Outputs (3)

Unsupervised Rotation Factorization in Restricted Boltzmann Machines (2019)
Journal Article
Giuffrida, M. V., & Tsaftaris, S. A. (2020). Unsupervised Rotation Factorization in Restricted Boltzmann Machines. IEEE Transactions on Image Processing, 29(1), 2166-2175. https://doi.org/10.1109/TIP.2019.2946455

Finding suitable image representations for the task at hand is critical in computer vision. Different approaches extending the original Restricted Boltzmann Machine (RBM) model have recently been proposed to offer rotation-invariant feature learning.... Read More about Unsupervised Rotation Factorization in Restricted Boltzmann Machines.

Leaf Counting Without Annotations Using Adversarial Unsupervised Domain Adaptation (2019)
Presentation / Conference Contribution
Giuffrida, M. V., Dobrescu, A., Doerner, P., & Tsaftaris, S. A. (2019). Leaf Counting Without Annotations Using Adversarial Unsupervised Domain Adaptation.

Deep learning is making strides in plant phenotyping and agriculture. But pretrained models require significant adaptation to work on new target datasets originating from a different experiment even on the same species. The current solution is to ret... Read More about Leaf Counting Without Annotations Using Adversarial Unsupervised Domain Adaptation.

Adversarial Large-scale Root Gap Inpainting (2019)
Presentation / Conference Contribution
Chen, H., Giuffrida, M. V., Doerner, P., & Tsaftaris, S. A. (2019). Adversarial Large-scale Root Gap Inpainting.

Root imaging of a growing plant in a non-invasive, affordable , and effective way remains challenging. One approach is to image roots by growing them in a rhizobox, a soil-filled transparent container, imaging them with digital cameras, and segmentin... Read More about Adversarial Large-scale Root Gap Inpainting.