U. Seiffert

发表

HyphArea--automated analysis of spatiotemporal fungal patterns.

T. Baum, A. Navarro-Quezada, W. Knogge, 2011, Journal of plant physiology.

Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images

U. Knauer, U. Seiffert, A. Matros, 2017, Plant Methods.

Functional approximation for the classification of smooth time series

Michael Biehl, U. Seiffert, F. Melchert, 2016 .

Adaptive color spaces based on multivariate Gaussian distributions for color image segmentation

U. Seiffert, A. Ihlow, 2014 .

Label-free proteome profiling reveals developmental-dependent patterns in young barley grains.

A. Hartmann, U. Seiffert, W. Weschke, 2016, Journal of proteomics.

Combining Phenotypic and Genotypic Learning

U. Seiffert, 2008 .

Genetic regulation of growth and nutrient content under phosphorus deficiency in the wild barley introgression library S42IL

Hans-Peter Mock, Udo Seiffert, Klaus Pillen, 2017 .

Phenotyping and Genotyping of Plants

U. Seiffert, Andreas Herzog, 2020 .

Identification and Classification of Contaminations on Wafers Using Hyperspectral Imaging

Udo Seiffert, Sylke Meyer, Andreas Backhaus, 2016 .

Non-invasive Presymptomatic Detection of Cercospora beticola Infection and Identification of Early Metabolic Responses in Sugar Beet

Hans-Peter Mock, Udo Seiffert, Andreas Backhaus, 2016, Front. Plant Sci..

Gradients of Pearson Correlation for Analysis of Biomedical Data

U. Seiffert, M. Strickert, F. Schleif, 2007 .

Improving Image Processing Systems by Artificial Neural Networks

Gerald Krell, Bernd Michaelis, Udo Seiffert, 2004, Reading and Learning.

Visualizing and Exploring Barley Grain Models in VR with the Elbe Dom Immersive Laser Projection System

U. Seiffert, W. Schoor, S. Masik, 2008 .

A high-throughput screening system for barley/powdery mildew interactions based on automated analysis of light micrographs

P. Schweizer, U. Seiffert, A. Ihlow, 2008, BMC Plant Biology.

Microphenomics for Interactions of Barley with Fungal Pathogens

T. Baum, D. Douchkov, P. Schweizer, 2014 .

Polynomial Approximation of Spectral Data in LVQ and Relevance Learning

Michael Biehl, U. Seiffert, F. Melchert, 2015 .

Evaluating the suitability of hyper- and multispectral imaging to detect foliar symptoms of the grapevine trunk disease Esca in vineyards

Andreas Backhaus, U. Seiffert, M. Fischer, 2020, Plant methods.

“Macrobot”: An Automated Segmentation-Based System for Powdery Mildew Disease Quantification

Andreas Backhaus, D. Douchkov, P. Schweizer, 2020, Plant phenomics.

“Macrobot”: An Automated Segmentation-Based System for Powdery Mildew Disease Quantification

Andreas Backhaus, D. Douchkov, U. Seiffert, 2020, Plant phenomics.

An Approach for Automated Registration of Hyperspectral Images for Boresight Calibration

U. Knauer, U. Seiffert, 2015 .

Determination of Fructans in Plants: Current Analytical Means for Extraction, Detection, and Quantification

U. Seiffert, R. Burton, A. Matros, 2019, Annual Plant Reviews online.

Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data

U. Seiffert, Barbara Hammer, Valerie Vaquet, 2022, ESANN.

Learning vector quantization and relevances in complex coefficient space

M. Biehl, T. Villmann, U. Seiffert, 2019, Neural Computing and Applications.

Microscope color image segmentation for resistance analysis of barley cells against powdery mildew

Udo Seiffert, Alexander Ihlow, U. Seiffert, 2014 .

Computational Intelligence in Biomedical Image Processing

Felix Bollenbeck, Udo Seiffert, U. Seiffert, 2009, Foundations of Computational Intelligence.

Perspectives of Self-adapted Self-organizing Clustering in Organic Computing

Thomas Villmann, Barbara Hammer, Udo Seiffert, 2006, BioADIT.

Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data

Thomas Villmann, Frank-Michael Schleif, Marc Strickert, 2009, Similarity-Based Clustering.

Correlation-maximizing surrogate gene space for visual mining of gene expression patterns in developing barley endosperm tissue

Marc Strickert, Udo Seiffert, Björn Usadel, 2007, BMC Bioinformatics.

Machine Learning Reports

Michael Biehl, U. Seiffert, A. Matros, 2016 .

Dynamics and genetic regulation of leaf nutrient concentration in barley based on hyperspectral imaging and machine learning.

Andreas Backhaus, U. Seiffert, K. Pillen, 2021, Plant science : an international journal of experimental plant biology.

Genetic dissection of grain elements predicted by hyperspectral imaging associated with yield-related traits in a wild barley NAM population.

Udo Seiffert, Klaus Pillen, Andreas Maurer, 2019, Plant science : an international journal of experimental plant biology.

Necrosis-inducing proteins of Rhynchosporium commune, effectors in quantitative disease resistance.

A. Leitner, T. Baum, A. Navarro-Quezada, 2012, Molecular plant-microbe interactions : MPMI.

Clustering of crop phenotypes by means of hyperspectral signatures using artificial neural networks

Andrea Matros, Hans-Peter Mock, Felix Bollenbeck, 2010, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

Barley grain development toward an integrative view.

Hans-Peter Mock, Udo Seiffert, Björn H. Junker, 2010, International review of cell and molecular biology.

Inter-modality registration of NMRi and histological section images using neural networks regression in Gabor feature space

Felix Bollenbeck, Udo Seiffert, Rainer Pielot, 2009, 2009 IEEE Symposium on Computational Intelligence for Image Processing.

Growing multi-dimensional self-organizing maps for motion detection

Udo Seiffert, U. Seiffert, 2001 .

Detection of Grapevine Leafroll-Associated Virus 1 and 3 in White and Red Grapevine Cultivars Using Hyperspectral Imaging

Udo Seiffert, Michael Maixner, Nele Bendel, 2020, Remote. Sens..

Processing Hyperspectral Data in Machine Learning

Thomas Villmann, Udo Seiffert, Marika Kaden, 2013, ESANN.

Adaptive basis functions for prototype-based classification of functional data

Michael Biehl, Friedrich Melchert, Udo Seiffert, 2017, Neural Computing and Applications.

Theory and applications of neural maps

Thomas Villmann, Axel Wismüller, Udo Seiffert, 2004, ESANN.

Artificial Neural Networks on Massively Parallel Computer Hardware

Udo Seiffert, U. Seiffert, 2004, ESANN.

Multi-Dimensional Self-Organizing Maps on Massively Parallel Hardware

Bernd Michaelis, Udo Seiffert, B. Michaelis, 2001, WSOM.

A pattern recognition tool for quantitative analysis of in planta hyphal growth of powdery mildew fungi.

U Seiffert, P. Schweizer, U. Seiffert, 2005, Molecular plant-microbe interactions : MPMI.

High-Throughput Multi-dimensional Scaling (HiT-MDS) for cDNA-Array Expression Data

Marc Strickert, Udo Seiffert, Nese Sreenivasulu, 2005, ICANN.

DETECTION OF TUMOR IN DIGITAL IMAGES OF THE BRAIN

B. Michaelis, U. Seiffert, ANGELO ZIZZARI, 2001 .

Multiple Layer Perceptron training using genetic algorithms

Udo Seiffert, U. Seiffert, 2001, ESANN.

Mass Spectrometry Imaging of Metabolites in Barley Grain Tissues.

U. Seiffert, A. Matros, Manuela Peukert, 2016, Current protocols in plant biology.

ANNIE - Artificial Neural Network-based Image Encoder

Udo Seiffert, U. Seiffert, 2014, Neurocomputing.

Phenoliner: A New Field Phenotyping Platform for Grapevine Research

Udo Seiffert, Heiner Kuhlmann, Katja Herzog, 2017, Sensors.

Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis

Thomas Villmann, Barbara Hammer, Marc Strickert, 2006, Neurocomputing.

Generalized Relevance LVQ with Correlation Measures for Biological Data

T. Villmann, U. Seiffert, N. Sreenivasulu, 2005, ESANN.

Correlation-based Data Representation

Marc Strickert, Udo Seiffert, U. Seiffert, 2007, Similarity-based Clustering and its Application to Medicine and Biology.

Machine learning approches and pattern recognition for spectral data

Thomas Villmann, Erzsébet Merényi, Udo Seiffert, 2008, ESANN.

Relevance Learning in Unsupervised Vector Quantization Based on Divergences

Thomas Villmann, Sven Haase, Udo Seiffert, 2011, WSOM.

Detection of Two Different Grapevine Yellows in Vitis vinifera Using Hyperspectral Imaging

Nele Bendel, Andreas Backhaus, Anna Kicherer, 2020, Remote. Sens..

Biologically Inspired Image Compression in Biomedical High-Throughput Screening

Udo Seiffert, U. Seiffert, 2004, BioADIT.

Content Adaptive Compression of Images Using Neural Maps Content Adaptive Compression of Images Using Neural Maps

Udo Seiffert, U. Seiffert, 2005 .

Fast image segmentation based on boosted random forests, integral images, and features on demand

Udo Seiffert, Uwe Knauer, U. Knauer, 2014, 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL).

Adaptive Implementation of Artificial Neural Networks Reflecting Changing Hardware Resources at Run-Time

Udo Seiffert, U. Seiffert, 2005, Artificial Intelligence and Applications.

Training of Large-Scale Feed-Forward Neural Networks

Udo Seiffert, U. Seiffert, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers

Udo Seiffert, Ina Ehrhardt, Uwe Knauer, 2019, Remote. Sens..

Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding

Udo Seiffert, Uwe Knauer, Klaus Pillen, 2021, Remote. Sens..

Genome-wide association study reveals the genetic complexity of fructan accumulation patterns in barley grain.

Matthew R. Tucker, U. Seiffert, B. Berger, 2021, Journal of experimental botany.

GWAS reveals the genetic complexity of fructan accumulation patterns in barley grain

Matthew R. Tucker, U. Seiffert, B. Berger, 2020, bioRxiv.

Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images

U. Knauer, U. Seiffert, A. Matros, 2017, Plant Methods.

Classification in high-dimensional spectral data: Accuracy vs. interpretability vs. model size

Udo Seiffert, Andreas Backhaus, Andreas Backhaus, 2014, Neurocomputing.

Robust classification of the nutrition state in crop plants by hyperspectral imaging and artificial neural networks

Felix Bollenbeck, Udo Seiffert, Andreas Backhaus, 2011, 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

Cascaded Reduction and Growing of Result Sets for Combining Object Detectors

Udo Seiffert, Uwe Knauer, U. Knauer, 2013, MCS.

MIWOCI 2010, Mittweida Workshop on Computational Intelligence

T. Villmann, F. M. Schleif, U. Seiffert, 2010 .

Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging

Mathias Wiegmann, Andreas Backhaus, Udo Seiffert, 2019, PloS one.

Sanger-driven MDSLocalize - a comparative study for genomic data

Marc Strickert, Udo Seiffert, Nese Sreenivasulu, 2006, ESANN.

Neural networks and machine learning in bioinformatics - theory and applications

Thomas Villmann, Barbara Hammer, Samuel Kaski, 2006, ESANN.

Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data

Thomas Villmann, Frank-Michael Schleif, Marc Strickert, 2008, Inteligencia Artif..

Self-Organizing neural networks: recent advances and applications

Lakhmi C. Jain, Udo Seiffert, L. Jain, 2001 .

Bioinformatics using computational intelligence paradigms

Lakhmi C. Jain, Udo Seiffert, Patric Schweizer, 2005 .

Functional Representation of Prototypes in LVQ and Relevance Learning

Michael Biehl, Friedrich Melchert, Udo Seiffert, 2016, WSOM.