Andreas Backhaus

23 papers B 5C 1Journal 8Unranked 9
YearRankTypeTitle / Venue / Authors
2024 J jnl
A new framework for calibrating COVID-19 SEIR models with spatial-/time-varying coefficients using genetic and sliding window algorithms.
CoRR
Huan Zhou, Ralf Schneider, Sebastian Klüsener, Andreas Backhaus
2021 J jnl
Optimal multispectral sensor configurations through machine learning for cognitive agriculture.
Autom.
Florian Becker, Andreas Backhaus, Felix Johrden, Merle Flitter
2020 J jnl
Detection of Grapevine Leafroll-Associated Virus 1 and 3 in White and Red Grapevine Cultivars Using Hyperspectral Imaging.
Remote. Sens.
Nele Bendel, Anna Kicherer, Andreas Backhaus, Janine Köckerling, Michael Maixner, Elvira Bleser, Hans-Christian Klück, Udo Seiffert, Ralf T. Voegele, Reinhard Töpfer
2020 J jnl
Detection of Two Different Grapevine Yellows in Vitis vinifera Using Hyperspectral Imaging.
Remote. Sens.
Nele Bendel, Andreas Backhaus, Anna Kicherer, Janine Köckerling, Michael Maixner, Barbara Jarausch, Sandra Biancu, Hans-Christian Klück, Udo Seiffert, Ralf T. Voegele, Reinhard Töpfer
2019 B conf
Transfer Learning for transferring machine-learning based models among hyperspectral sensors.
ESANN
Patrick Menz, Andreas Backhaus, Udo Seiffert
2017 — conf
Data dependent evaluation of dissimilarities in nearest prototype vector quantizers regarding their discriminating abilities.
WSOM
Marika Kaden, David Nebel, Friedrich Melchert, Andreas Backhaus, Udo Seiffert, Thomas Villmann
2017 J jnl
Phenoliner: A New Field Phenotyping Platform for Grapevine Research.
Sensors
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
2015 — conf
Evaluation of Fusion Methods for Gamma-Divergence-Based Neural Network Ensembles.
SSCI
Uwe Knauer, Andreas Backhaus, Udo Seiffert
2015 J jnl
Fusion trees for fast and accurate classification of hyperspectral data with ensembles of γ-divergence-based RBF networks.
Neural Comput. Appl.
Uwe Knauer, Andreas Backhaus, Udo Seiffert
2014 — conf
Beyond Standard Metrics - On the Selection and Combination of Distance Metrics for an Improved Classification of Hyperspectral Data.
WSOM
Uwe Knauer, Andreas Backhaus, Udo Seiffert
2014 J jnl
Classification in high-dimensional spectral data: Accuracy vs. interpretability vs. model size.
Neurocomputing
Andreas Backhaus, Udo Seiffert
2013 B conf
Processing Hyperspectral Data in Machine Learning.
ESANN
Thomas Villmann, Marika Kästner, Andreas Backhaus, Udo Seiffert
2013 C conf
Quantitative Measurements of model interpretability for the analysis of spectral data.
CIDM
Andreas Backhaus, Udo Seiffert
2012 B conf
Classifying Scotch Whisky from near-infrared Raman spectra with a Radial Basis Function Network with Relevance Learning.
ESANN
Andreas Backhaus, Praveen Cheriyan Ashok, Bavishna Balagopal Praveen, Kishan Dholakia, Udo Seiffert
2012 B conf
Hardware accelerated real time classification of hyperspectral imaging data for coffee sorting.
ESANN
Andreas Backhaus, Jan Lachmair, Ulrich Rückert, Udo Seiffert
2011 — conf
A multivariate wavelet-PCA denoising-filter for hyperspectral images.
WHISPERS
Felix Bollenbeck, Andreas Backhaus, Udo Seiffert
2011 J jnl
Modelling Visual Search with the Selective Attention for Identification Model (VS-SAIM): A Novel Explanation for Visual Search Asymmetries.
Cogn. Comput.
Dietmar Heinke, Andreas Backhaus
2011 — conf
Relevance Learning in Unsupervised Vector Quantization Based on Divergences.
WSOM
Marika Kästner, Andreas Backhaus, Tina Geweniger, Sven Haase, Udo Seiffert, Thomas Villmann
2011 — conf
Robust classification of the nutrition state in crop plants by hyperspectral imaging and artificial neural networks.
WHISPERS
Andreas Backhaus, Felix Bollenbeck, Udo Seiffert
2010 B conf
Validation of unsupervised clustering methods for leaf phenotype screening.
ESANN
Andreas Backhaus, Asuka Kuwabara, Andrew Fleming, Udo Seiffert
2007 — conf
The Selective Attention for Identification Model (SAIM): Simulating Visual Search in Natural Colour Images.
WAPCV
Dietmar Heinke, Andreas Backhaus, Yaoru Sun, Glyn W. Humphreys
2005 — conf
Contextual Learning in the Selective Attention for Identification model (CL-SAIM): Modeling contextual cueing in visual search tasks.
CVPR Workshops
Andreas Backhaus, Dietmar Heinke, Glyn W. Humphreys
2004 — conf
Statistical and neural methods for vision-based analysis of facial expressions and gender.
SMC (3)
Torsten Wilhelm, Hans-Joachim Böhme, Horst-Michael Gross, Andreas Backhaus