2016
S. Karthik Mustikovela, Yang, M. Ying, and Rother, C.,
“Can ground truth label propagation from video help semantic segmentation?”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9915 LNCS, pp. 804–820.
L. A. Royer, Richmond, D. L., Rother, C., Andres, B., and Kainmueller, D.,
“Convexity shape constraints for image segmentation”, in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 402–410.
L. A. Royer, Richmond, D. L., Rother, C., Andres, B., and Kainmueller, D.,
“Convexity shape constraints for image segmentation”, in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 402–410.
L. A. Royer, Richmond, D. L., Rother, C., Andres, B., and Kainmueller, D.,
“Convexity shape constraints for image segmentation”, in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 402–410.
M. von Borstel, Kandemir, M., Schmidt, P., Rao, M., Rajamani, K., and Hamprecht, F. A.,
“Gaussian process density counting from weak supervision”,
ECCV. Proceedings, vol. LNCS 9905. Springer, pp. 365-380 , 2016.
Technical Report (1.71 MB) M. von Borstel, Kandemir, M., Schmidt, P., Rao, M., Rajamani, K., and Hamprecht, F. A.,
“Gaussian process density counting from weak supervision”,
ECCV. Proceedings, vol. LNCS 9905. Springer, pp. 365-380 , 2016.
Technical Report (1.71 MB) D. Kondermann, Nair, R., Honauer, K., Krispin, K., Andrulis, J., Brock, A., Güssefeld, B., Rahimimoghaddam, M., Hofmann, S., Brenner, C., and Jähne, B.,
“The HCI Benchmark Suite: Stereo and Flow Ground Truth With Uncertainties for Urban Autonomous Driving”, in
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016.
J. Kappes, Speth, M., Reinelt, G., and Schnörr, C.,
“Higher-order Segmentation via Multicuts”,
Comp. Vision Image Understanding, vol. 143, pp. 104–119, 2016.
J. Mund, Michel, F., Dieke-Meier, F., Fricke, H., Meyer, L., and Rother, C.,
“Introducing LiDAR Point Cloud-based Object Classification for Safer Apron Operations”, in
International Symposium on Enhanced Solutions for Aircraft and Vehicle Surveillance Applications, 2016.
P. Pinggera, Ramos, S., Gehrig, S., Franke, U., Rother, C., and Mester, R.,
“Lost and found: Detecting small road hazards for self-driving vehicles”, in
IEEE International Conference on Intelligent Robots and Systems, 2016, vol. 2016-Novem, pp. 1099–1106.
P. Pinggera, Ramos, S., Gehrig, S., Franke, U., Rother, C., and Mester, R.,
“Lost and found: Detecting small road hazards for self-driving vehicles”, in
IEEE International Conference on Intelligent Robots and Systems, 2016, vol. 2016-Novem, pp. 1099–1106.
D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C.,
“Mapping auto-context decision forests to deep convnets for semantic segmentation”, in
British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.
D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C.,
“Mapping auto-context decision forests to deep convnets for semantic segmentation”, in
British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.
D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C.,
“Mapping auto-context decision forests to deep convnets for semantic segmentation”, in
British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.
D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C.,
“Mapping auto-context decision forests to deep convnets for semantic segmentation”, in
British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.
D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C.,
“Mapping auto-context decision forests to deep convnets for semantic segmentation”, in
British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.
D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C.,
“Mapping auto-context decision forests to deep convnets for semantic segmentation”, in
British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.
A. Sellent, Rother, C., and Roth, S.,
“Stereo video deblurring”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9906 LNCS, pp. 558–575.
A. Sellent, Rother, C., and Roth, S.,
“Stereo video deblurring”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9906 LNCS, pp. 558–575.
E. Brachmann, Michel, F., Krull, A., Yang, M. Ying, Gumhold, S., and Rother, C.,
“Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image”, in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 3364–3372.
E. Brachmann, Michel, F., Krull, A., Yang, M. Ying, Gumhold, S., and Rother, C.,
“Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image”, in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 3364–3372.
M. Kandemir, Haußmann, M., Diego, F., Rajamani, K., van der Laak, J., and Hamprecht, F. A.,
“Variational weakly-supervised Gaussian processes”,
BMVC. Proceedings. 2016.
Technical Report (3.28 MB)