Biblio
Export 36 results:
Author Title Type [ Year] Filters: First Letter Of Last Name is G [Clear All Filters]
Deep transformation models for functional outcome prediction after acute ischemic stroke.
Biometrical Journal. 65, 2100379.
(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.
(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.
(2023). Incremental one-class learning using regularized null-space training for industrial defect detection.
16th International Conference on Machine Vision (ICMV).
(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.
(2023). Visual Pitch and Roll Estimation For Inland Water Vessels.
IEEE International Conference on Robotics and Automation (ICRA). 1961-1967.
(2023). 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.
(2022). Fast and efficient image novelty detection based on mean-shifts.
Sensors | Unusual Behavior Detection Based on Machine Learning .
(2022). Image novelty detection based on mean-shift and typical set size.
21th International Conference on Image Analysis and Processing, ICIAP. ICIAP-mean-shift-novelty-detection-preprint.pdf (2.96 MB)
(2022). Targetless Lidar-camera registration using patch-wise mutual information.
International Conference on Information Fusion. mir_reg_patch.pdf (9.58 MB)
(2022). Targetless Lidar-camera registration using patch-wise mutual information.
International Conference on Information Fusion. mir_reg_patch.pdf (9.58 MB)
(2022).
(2022). Biologically-inspired vs. CNN texture representations in novelty detection.
Applications of Machine Learning 2021. 118430I. Spie2021.pdf (5.33 MB)
(2021). CNN-Based Monocular 3D Ship Detection Using Inverse Perspective.
Global Oceans.
(2020). Optical Surface Detection: A novelty detection approach based on CNN-encoded features.
SPIE Optics and Photonics. 10752 - 10752 - 13. Spie2018.pdf (730 KB)
(2018). Radiometric calibration of digital cameras using neural networks.
Optics and Photonics for Information Processing XI.
(2017). Radiometric calibration of digital cameras using sparse Gaussian processes.
Workshop Farbbildverarbeitung.
(2016). Radiometric calibration of digital cameras using sparse Gaussian processes.
Workshop Farbbildverarbeitung.
(2016). Wahrnehmungsorientierte optische Inspektion von texturierten Oberflächen.
INFORMATIK 2016, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik. 259, 1963–1968.
(2016). 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. OnlineCADReconst.pdf (3.18 MB)
(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. OnlineCADReconst.pdf (3.18 MB)
(2015). Pixel-wise Hybrid Image Registration on Wood Decors.
BW-CAR| SINCOM. 24. Grunwald_2015_Pixel-wiseHybridImageRegistration.pdf (2.42 MB)
(2015). Radiometric calibration of digital cameras using Gaussian processes.
SPIE Optics+ Optoelectronics. Schall et al_2015_Radiometric calibration of digital cameras using Gaussian processes.PDF (953.2 KB)
(2015). Support Vector Machines for Classification of Geometric Primitives in Point Clouds.
(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. 80-95. Caputo et al_2015_Support vector machines for classification of geometric primitives in point clouds.pdf (2.64 MB)
(2015). Support Vector Machines for Classification of Geometric Primitives in Point Clouds.
(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. 80-95. Caputo et al_2015_Support vector machines for classification of geometric primitives in point clouds.pdf (2.64 MB)
(2015).