@incollection{JuMaurerBreussetal., author = {Ju, Yong Chul and Maurer, Daniel and Breuß, Michael and Bruhn, Andr{\´e}s}, title = {Direct variational perspective shape from shading with Cartesian depth parametrisation}, series = {Perspectives in Shape Analysis, Mathematics and Visualization}, booktitle = {Perspectives in Shape Analysis, Mathematics and Visualization}, editor = {Breuß, Michael and Bruckstein, Alfred and Maragos, Petros and Wuhrer, Stefanie}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-24724-3}, doi = {10.1007/978-3-319-24726-7}, pages = {43 -- 72}, language = {en} } @misc{KraljevskiDuckhornJuetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Ju, Yong Chul and Tsch{\"o}pe, Constanze and Richter, Christian and Wolff, Matthias}, title = {Acoustic Resonance Recognition of Coins}, series = {2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 25-28 May 2020, Dubrovnik, Croatia}, journal = {2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 25-28 May 2020, Dubrovnik, Croatia}, publisher = {IEEE Xplore}, isbn = {978-1-7281-4460-3}, doi = {10.1109/I2MTC43012.2020.9129256}, pages = {6}, abstract = {In this study, we compare different machine learning approaches applied to acoustic resonance recognition of coins. Euro-cents and Euro-coins were classified by the sound emerging when throwing the coins onto a hard surface.The used dataset is a representative example of a small data which was collected in carefully prepared experiments.Due to the small number of coin specimens and the count of the collected observations, it was interesting to see whether deep learning methods can achieve similarly or maybe even better classification performances compared with more traditional methods.The results of the multi-class prediction of coin denominations are presented and compared in terms of balanced accuracy and Matthews Correlation Coefficient metrics. The feature analysis methods combined with the employed classifiers achieved acceptable results, despite the relatively small dataset.}, language = {en} } @misc{KraljevskiDuckhornJuetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Ju, Yong Chul and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Acoustic Resonance Testing of Glass IV Bottles}, series = {Artificial Intelligence Applications and Innovations : 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5-7, 2020, Proceedings, Part II}, volume = {Cham}, journal = {Artificial Intelligence Applications and Innovations : 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5-7, 2020, Proceedings, Part II}, editor = {Maglogiannis, Ilias and Iliadis, Lazaros S. and Pimenidis, Elias}, publisher = {Springer International Publishing}, isbn = {978-3-030-49186-4}, issn = {1868-4238}, doi = {10.1007/978-3-030-49186-4_17}, pages = {195 -- 206}, abstract = {In this paper, acoustic resonance testing on glass intravenous (IV) bottles is presented. Different machine learning methods were applied to distinguish acoustic observations of bottles with defects from the intact ones. Due to the very limited amount of available specimens, the question arises whether the deep learning methods can achieve similar or even better detection performance compared with traditional methods.}, language = {en} } @inproceedings{KleefeldJuTozzaetal., author = {Kleefeld, Andreas and Ju, Yong Chul and Tozza, Silvia and Breuß, Michael and Bruhn, Andr{\´e}s}, title = {Generalised Perspective Shape from Shading with Oren-Nayar Reflectance}, series = {Proceedings 24th British Machine Vision Conference, BMVC 2013, Bristol, UK, September 2013}, booktitle = {Proceedings 24th British Machine Vision Conference, BMVC 2013, Bristol, UK, September 2013}, editor = {Burghardt, Tilo and Damen, Dima and Mayol-Cuevas, Walterio and Mirmehdi, Majid}, publisher = {BMVA Press}, doi = {10.5244/C.27.42}, pages = {42.1 -- 42.11}, abstract = {In spite of significant advances in Shape from Shading (SfS) over the last years, it is still a challenging task to design SfS approaches that are flexible enough to handle a wide range of input scenes. In this paper, we address this lack of flexibility by proposing a novel model that extends the range of possible applications. To this end, we consider the class of modern perspective SfS models formulated via partial differential equations (PDEs). By combining a recent spherical surface parametrisation with the advanced non-Lambertian Oren-Nayar reflectance model, we obtain a robust approach that allows to deal with an arbitrary position of the light source while being able to handle rough surfaces and thus more realistic objects at the same time. To our knowledge, the resulting model is currently the most advanced and most flexible approach in the literature on PDE-based perspective SfS. Apart from deriving our model, we also show how the corresponding set of sophisticated Hamilton-Jacobi equations can be efficiently solved by a specifically tailored fast marching scheme. Experiments with medical real-world data demonstrate that our model works in practice and that is offers the desired flexibility.}, language = {en} } @inproceedings{ChulJuBruhnBreuss, author = {Chul Ju, Yong and Bruhn, Andr{\´e}s and Breuß, Michael}, title = {Variational Perspective Shape from Shading}, series = {Scale Space and Variational Methods in Computer Vision, 5th International Conference, SSVM 2015, L{\`e}ge-Cap Ferret, France, May 31 - June 4, 2015, Proceedings}, booktitle = {Scale Space and Variational Methods in Computer Vision, 5th International Conference, SSVM 2015, L{\`e}ge-Cap Ferret, France, May 31 - June 4, 2015, Proceedings}, editor = {Aujol, Jean-Fran{\c{c}}ois and Nikolova, Mila and Papadakis, Nicolas}, publisher = {Springer International Publishing}, address = {Berlin}, isbn = {978-3-319-18460-9}, doi = {10.1007/978-3-319-18461-6_43}, pages = {538 -- 550}, language = {en} } @inproceedings{GallianiJuBreussetal., author = {Galliani, S. and Ju, Yong Chul and Breuß, Michael and Bruhn, Andr{\´e}s}, title = {Generalised Perspective Shape from Shading in Spherical Coordinates}, series = {Proceedings Fourth International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2013, Graz, Austria, June 2013}, booktitle = {Proceedings Fourth International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2013, Graz, Austria, June 2013}, editor = {Kuijper, A. and Pock, T. and Bredies, K. and Bischof, H.}, publisher = {Springer}, address = {Berlin}, isbn = {978-3-642-38266-6}, pages = {222 -- 233}, language = {en} } @inproceedings{JuBreussBruhnetal., author = {Ju, Yong Chul and Breuß, Michael and Bruhn, Andr{\´e}s and Galiani, Silvano}, title = {Shape from Shading for Rough Surfaces: Analysis of the Oren-Nayar Model}, series = {Proceedings 23rd British Machine Vision Conference, BMVC 2012, Surrey, UK, September 3-7, 2012}, booktitle = {Proceedings 23rd British Machine Vision Conference, BMVC 2012, Surrey, UK, September 3-7, 2012}, editor = {Browden, R. and Collomosse, J. and Mikolajczyk, K.}, publisher = {BMVA Press}, isbn = {1-901725-46-4}, doi = {10.5244/C.26.104}, pages = {104.1 -- 104.11}, language = {en} } @inproceedings{GallianiBreussJu, author = {Galliani, S. and Breuß, Michael and Ju, Yong Chul}, title = {Fast and Robust Surface Normal Integration by a Discrete Eikonal Equation}, series = {Proceedings, 23rd British Machine Vision Conference, BMVC 2012, Surrey, UK, September 3-7, 2012}, booktitle = {Proceedings, 23rd British Machine Vision Conference, BMVC 2012, Surrey, UK, September 3-7, 2012}, editor = {Bowden, R. and Collomosse, J. and Mikolajczyk, K.}, publisher = {BMVA Press}, isbn = {1-901725-46-4}, doi = {10.5244/C.26.106}, pages = {106.1 -- 106.11}, language = {en} } @misc{JuKraljevskiNeunuebeletal., author = {Ju, Yong Chul and Kraljevski, Ivan and Neun{\"u}bel, Heiko and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Acoustic Resonance Testing of Small Data on Sintered Cogwheels}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {15}, issn = {1424-8220}, doi = {10.3390/s22155814}, abstract = {Based on the fact that cogwheels are indispensable parts in manufacturing, we present the acoustic resonance testing (ART) of small data on sintered cogwheels for quality control in the context of non-destructive testing (NDT). Considering the lack of extensive studies on cogwheel data by means of ART in combination with machine learning (ML), we utilize time-frequency domain feature analysis and apply ML algorithms to the obtained feature sets in order to detect damaged samples in two ways: one-class and binary classification. In each case, despite small data, our approach delivers robust performance: All damaged test samples reflecting real-world scenarios are recognized in two one-class classifiers (also called detectors), and one intact test sample is misclassified in binary ones. This shows the usefulness of ML and time-frequency domain feature analysis in ART on a sintered cogwheel dataset.}, language = {en} } @misc{KraljevskiJuIvanovetal., author = {Kraljevski, Ivan and Ju, Yong Chul and Ivanov, Dmitrij and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {How to Do Machine Learning with Small Data? - A Review from an Industrial Perspective}, series = {arXiv}, journal = {arXiv}, abstract = {Artificial intelligence experienced a technological breakthrough in science, industry, and everyday life in the recent few decades. The advancements can be credited to the ever-increasing availability and miniaturization of computational resources that resulted in exponential data growth. However, because of the insufficient amount of data in some cases, employing machine learning in solving complex tasks is not straightforward or even possible. As a result, machine learning with small data experiences rising importance in data science and application in several fields. The authors focus on interpreting the general term of "small data" and their engineering and industrial application role. They give a brief overview of the most important industrial applications of machine learning and small data. Small data is defined in terms of various characteristics compared to big data, and a machine learning formalism was introduced. Five critical challenges of machine learning with small data in industrial applications are presented: unlabeled data, imbalanced data, missing data, insufficient data, and rare events. Based on those definitions, an overview of the considerations in domain representation and data acquisition is given along with a taxonomy of machine learning approaches in the context of small data.}, language = {en} }