauer, Udo Seiffert:
Fast image segmentation based on boosted random forests, integral images, and features on demand. CIEL 2014: 7-12 - [c57]Uwe Knauer, Andreas Backhaus, Udo Seiffert:
Beyond Standard Metrics - On the Selection and Combination of Distance Metrics for an Improved Classification of Hyperspectral Data. WSOM 2014: 167-177 - 2013
- [c56]Andreas Backhaus, Udo Seiffert:
Quantitative Measurements of model interpretability for the analysis of spectral data. CIDM 2013: 18-25 - [c55]Thomas Villmann, Marika Kästner, Andreas Backhaus, Udo Seiffert:
Processing Hyperspectral Data in Machine Learning. ESANN 2013 - [c54]Uwe Knauer, Udo Seiffert:
A comparison of late fusion methods for object detection. ICIP 2013: 3297-3301 - [c53]Uwe Knauer, Udo Seiffert:
Cascaded Reduction and Growing of Result Sets for Combining Object Detectors. MCS 2013: 121-133 - 2012
- [c52]Andreas Backhaus, Praveen Cheriyan Ashok, Bavishna Balagopal Praveen, Kishan Dholakia, Udo Seiffert:
Classifying Scotch Whisky from near-infrared Raman spectra with a Radial Basis Function Network with Relevance Learning. ESANN 2012 - [c51]Andreas Backhaus, Jan Lachmair, Ulrich Rückert, Udo Seiffert:
Hardware accelerated real time classification of hyperspectral imaging data for coffee sorting. ESANN 2012 - [c50]Wonsang You, Sophie Achard, Jörg Stadler, Bernd Brückner, Udo Seiffert:
Fractal analysis of resting state functional connectivity of the brain. IJCNN 2012: 1-8 - 2011
- [c49]Udo Seiffert, Frank-Michael Schleif, Dietlind Zühlke:
Recent trends in computational intelligence in life sciences. ESANN 2011 - [c48]Andreas Backhaus, Felix Bollenbeck, Udo Seiffert:
Robust classification of the nutrition state in crop plants by hyperspectral imaging and artificial neural networks. WHISPERS 2011: 1-4 - [c47]Felix Bollenbeck, Andreas Backhaus, Udo Seiffert:
A multivariate wavelet-PCA denoising-filter for hyperspectral images. WHISPERS 2011: 1-4 - [c46]Marika Kästner, Andreas Backhaus, Tina Geweniger, Sven Haase, Udo Seiffert, Thomas Villmann:
Relevance Learning in Unsupervised Vector Quantization Based on Divergences. WSOM 2011: 90-100 - 2010
- [c45]Andreas Backhaus, Asuka Kuwabara, Andrew Fleming, Udo Seiffert:
Validation of unsupervised clustering methods for leaf phenotype screening. ESANN 2010 - [c44]Felix Bollenbeck, Udo Seiffert:
Joint Registration and Segmentation of Histological Volume Data by Diffusion-Based Label Adaption. ICPR 2010: 2440-2443 - [c43]Udo Seiffert, Felix Bollenbeck, Hans-Peter Mock, Andrea Matros:
Clustering of crop phenotypes by means of hyperspectral signatures using artificial neural networks. WHISPERS 2010: 1-4
2000 – 2009
- 2009
- [j7]Wolfram Schoor, Felix Bollenbeck, Thomas Seidl, Diana Weier, Winfriede Weschke, Bernhard Preim, Udo Seiffert, Rüdiger Mecke:
VR Based Visualization and Exploration of Plant Biological Data. J. Virtual Real. Broadcast. 6 (2009) - [c42]Felix Bollenbeck, Stephanie Kaspar, Hans-Peter Mock, Diana Weier, Udo Seiffert:
Three-Dimensional Multimodality Modelling by Integration of High-Resolution Interindividual Atlases and Functional MALDI-IMS Data. BICoB 2009: 126-138 - [c41]Felix Bollenbeck, Rainer Pielot, Diana Weier, Winfriede Weschke, Udo Seiffert:
Inter-modality registration of NMRi and histological section images using neural networks regression in Gabor feature space. CIIP 2009: 27-32 - [c40]Marc Strickert, Frank-Michael Schleif, Thomas Villmann, Udo Seiffert:
Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data. Similarity-Based Clustering 2009: 70-91 - [c39]Wolfram Schoor, Rüdiger Mecke, Udo Seiffert, Felix Bollenbeck, Uwe Scholz:
Remote Rendering of Large Biological Datasets. GRAPP 2009: 223-227 - [c38]Felix Bollenbeck, Diana Weier, Wolfram Schoor, Udo Seiffert:
From Individual Intensity Voxel Data to Inter-individual Probabilistic Atlases of Biological Objects by an Interleaved Registration-segmentation Approach. VISAPP (1) 2009: 125-129 - [c37]Rainer Pielot, Udo Seiffert, Bertram Manz, Diana Weier, Frank Volke, Falk Schreiber, Winfriede Weschke:
Multimodal Registration of NMR-volumes and Histological Cross-sections of Barley Grains on the Cell Broadband Engine Processor. VISAPP (1) 2009: 241-244 - [p2]Felix Bollenbeck, Udo Seiffert:
Computational Intelligence in Biomedical Image Processing. Foundations of Computational Intelligence (5) 2009: 197-222 - 2008
- [j6]Marc Strickert, Frank-Michael Schleif, Udo Seiffert, Thomas Villmann:
Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data. Inteligencia Artif. 12(37): 37-44 (2008) - [c36]Thomas Villmann, Erzsébet Merényi, Udo Seiffert:
Machine learning approches and pattern recognition for spectral data. ESANN 2008: 433-444 - [c35]Udo Seiffert, Felix Bollenbeck:
Fuzzy image segmentation by potential fields. FUZZ-IEEE 2008: 1118-1123 - [c34]Felix Bollenbeck, Udo Seiffert:
Fast registration-based automatic segmentation of serial section images for high-resolution 3-D plant seed modeling. ISBI 2008: 352-355 - [c33]Rainer Pielot, Udo Seiffert, Bertram Manz, Diana Weier, Frank Volke, Winfriede Weschke:
4D Warping for Analysing Morphological Changes in Seed Development of Barley Grains. VISAPP (1) 2008: 335-340 - [p1]Vincent Jasper Dercksen, Cornelia Brüß, Detlev Stalling, Sabine Gubatz, Udo Seiffert, Hans-Christian Hege:
Towards Automatic Generation of 3D Models of Biological Objects Based on Serial Sections. Visualization in Medicine and Life Sciences 2008: 3-25 - 2007
- [j5]Marc Strickert, Nese Sreenivasulu, Björn Usadel, Udo Seiffert:
Correlation-maximizing surrogate gene space for visual mining of gene expression patterns in developing barley endosperm tissue. BMC Bioinform. 8 (2007) - [c32]Thomas Villmann, Marc Strickert, Cornelia Brüß, Frank-Michael Schleif, Udo Seiffert:
Visualization of Fuzzy Information in Fuzzy-Classification for Image Segmentation using MDS. ESANN 2007: 103-108 - [c31]Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann, Marc Strickert, Udo Seiffert:
Intuitive Clustering of Biological Data. IJCNN 2007: 1877-1882 - [i1]Marc Strickert, Udo Seiffert:
Correlation-based Data Representation. Similarity-based Clustering and its Application to Medicine and Biology 2007 - 2006
- [j4]Marc Strickert, Udo Seiffert, Nese Sreenivasulu, Winfriede Weschke, Thomas Villmann, Barbara Hammer:
Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis. Neurocomputing 69(7-9): 651-659 (2006) - [c30]Thomas Villmann, Udo Seiffert, Frank-Michael Schleif, Cornelia Brüß, Tina Geweniger, Barbara Hammer:
Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. ANNPR 2006: 46-56 - [c29]Marc Strickert, Nese Sreenivasulu, Silke Peterek, Winfriede Weschke, Hans-Peter Mock, Udo Seiffert:
Unsupervised Feature Selection for Biomarker Identification in Chromatography and Gene Expression Data. ANNPR 2006: 274-285 - [c28]Cornelia Brüß, Marc Strickert, Udo Seiffert:
Towards Automatic Segmentation of Serial High-Resolution Images. Bildverarbeitung für die Medizin 2006: 126-130 - [c27]Thomas Villmann, Barbara Hammer, Udo Seiffert:
Perspectives of Self-adapted Self-organizing Clustering in Organic Computing. BioADIT 2006: 141-159 - [c26]Marc Strickert, Nese Sreenivasulu, Udo Seiffert:
Sanger-driven MDSLocalize - a comparative study for genomic data. ESANN 2006: 265-270 - [c25]Udo Seiffert, Barbara Hammer, Samuel Kaski, Thomas Villmann:
Neural networks and machine learning in bioinformatics - theory and applications. ESANN 2006: 521-532 - [c24]Cornelia Brüß, Felix Bollenbeck, Frank-Michael Schleif, Winfriede Weschke, Thomas Villmann, Udo Seiffert:
Fuzzy image segmentation with Fuzzy Labelled Neural Gas. ESANN 2006: 563-568 - [c23]Udo Seiffert:
Training of Large-Scale Feed-Forward Neural Networks. IJCNN 2006: 5324-5329 - 2005
- [c22]Udo Seiffert:
Adaptive Implementation of Artificial Neural Networks Reflecting Changing Hardware Resources at Run-Time. Artificial Intelligence and Applications 2005: 733-737 - [c21]Marc Strickert, Nese Sreenivasulu, Winfriede Weschke, Udo Seiffert, Thomas Villmann:
Generalized Relevance LVQ with Correlation Measures for Biological Data. ESANN 2005: 331-338 - [c20]Marc Strickert, Stefan Teichmann, Nese Sreenivasulu, Udo Seiffert:
High-Throughput Multi-dimensional Scaling (HiT-MDS) for cDNA-Array Expression Data. ICANN (1) 2005: 625-633 - [c19]Ralf Tautenhahn, Alexander Ihlow, Udo Seiffert:
Adaptive Feature Selection for Classification of Microscope Images. WILF 2005: 215-222 - 2004
- [j3]Udo Seiffert:
Artificial neural networks on massively parallel computer hardware. Neurocomputing 57: 135-150 (2004) - [c18]Udo Seiffert:
Biologically Inspired Image Compression in Biomedical High-Throughput Screening. BioADIT 2004: 428-439 - [c17]Alexander Ihlow, Udo Seiffert:
Automating Microscope Colour Image Analysis Using the Expectation Maximisation Algorithm. DAGM-Symposium 2004: 536-543 - [c16]Thomas Villmann, Udo Seiffert, Axel Wismüller:
Theory and applications of neural maps. ESANN 2004: 25-38 - [c15]Alexander Ihlow, Udo Seiffert:
Haustoria Segmentation in Microscope Colour Images of Barley Cells. Workshop Farbbildverarbeitung 2004: 119-126 - [c14]R. Rebmann, Bernd Michaelis, Gerald Krell, Udo Seiffert, F. Püschel:
Improving Image Processing Systems by Artificial Neural Networks. Reading and Learning 2004: 37-64 - 2003
- [c13]Gerald Krell, R. Rebmann, Udo Seiffert, Bernd Michaelis:
Improving Still Image Coding by an SOM-Controlled Associative Memory. CIARP 2003: 571-579 - [c12]R. Rebmann, Gerald Krell, Udo Seiffert, Bernd Michaelis:
Associative Correction Of Compression Artefacts With A Self-Organizing Map Classifying The Image Content. DCC 2003: 446 - [c11]Axel Wismüller, Udo Seiffert:
Digital Image Processing with Neural Networks. ESANN 2003: 493-502 - 2002
- [c10]Udo Seiffert:
Artificial Neural Networks on Massively Parallel Computer Hardware. ESANN 2002: 319-330 - 2001
- [c9]Udo Seiffert:
Multiple Layer Perceptron training using genetic algorithms. ESANN 2001: 159-164 - [c8]Vaijayanti Joshi, Lakhmi C. Jain, Udo Seiffert, Kathleen Zyga, Richard Price, Friedrich Leisch:
Neural Techniques in Logo Recognition. HIS 2001: 25-32 - [c7]Udo Seiffert, Bernd Michaelis:
Multi-Dimensional Self-Organizing Maps on Massively Parallel Hardware. WSOM 2001: 160-166
1990 – 1999
- 1999
- [c6]Udo Seiffert, Bernd Michaelis:
Indirect Unsuperivised Training of Backpropagation Nets. SIP 1999: 328-332 - 1998
- [b1]Udo Seiffert:
Wachsende mehrdimensionale selbstorganisierende Karten zur Analyse bewegter Szenen. Otto-von-Guericke University Magdeburg, Germany, 1998, pp. 1-158 - 1997
- [j2]Udo Seiffert, Bernd Michaelis:
Growing 3D-SOMs with 2D-Input Layer as a Classification Tool in a Motion Detection System. Int. J. Neural Syst. 8(1): 81-89 (1997) - [j1]Udo Seiffert, Bernd Michaelis:
Estimating Motion Parameters with Three-Dimensional Self-Organizing Maps. Inf. Sci. 101(3-4): 187-201 (1997) - [c5]G. Sommerkorn, Udo Seiffert, D. Surmeli, Andreas Herzog, Bernd Michaelis, Katharina Braun:
Classification of 3-D Dendritic Spines using Self-Organizing Maps. ICANNGA 1997: 129-132 - 1996
- [c4]Udo Seiffert, Bernd Michaelis:
Adaptive three-dimensional self-organizing map with a two-dimensional input layer. ANZIIS 1996: 258-263 - [c3]Andreas Herzog, G. Sommerkorn, Udo Seiffert, Bernd Michaelis, Katharina Braun, Werner Zuschratter:
Rekonstruktion und Klassifikation dendritischer Spines aus konfokalen Bilddaten. Bildverarbeitung für die Medizin 1996 - [c2]Bernd Michaelis, Olaf Schnelting, Udo Seiffert, Rüdiger Mecke:
Adaptive filtering of distorted displacement vector fields using artificial neural networks. ICPR 1996: 335-339 - 1995
- [c1]Udo Seiffert, Bernd Michaelis:
Three-dimensional Self-Organizing Maps for Classification of Image Properties. ANNES 1995: 310-313
Coauthor Index
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