@book{LacknerGrabowStadler,
  author    = {Lackner, Maximilian and Grabow, Wilhelm and Stadler, Philipp},
  title     = {Handbook of Online and Near-real-time Methods in Microbiology},
  publisher      = {Fachhochschule Technikum Wien},
  subject      = {Microbiology},
  language  = {en}
}
@misc{Lackner,
  author    = {Lackner, Maximilian},
  title     = {PBAT - A versatile bioplastics},
  subject      = {Bioplastics},
  language  = {en}
}
@book{Lackner,
  author    = {Lackner, Maximilian},
  title     = {Chemical Engineering Vocabulary: Bilingual},
  publisher      = {Fachhochschule Technikum Wien},
  subject      = {Chemical Engineering},
  language  = {en}
}
@article{SpitzerSonnleitnerKempeLackner,
  author    = {Spitzer-Sonnleitner, Birgit and Kempe, Andre and Lackner, Maximilian},
  title     = {Influence of halide solutions on collagen networks - measurements of physical properties by atomic force microscopy (AFM)},
  series = {Influence of halide solutions on collagen networks},
  journal   = {Influence of halide solutions on collagen networks},
  subject      = {Collagen Networks},
  language  = {en}
}
@article{WoeberMehnenCurtoetal.,
  author    = {W{\"o}ber, Wilfried and Mehnen, Lars and Curto, Manuel and Dias Tibihika, Papius and Tesfaye, Genanaw and Meimberg, Harald},
  title     = {Investigating Shape Variation Using Generalized Procrustes Analysis and Machine Learning},
  series = {Applied Sciences},
  volume    = {2022},
  journal   = {Applied Sciences},
  number    = {12(6), 3158},
  pages     = {26},
  abstract  = {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},
  subject      = {generalized procrustes analysis},
  language  = {en}
}
@article{WoeberCurtoTibihikaetal.,
  author    = {W{\"o}ber, Wilfried and Curto, Manuel and Tibihika, Papius D. and Meulenboek, Paul and Alemayehu, Esayas and Mehnen, Lars and Meimberg, Harald and Sykacek, Peter},
  title     = {Identifying geographically differentiated features of Ethopian Nile tilapia (Oreochromis niloticus) morphology with machine learning},
  series = {PlosONE},
  volume    = {16},
  journal   = {PlosONE},
  number    = {4},
  subject      = {Machine Learning},
  language  = {en}
}
@inproceedings{WoeberTibihikaOlaverriMonrealetal.,
  author    = {W{\"o}ber, Wilfried and Tibihika, Papius D and Olaverri-Monreal, Cristina and Mehnen, Lars and Sykacek, Peter and Meimberg, Harald},
  title     = {Comparison of Unsupervised Learning Methods for Natural Image Processing},
  series = {Biodiversity Information Science and Standards},
  booktitle = {Biodiversity Information Science and Standards},
  number    = {3},
  subject      = {Machine Learning},
  language  = {en}
}
@inproceedings{WoeberSzuegyiKubingeretal.,
  author    = {W{\"o}ber, Wilfried and Szuegyi, Daniel and Kubinger, Wilfried and Mehnen, Lars},
  title     = {A principal component analysis based object detection for thermal infra-red images},
  series = {Proceedings of the 55th International Symposium ELMAR},
  booktitle = {Proceedings of the 55th International Symposium ELMAR},
  subject      = {Principal Component Analysis},
  language  = {en}
}
@misc{SpulakOtrebskiKubinger,
  author    = {Spulak, David and Otrebski, Richard and Kubinger, Wilfried},
  title     = {Object Tracking by Combining Supervised and Adaptive Online Learning through Sensor Fusion of Multiple Stereo Camera Systems},
  subject      = {Object Tracking},
  language  = {en}
}
@inproceedings{WoeberAburaiaOlaverriMonreal,
  author    = {W{\"o}ber, Wilfried and Aburaia, Mohamed and Olaverri-Monreal, Cristina},
  title     = {Classification of Streetsigns Using Gaussian Process Latent Variable Models},
  series = {2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE), Graz, Austria},
  booktitle = {2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE), Graz, Austria},
  subject      = {Streetsigns},
  language  = {en}
}