@article{KubingerPeschakWoeberetal.,
  author    = {Kubinger, Wilfried and Peschak, Bernhard and W{\"o}ber, Wilfried and Sulz, Clemens},
  title     = {Bildgebende Sensorsystems f{\"u}r robotische Systeme in der Agrar- und Landtechnik},
  series = {e\&i Elektrotechnik und Informationstechnik},
  volume    = {134},
  journal   = {e\&i Elektrotechnik und Informationstechnik},
  number    = {6},
  pages     = {316 -- 322},
  subject      = {Sensor},
  language  = {de}
}
@inproceedings{WoeberKeferKubingeretal.,
  author    = {W{\"o}ber, Wilfried and Kefer, Martin and Kubinger, Wilfried and Szuegyi, Daniel},
  title     = {Evaluation of Daylight and Thermal Infra-Red based Detection for Platooning Vehicles},
  series = {Annals of DAAM for 2012 and Proceedings of the 23rd International DAAM Symposium},
  booktitle = {Annals of DAAM for 2012 and Proceedings of the 23rd International DAAM Symposium},
  pages     = {719 -- 722},
  subject      = {Vehicle},
  language  = {en}
}
@incollection{WoeberPeschakOtrebski,
  author    = {W{\"o}ber, Wilfried and Peschak, Bernhard and Otrebski, Richard},
  title     = {ASK: Entwicklung eines modularen Systems zur Automatisierung landwirtschaftlicher Maschinen},
  series = {Intelligente Systeme - Stand der Technik und neue M{\"o}glichkeiten. Lecture Notes in Informatics},
  booktitle = {Intelligente Systeme - Stand der Technik und neue M{\"o}glichkeiten. Lecture Notes in Informatics},
  address   = {Bonn},
  publisher      = {Fachhochschule Technikum Wien},
  pages     = {221 -- 224},
  subject      = {Automation},
  language  = {de}
}
@inproceedings{SteiglAburaiaWoeber,
  author    = {Steigl, D and Aburaia, Mohamed and W{\"o}ber, Wilfried},
  title     = {Autonomous Grasping of Known Objects Using Depth Data and the PCA},
  series = {Austrian Robotics Workshop 2020},
  booktitle = {Austrian Robotics Workshop 2020},
  subject      = {Robotic},
  language  = {en}
}
@article{WoeberRauerPapaetal.,
  author    = {W{\"o}ber, Wilfried and Rauer, Johannes and Papa, Maximilian and Aburaia, Ali and Schwaiger, Simon and Novotny, Georg and Aburaia, Mohamed and Kubinger, Wilfried},
  title     = {Evaluierung von Navigationsmethoden f{\"u}r mobile Roboter},
  series = {e \& i Elektrotechnik und Informationstechnik},
  journal   = {e \& i Elektrotechnik und Informationstechnik},
  subject      = {Robotics},
  language  = {de}
}
@inproceedings{SchwaigerAburaiaAburaiaetal.,
  author    = {Schwaiger, Simon and Aburaia, Mohamed and Aburaia, Ali and W{\"o}ber, Wilfried},
  title     = {Explainable Artificial Intelligence for Robot Arm Control},
  series = {Proceedings of the 32nd International DAAAM Virtual Symposium `Intelligent Manufacturing \& Automation`, 28-29th October 2021, Vienna},
  volume    = {32},
  booktitle = {Proceedings of the 32nd International DAAAM Virtual Symposium `Intelligent Manufacturing \& Automation`, 28-29th October 2021, Vienna},
  number    = {1},
  pages     = {0640 -- 0647},
  subject      = {Artificial Intelligence},
  language  = {en}
}
@inproceedings{PeschakWoeberOtrebskietal.,
  author    = {Peschak, Bernhard and W{\"o}ber, Wilfried and Otrebski, Richard and Sulz, Clemens and Thalhammer, J.},
  title     = {Sensorfusion f{\"u}r landwirtschaftliche Applikationen},
  series = {Referate der 37. GIL-Jahrestagung: Digitale Transofmration - Wege in eine zukunftsf{\"a}hige Landwirtschaft},
  booktitle = {Referate der 37. GIL-Jahrestagung: Digitale Transofmration - Wege in eine zukunftsf{\"a}hige Landwirtschaft},
  subject      = {Sensor},
  language  = {de}
}
@incollection{KrieglerWoeberAburaia,
  author    = {Kriegler, Andreas and W{\"o}ber, Wilfried and Aburaia, Mohamed},
  title     = {Artificial Neural Networks Based Place Categorization},
  series = {Digital Conversion on the Way to Industry 4.0},
  booktitle = {Digital Conversion on the Way to Industry 4.0},
  publisher = {Springer Verlag},
  publisher      = {Fachhochschule Technikum Wien},
  pages     = {201 -- 209},
  subject      = {Artificial Intelligence},
  language  = {en}
}
@incollection{FelberAburaiaWoeberetal.,
  author    = {Felber, Stefan Otto and Aburaia, Mohamed and W{\"o}ber, Wilfried and Lackner, Maximilian},
  title     = {Parameter Optimization for the 3D Print of Thermo-Plastic Pellets with an Industrial Robot},
  series = {Digital Conversion on the Way to Industry 4.0},
  booktitle = {Digital Conversion on the Way to Industry 4.0},
  publisher      = {Fachhochschule Technikum Wien},
  pages     = {236 -- 247},
  subject      = {Thermo Plastics},
  language  = {en}
}
@article{RauerAburaiaWoeber,
  author    = {Rauer, Johannes and Aburaia, Mohamed and W{\"o}ber, Wilfried},
  title     = {Semi-Automatic Generation of Training Data for Neural Networks for 6D Pose Estimation and Robotic Graspin},
  series = {Proceedings of Joint Austrian Computer Vision and Robotics Workshop 2020},
  journal   = {Proceedings of Joint Austrian Computer Vision and Robotics Workshop 2020},
  pages     = {2 -- 3},
  subject      = {Robotics},
  language  = {en}
}
@inproceedings{AbdankAburaiaWoeber,
  author    = {Abdank, Moritz and Aburaia, Mohamed and W{\"o}ber, Wilfried},
  title     = {Using-Colour-Based Object Detection for Pick and Place Applications},
  series = {Proceedings of the 32nd International DAAAM Virtual Symposium 'Intelligent Manufacturing \& Automation', 28-29th October 2021, Vienna},
  volume    = {32},
  booktitle = {Proceedings of the 32nd International DAAAM Virtual Symposium 'Intelligent Manufacturing \& Automation', 28-29th October 2021, Vienna},
  number    = {1},
  pages     = {0536 -- 0541},
  subject      = {Computer Vision},
  language  = {en}
}
@inproceedings{WoeberAburaiaAburaiaetal.,
  author    = {W{\"o}ber, Wilfried and Aburaia, Ali and Aburaia, Mohamed and Kubinger, Wilfried and Otrebski, Richard and Engelhardt-Nowitzki, Corinna and Markl, Erich},
  title     = {Konferenz der Mechatronik-Plattform: Autonome mechatronische Systeme},
  series = {FH CAMPUS 02, 22. November 2018 Digital Manufacturing \& Robotics im Department Industrial Engineering},
  booktitle = {FH CAMPUS 02, 22. November 2018 Digital Manufacturing \& Robotics im Department Industrial Engineering},
  subject      = {Autonom},
  language  = {en}
}
@inproceedings{RauerAburaiaWoeber,
  author    = {Rauer, Johannes and Aburaia, Mohamed and W{\"o}ber, Wilfried},
  title     = {Semi-Automatic Generation of Training Data for Neural Networks for 6D Pose Estimation and Robotic Grasping},
  series = {Austrian Robotics Workshop 2020},
  booktitle = {Austrian Robotics Workshop 2020},
  subject      = {Semi-Automatic},
  language  = {en}
}
@inproceedings{KeferWoeberSzuegyietal.,
  author    = {Kefer, Martin and W{\"o}ber, Wilfried and Szuegyi, Daniel and Kubinger, Wilfried},
  title     = {A Novel and Automated Circle Pattern Recognition Technique for Infra-Red Stero Camera Calibration},
  series = {Proceedings of the 10th IASTED International Conference on Signal Processing, Pattern Recognition and Applications},
  booktitle = {Proceedings of the 10th IASTED International Conference on Signal Processing, Pattern Recognition and Applications},
  pages     = {404 -- 410},
  subject      = {Pattern Recognition},
  language  = {en}
}
@inproceedings{KrieglerWoeber,
  author    = {Kriegler, Andreas and W{\"o}ber, Wilfried},
  title     = {Vision-based Docking of a Mobile Robot},
  series = {Proceedings of the Joint Austrian Computer Vision and Robotics Workshop 2020},
  booktitle = {Proceedings of the Joint Austrian Computer Vision and Robotics Workshop 2020},
  pages     = {6 -- 12},
  subject      = {Automation},
  language  = {en}
}
@inproceedings{WoeberSupperAschaueretal.,
  author    = {W{\"o}ber, Wilfried and Supper, Georg and Aschauer, Christian and Gronauer, Andreas and Tomic, Dana Kathrin and H{\"o}rmann, Sandra},
  title     = {Entwicklung eines auf semantischer Technologie basierenden Analysesystems zur {\"U}berwachung der Wasserversorgung von landwirtschaftlichen Nutzfl{\"a}chen},
  series = {Referate der 35. GIL-Jahrestagung in Geisenheim - Komplexit{\"a}t versus Bedienbarkeit/Mensch-Maschine-Schnittstellen},
  booktitle = {Referate der 35. GIL-Jahrestagung in Geisenheim - Komplexit{\"a}t versus Bedienbarkeit/Mensch-Maschine-Schnittstellen},
  abstract  = {Eine ressourcenschonende Bew{\"a}sserung von Nutzpflanzen wird durch den Klimawandel in den n{\"a}chsten Jahren immer gr{\"o}ßere Bedeutung gewinnen. Eine M{\"o}glichkeit, das f{\"u}r Pflanzen zur Verf{\"u}gung stehende Wasserpotential beurteilen zu k{\"o}nnen, ist die Nutzung von klimatischen Faktoren und Computermodellen. In diesem Beitrag wird die Entwicklung eines auf dem Forschungsprojekt agriOpenLink basierten Softwaresystems zur Absch{\"a}tzung der Evapotranspiration auf einer landwirtschaftlichen Nutzfl{\"a}che beschrieben. Der Fokus dieses Berichts liegt auf dem Softwareframework, welches auf semantischer Technologie basiert und formalisiertes landwirtschaftliches Wissen sowie Computermodelle zur Absch{\"a}tzung der Evapotranspiration beinhaltet. Es werden erste experimentelle Ergebnisse diskutiert und die semantische Technologie bez{\"u}glich praktischer Nutzung evaluiert.},
  subject      = {Semantic Modelling},
  language  = {de}
}
@article{TomicDrenjanacLazendicetal.,
  author    = {Tomic, Dana Kathrin and Drenjanac, Domagoj and Lazendic, Goran and H{\"o}rmann, Sandra and Handler, Franz and W{\"o}ber, Wilfried and Aschauer, Christian and Auer, Wolfgang},
  title     = {Semantische Technologien f{\"u}r Produktionsprozessinnovationen in der Landwirtschaft},
  series = {e\&i Elektrotechnik und Informationstechnik},
  volume    = {131},
  journal   = {e\&i Elektrotechnik und Informationstechnik},
  number    = {7},
  pages     = {223 -- 229},
  subject      = {Semantics},
  language  = {de}
}
@inproceedings{WoeberAburaiaAburaiaetal.,
  author    = {W{\"o}ber, Wilfried and Aburaia, Ali and Aburaia, Mohamed and Kubinger, Wilfried and Otrebski, Richard and Engelhardt-Nowitzki, Corinna and Markl, Erich},
  title     = {Digital Manufacturing \& Robotics im Department Industrial Engineering},
  series = {KONFERENZ DER MECHATRONIK PLATTFORM: Autonome mechatronische Systeme},
  booktitle = {KONFERENZ DER MECHATRONIK PLATTFORM: Autonome mechatronische Systeme},
  subject      = {Digital Manufacturing},
  language  = {de}
}
@inproceedings{WoeberNovotnyAburaiaetal.,
  author    = {W{\"o}ber, Wilfried and Novotny, Georg and Aburaia, Mohamed and Otrebski, Richard and Kubinger, Wilfried},
  title     = {Estimating a Sparse Representation of Gaussian Processes Using Global Optimization and the Bayesian Information Criterion},
  series = {Austrian Robotics Workshop 2018},
  booktitle = {Austrian Robotics Workshop 2018},
  subject      = {Mobile Robotics},
  language  = {en}
}
@inproceedings{WoeberSchulmeisterAschaueretal.,
  author    = {W{\"o}ber, Wilfried and Schulmeister, Klemens and Aschauer, Christian and Gronauer, Andreas and Tomic, Dana Kathrin and Fensel, Anna and Riegler, Thomas and Handler, Franz and H{\"o}rmann, Sandra and Otte, Marcel and Auer, Wolfgang},
  title     = {Adaptive Agricultural Processes via Open Interfaces and Linked Services},
  series = {Referate der 34. GIL-Jahrestagung - IT-Standards in der Agrar- und Ern{\"a}hrungswissenschaft},
  booktitle = {Referate der 34. GIL-Jahrestagung - IT-Standards in der Agrar- und Ern{\"a}hrungswissenschaft},
  pages     = {157 -- 160},
  subject      = {Adaption},
  language  = {en}
}
@inproceedings{RauerWoeberAburaia,
  author    = {Rauer, Johannes and W{\"o}ber, Wilfried and Aburaia, Mohamed},
  title     = {An Autonomous Mobile Handling Robot Using Object Recognition},
  series = {Proceedings of ARW \& OAGM Workshop 2019},
  booktitle = {Proceedings of ARW \& OAGM Workshop 2019},
  subject      = {Mobile Robotics},
  language  = {en}
}
@inproceedings{WoeberAburaiaAburaiaetal.,
  author    = {W{\"o}ber, Wilfried and Aburaia, Ali and Aburaia, Mohamed and Kubinger, Wilfried and Otrebski, Richard and Engelhardt-Nowitzki, Corinna and Markl, Erich},
  title     = {Digital Manufacturing \& Robotics im Department Industrial Engineering},
  series = {Konferenz der Mechatronik Plattform Autonome mechatronische Systeme},
  booktitle = {Konferenz der Mechatronik Plattform Autonome mechatronische Systeme},
  pages     = {20 -- 21},
  abstract  = {Digitalisierung und Automatisierung in produzierender Industrie sind Schlagw{\"o}rter heutiger Forschungsbarbeiten am Department Industrial Engineering der Fachhochschule Technikum Wien. K{\"u}nstliche Intelligenz, vernetzte Sensorsysteme oder {\"u}bergreifende Prozessplanung erm{\"o}glichen Optimierung hinsichtlich Produktionszeit oder Fertigung von individualisierten Produkten, was zur Losgr{\"o}ße 1 f{\"u}hrt. In diesem Dokument werden Forschungst{\"a}tigkeiten des Departments Industrial Engineering im Kontext Vernetzung, Servicerobotik sowie Industrierobotik zusammengefasst.},
  subject      = {Industrial Engineering},
  language  = {de}
}
@misc{WoeberAburaiaKubingeretal.,
  author    = {W{\"o}ber, Wilfried and Aburaia, Ali and Kubinger, Wilfried and Otrebski, Richard and Engelhardt-Nowitzki, Corinna and Markl, Erich and Aburaia, Mohamed},
  title     = {Digital Manufacturing \& Robotics im Department Industrial Engineering},
  subject      = {Industrial Engineering},
  language  = {de}
}
@inproceedings{TomicDrenjanacLazendicetal.,
  author    = {Tomic, Dana Kathrin and Drenjanac, Domagoj and Lazendic, Goran and H{\"o}rmann, Sandra and Handler, Franz and W{\"o}ber, Wilfried and Schulmeister, Klemens and Otte, Marcel and Auer, Wolfgang},
  title     = {Semantic Services for Adaptive Processes in Livestock Farming},
  series = {International Conference of Agricultural Engineering (AgEng 2014)},
  booktitle = {International Conference of Agricultural Engineering (AgEng 2014)},
  pages     = {8},
  subject      = {Agriculture},
  language  = {en}
}
@article{TomicDrenjanacLazendicetal.,
  author    = {Tomic, Dana Kathrin and Drenjanac, Domagoj and Lazendic, Goran and H{\"o}rmann, Sandra and Handler, Franz and W{\"o}ber, Wilfried and Aschauer, Christian and Auer, Wolfgang},
  title     = {Ontologies and semantic services for process optimization in agricultural production},
  series = {e\&i Elektrotechnik und Informationstechnik},
  journal   = {e\&i Elektrotechnik und Informationstechnik},
  subject      = {Semantics},
  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}
}
@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}
}
@article{WoeberNovotnyMehnenetal.,
  author    = {W{\"o}ber, Wilfried and Novotny, Georg and Mehnen, Lars and Olaverri-Monreal, Cristina},
  title     = {Autonomous Vehicles: Vehicle Parameter Estimation Using Variational Bayes and Kinematics},
  series = {Applied Sciences},
  volume    = {10},
  journal   = {Applied Sciences},
  number    = {18},
  subject      = {Variational bayes},
  language  = {en}
}
@article{WoeberMehnenSykaceketal.,
  author    = {W{\"o}ber, Wilfried and Mehnen, Lars and Sykacek, Peter and Meimberg, Harald},
  title     = {Investigating Explanatory Factors of Machine Learning Models for Plant Classification},
  series = {Plants},
  volume    = {2021},
  journal   = {Plants},
  number    = {10(12):2674},
  pages     = {20},
  abstract  = {Recent progress in machine learning and deep learning has enabled the implementation of plant and crop detection using systematic inspection of the leaf shapes and other morphological characters for identification systems for precision farming. However, the models used for this approach tend to become black-box models, in the sense that it is difficult to trace characters that are the base for the classification. The interpretability is therefore limited and the explanatory factors may not be based on reasonable visible characters. We investigate the explanatory factors of recent machine learning and deep learning models for plant classification tasks. Based on a Daucus carota and a Beta vulgaris image data set, we implement plant classification models and compare those models by their predictive performance as well as explainability. For comparison we implemented a feed forward convolutional neuronal network as a default model. To evaluate the performance, we trained an unsupervised Bayesian Gaussian process latent variable model as well as a convolutional autoencoder for feature extraction and rely on a support vector machine for classification. The explanatory factors of all models were extracted and analyzed. The experiments show, that feed forward convolutional neuronal networks (98.24\% and 96.10\% mean accuracy) outperforms the Bayesian Gaussian process latent variable pipeline (92.08\% and 94.31\% mean accuracy) as well as the convolutional autoenceoder pipeline (92.38\% and 93.28\% mean accuracy) based approaches in terms of classification accuracy, even though not significant for Beta vulgaris images. Additionally, we found that the neuronal network used biological uninterpretable image regions for the plant classification task. In contrast to that, the unsupervised learning models rely on explainable visual characters. We conclude that supervised convolutional neuronal networks must be used carefully to ensure biological interpretability. We recommend unsupervised machine learning, careful feature investigation, and statistical feature analysis for biological applications. View Full-Text Keywords: deep learning; machine learning; plant leaf morphometrics; explainable AI},
  subject      = {deep learning},
  language  = {en}
}