Biblio

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1
Berlin, C., Adomeit S., Grover P., Dreischarf M., Dürr O., & Obid P. (2022).  140. Automated measurement technique for coronal parameters using a novel artificial intelligence algorithm: an independent validation study on 100 preoperative AP spine X-rays. The Spine Journal. 22, S74.
A
Ginkel, I., & Umlauf G. (2007).  Analyzing a generalized Loop subdivision scheme. Computing. 79, 353-363.PDF icon AnalyzeSubScheme.pdf (190.38 KB)
B
Grunwald, M., Hermann M., Freiberg F., & Franz M. O. (2021).  Biologically-inspired vs. CNN texture representations in novelty detection. Applications of Machine Learning 2021. 118430I.PDF icon Spie2021.pdf (5.33 MB)
C
Griesser, D., Dold D., Umlauf G., & Franz M. O. (2020).  CNN-Based Monocular 3D Ship Detection Using Inverse Perspective. Global Oceans.
Ginkel, I., & Umlauf G. (2006).  Controlling a subdivision tuning method. (Cohen, A., Merrien J.-L., & Schumaker L.L., Ed.).Curve and Surface Fitting. PDF icon SubTuning.pdf (553.79 KB)
D
Herzog, L., Kook L., Götschi A., Petermann K., Hänsel M., Hamann J., et al. (2023).  Deep transformation models for functional outcome prediction after acute ischemic stroke. Biometrical Journal. 65, 2100379.
E
Hoher, P., Reuter J., Dold D., Griesser D., Govaers F., & Koch W. (2023).  Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Gaussian Processes Regression Model. International Conference on Information Fusion (FUSION). 1-8.
Hoher, P., Reuter J., Dold D., Griesser D., Govaers F., & Koch W. (2023).  Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Gaussian Processes Regression Model. International Conference on Information Fusion (FUSION). 1-8.
F
Hermann, M., Umlauf G., Goldlücke B., & Franz M. O. (2022).  Fast and efficient image novelty detection based on mean-shifts. Sensors | Unusual Behavior Detection Based on Machine Learning .
I
Hermann, M., Goldlücke B., & Franz M. O. (2022).  Image novelty detection based on mean-shift and typical set size. 21th International Conference on Image Analysis and Processing, ICIAP. PDF icon ICIAP-mean-shift-novelty-detection-preprint.pdf (2.96 MB)
Hermann, M., Umlauf G., Goldlücke B., & Franz M. O. (2023).  Incremental one-class learning using regularized null-space training for industrial defect detection. 16th International Conference on Machine Vision (ICMV).
L
Ginkel, I., & Umlauf G. (2008).  Local energy-optimizing subdivision algorithms. Computer Aided Geometric Design. 25, 137-147.PDF icon OptSub.pdf (706.26 KB)
Ginkel, I., & Umlauf G. (2006).  Loop subdivision with curvature control. (Polthier, K., & Sheffer A., Ed.).Eurographics Symposium on Geometry Processing. PDF icon LoopSubCurv.pdf (6.03 MB)
N
Bobach, T., & Umlauf G. (2007).  Natural neighbor concepts in scattered data interpolation and discrete function approximation. (Hagen, H., Hering-Bertram M., & Garth C., Ed.).GI Lecture Notes in Informatics, Visualization of Large and Unstructured Data Sets. PDF icon NatNeighborConcepts.pdf (1.21 MB)
Ginkel, I., Peters J., & Umlauf G. (2005).  On normals and control nets. (Martin, R., Bez H., & M. 233-239 S. pages =, Ed.).Mathematics of Surfaces XI. PDF icon NormalsControlNets.pdf (117.42 KB)
Ginkel, I., Peters J., & Umlauf G. (2007).  Normals of subdivision surfaces and their control polyhedra. Computer Aided Geometric Design. 24, 112-116.PDF icon SubSurfContrPoly.pdf (272.28 KB)
Berlin, C., Adomeit S., Grover P., Dreischarf M., Halm H., Dürr O., et al. (2023).  Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays. Global Spine Journal. 21925682231154543.
O
Denker, K., Hamann B., & Umlauf G. (2015).  On-line CAD Reconstruction with Accumulated Means of Local Geometric Properties. (Boissonnat, J-D., Cohen A., Gibaru O., Gout C., Lyche T., Mazure M-L., et al., Ed.).Curves and Surfaces, 8th International Conference, Paris 2014. 181-201.PDF icon OnlineCADReconst.pdf (3.18 MB)
Denker, K., Hamann B., & Umlauf G. (2015).  On-line CAD Reconstruction with Accumulated Means of Local Geometric Properties. (Boissonnat, J-D., Cohen A., Gibaru O., Gout C., Lyche T., Mazure M-L., et al., Ed.).Curves and Surfaces, 8th International Conference, Paris 2014. 181-201.PDF icon OnlineCADReconst.pdf (3.18 MB)
Denker, K., Lehner B., & Umlauf G. (2008).  Online triangulation of laser-scan data. (Garimella, R., Ed.).Proceedings of the 17th International Meshing Roundtable 2008. PDF icon OnlineTriang.pdf (8.39 MB)
Grunwald, M., Hermann M., Freiberg F., & Franz M. O. (2018).  Optical Surface Detection: A novelty detection approach based on CNN-encoded features. SPIE Optics and Photonics. 10752 - 10752 - 13.PDF icon Spie2018.pdf (730 KB)
P
Grunwald, M., Müller J., Schall M., Laube P., Umlauf G., & Franz M. O. (2015).  Pixel-wise Hybrid Image Registration on Wood Decors. BW-CAR| SINCOM. 24.PDF icon Grunwald_2015_Pixel-wiseHybridImageRegistration.pdf (2.42 MB)
R
Schall, M., Grunwald M., Umlauf G., & Franz M. O. (2015).  Radiometric calibration of digital cameras using Gaussian processes. SPIE Optics+ Optoelectronics. PDF icon Schall et al_2015_Radiometric calibration of digital cameras using Gaussian processes.PDF (953.2 KB)
Grunwald, M., Laube P., Schall M., Umlauf G., & Franz M. O. (2017).  Radiometric calibration of digital cameras using neural networks. Optics and Photonics for Information Processing XI.
Grunwald, M., Gansloser J., & Franz M. O. (2016).  Radiometric calibration of digital cameras using sparse Gaussian processes. Workshop Farbbildverarbeitung.

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