TY  - BOOK
A1  - Lackner, Maximilian
A1  - Grabow, Wilhelm
A1  - Stadler, Philipp
T1  - Handbook of Online and Near-real-time Methods in Microbiology
KW  - Microbiology
Y1  - 2018
ER  - 
TY  - GEN
A1  - Lackner, Maximilian
T1  - PBAT - A versatile bioplastics
KW  - Bioplastics
Y1  - 2018
ER  - 
TY  - BOOK
A1  - Lackner, Maximilian
T1  - Chemical Engineering Vocabulary: Bilingual
KW  - Chemical Engineering
KW  - Language
Y1  - 2018
ER  - 
TY  - JOUR
A1  - Spitzer-Sonnleitner, Birgit
A1  - Kempe, Andre
A1  - Lackner, Maximilian
T1  - Influence of halide solutions on collagen networks - measurements of physical properties by atomic force microscopy (AFM)
JF  - Influence of halide solutions on collagen networks
KW  - Collagen Networks
KW  - Microscopy
Y1  - 2018
ER  - 
TY  - JOUR
A1  - Wöber, Wilfried
A1  - Mehnen, Lars
A1  - Curto, Manuel
A1  - Dias Tibihika, Papius
A1  - Tesfaye, Genanaw
A1  - Meimberg, Harald
T1  - Investigating Shape Variation Using Generalized Procrustes Analysis and Machine Learning
JF  - Applied Sciences
N2  - Abstract: The biological investigation of a population’s shape diversity using digital images is typi-
cally reliant on geometrical morphometrics, which is an approach based on user-defined landmarks.
In contrast to this traditional approach, the progress in deep learning has led to numerous applications
ranging from specimen identification to object detection. Typically, these models tend to become black
boxes, which limits the usage of recent deep learning models for biological applications. However, the
progress in explainable artificial intelligence tries to overcome this limitation. This study compares
the explanatory power of unsupervised machine learning models to traditional landmark-based
approaches for population structure investigation. We apply convolutional autoencoders as well
as Gaussian process latent variable models to two Nile tilapia datasets to investigate the latent
structure using consensus clustering. The explanatory factors of the machine learning models were
extracted and compared to generalized Procrustes analysis. Hypotheses based on the Bayes factor are
formulated to test the unambiguity of population diversity unveiled by the machine learning models.
The findings show that it is possible to obtain biologically meaningful results relying on unsupervised
machine learning. Furthermore we show that the machine learning models unveil latent structures
close to the true population clusters. We found that 80% of the true population clusters relying on
the convolutional autoencoder are significantly different to the remaining clusters. Similarly, 60% of
the true population clusters relying on the Gaussian process latent variable model are significantly
different. We conclude that the machine learning models outperform generalized Procrustes analysis,
where 16% of the population cluster was found to be significantly different. However, the applied
machine learning models still have limited biological explainability. We recommend further in-depth
investigations to unveil the explanatory factors in the used model.

Keywords: generalized procrustes analysis; machine learning; convolutional autoencoder; Gaussian
process latent variable models
KW  - generalized procrustes analysis
KW  - machine learning
KW  - convolutional autoencoder
KW  - Gaussian process latent variable models
Y1  - 
VL  - 2022
IS  - 12(6), 3158
ER  - 
TY  - JOUR
A1  - Wöber, Wilfried
A1  - Curto, Manuel
A1  - Tibihika, Papius D.
A1  - Meulenboek, Paul
A1  - Alemayehu, Esayas
A1  - Mehnen, Lars
A1  - Meimberg, Harald
A1  - Sykacek, Peter
T1  - Identifying geographically differentiated features of Ethopian Nile tilapia (Oreochromis niloticus) morphology with machine learning
JF  - PlosONE
KW  - Machine Learning
Y1  - 
VL  - 16
IS  - 4
ER  - 
TY  - CHAP
A1  - Wöber, Wilfried
A1  - Tibihika, Papius D
A1  - Olaverri-Monreal, Cristina
A1  - Mehnen, Lars
A1  - Sykacek, Peter
A1  - Meimberg, Harald
T1  - Comparison of Unsupervised Learning Methods for Natural Image Processing
T2  - Biodiversity Information Science and Standards
KW  - Machine Learning
KW  - Deep Learning
KW  - Image Processing
Y1  - 
IS  - 3
ER  - 
TY  - CHAP
A1  - Wöber, Wilfried
A1  - Szuegyi, Daniel
A1  - Kubinger, Wilfried
A1  - Mehnen, Lars
T1  - A principal component analysis based object detection for thermal infra-red images
T2  - Proceedings of the 55th International Symposium ELMAR
KW  - Principal Component Analysis
KW  - Object Detection
KW  - Infra-Red Camera
Y1  - 2019
ER  - 
TY  - GEN
A1  - Spulak, David
A1  - Otrebski, Richard
A1  - Kubinger, Wilfried
T1  - Object Tracking by Combining Supervised and Adaptive Online Learning through Sensor Fusion of Multiple Stereo Camera Systems
KW  - Object Tracking
Y1  - 
ER  - 
TY  - CHAP
A1  - Wöber, Wilfried
A1  - Aburaia, Mohamed
A1  - Olaverri-Monreal, Cristina
T1  - Classification of Streetsigns Using Gaussian Process Latent Variable Models
T2  - 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE), Graz, Austria
KW  - Streetsigns
KW  - Gaussian
KW  - Vehicles
Y1  - 2020
ER  -