Hryniowski, A., Ben Daya, I., Gawish, A., Lamm, M., Wong, A., & Fieguth, P. (2018). Multi-projector resolution enhancement through biased interpolation 2018 15th Conference on Computer and Robot Vision (CRV), 190-197. IEEE.
References
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2018
Hryniowski, A. (2018). A Content Enhancement Framework for Multi-Projector Systems
Hu, X., Naiel, M. A., Azimifar, Z., Ben Daya, I., Lamm, M., & Fieguth, P. (2018). Text enhancement in projected imagery Journal of Computational Vision and Imaging Systems, 4, 3-3.
Chung, A., Fieguth, P., & Wong, A. (2018). Nature vs. nurture: The role of environmental resources in evolutionary deep intelligence 2018 15th Conference on Computer and Robot Vision (CRV), 368-374. IEEE.
Bello-Cerezo, R., Fieguth, P., & Bianconi, F. (2018). LBP-motivated colour texture classification Proceedings of the European Conference on Computer Vision (ECCV), 0-0.
Sankar, V., Gawish, A., Fieguth, P., & Lamm, M. (2018). Understanding Blur and Model Learning in Projector Compensation Journal of Computational Vision and Imaging Systems, 4, 3-3.
Hu, X., Chung, A. G., Fieguth, P., Khalvati, F., Haider, M. A., & Wong, A. (2018). Prostategan: Mitigating data bias via prostate diffusion imaging synthesis with generative adversarial networks ArXiv Preprint ArXiv:1811.05817.
Carrington, A., Fieguth, P., & Chen, H. (2018). Measures of model interpretability for model selection International Cross-Domain Conference for Machine Learning and Knowledge Extraction, 329-349. Springer, Cham.