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2020 – today
- 2024
[i1]Huan Zhou, Ralf Schneider, Sebastian Klüsener, Andreas Backhaus:
A new framework for calibrating COVID-19 SEIR models with spatial-/time-varying coefficients using genetic and sliding window algorithms. CoRR abs/2402.08524 (2024)- 2021
- [j7]Florian Becker, Andreas Backhaus, Felix Johrden, Merle Flitter:
Optimal multispectral sensor configurations through machine learning for cognitive agriculture. Autom. 69(4): 336-344 (2021) - 2020
- [j6]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) - [j5]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
- [c15]Patrick Menz, Andreas Backhaus, Udo Seiffert:
Transfer Learning for transferring machine-learning based models among hyperspectral sensors. ESANN 2019 - 2017
- [j4]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) - [c14]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 - 2015
- [j3]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) - [c13]Uwe Knauer, Andreas Backhaus, Udo Seiffert:
Evaluation of Fusion Methods for Gamma-Divergence-Based Neural Network Ensembles. SSCI 2015: 322-327 - 2014
- [j2]Andreas Backhaus, Udo Seiffert:
Classification in high-dimensional spectral data: Accuracy vs. interpretability vs. model size. Neurocomputing 131: 15-22 (2014) - [c12]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
- [c11]Andreas Backhaus, Udo Seiffert:
Quantitative Measurements of model interpretability for the analysis of spectral data. CIDM 2013: 18-25 - [c10]Thomas Villmann, Marika Kästner, Andreas Backhaus, Udo Seiffert:
Processing Hyperspectral Data in Machine Learning. ESANN 2013 - 2012
- [c9]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 - [c8]Andreas Backhaus, Jan Lachmair, Ulrich Rückert, Udo Seiffert:
Hardware accelerated real time classification of hyperspectral imaging data for coffee sorting. ESANN 2012 - 2011
- [j1]Dietmar Heinke, Andreas Backhaus:
Modelling Visual Search with the Selective Attention for Identification Model (VS-SAIM): A Novel Explanation for Visual Search Asymmetries. Cogn. Comput. 3(1): 185-205 (2011) - [c7]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 - [c6]Felix Bollenbeck, Andreas Backhaus, Udo Seiffert:
A multivariate wavelet-PCA denoising-filter for hyperspectral images. WHISPERS 2011: 1-4 - [c5]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
- [c4]Andreas Backhaus, Asuka Kuwabara, Andrew Fleming, Udo Seiffert:
Validation of unsupervised clustering methods for leaf phenotype screening. ESANN 2010
2000 – 2009
- 2007
- [c3]Dietmar Heinke, Andreas Backhaus, Yaoru Sun, Glyn W. Humphreys:
The Selective Attention for Identification Model (SAIM): Simulating Visual Search in Natural Colour Images. WAPCV 2007: 141-154 - 2005
- [c2]Andreas Backhaus, Dietmar Heinke, Glyn W. Humphreys:
Contextual Learning in the Selective Attention for Identification model (CL-SAIM): Modeling contextual cueing in visual search tasks. CVPR Workshops 2005: 87 - 2004
- [c1]Torsten Wilhelm, Hans-Joachim Böhme, Horst-Michael Gross, Andreas Backhaus:
Statistical and neural methods for vision-based analysis of facial expressions and gender. SMC (3) 2004: 2203-2208
Coauthor Index
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last updated on 2024-10-07 21:23 CEST by the dblp team
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