7 papers
Unsupervised Pelage Pattern Unwrapping for Animal Re-identification
Aleksandr Algasov, Ekaterina Nepovinnykh, Fedor Zolotarev +4
Existing individual re-identification methods often struggle with the deformable nature of animal fur or skin patterns which undergo geometric distortions due to body movement and…
Self-Supervised Pretraining for Fine-Grained Plankton Recognition
Joona Kareinen, Tuomas Eerola, Kaisa Kraft +3
Plankton recognition is an important computer vision problem due to plankton's essential role in ocean food webs and carbon capture, highlighting the need for species-level monitor…
Multimodal surface defect detection from wooden logs for sawing optimization
BoÅek Reich, Matej Kunda, Fedor Zolotarev +3
We propose a novel, good-quality, and less demanding method for detecting knots on the surface of wooden logs using multimodal data fusion. Knots are a primary factor affecting the…
Towards synthetic generation of realistic wooden logs
Fedor Zolotarev, Borek Reich, Tuomas Eerola +2
In this work, we propose a novel method to synthetically generate realistic 3D representations of wooden logs. Efficient sawmilling heavily relies on accurate measurement of logs a…
Deep Unsupervised Segmentation of Log Point Clouds
Fedor Zolotarev, Tuomas Eerola, Tomi Kauppi
In sawmills, it is essential to accurately measure the raw material, i.e. wooden logs, to optimise the sawing process. Earlier studies have shown that accurate predictions of the i…
Open-Set Plankton Recognition
Joona Kareinen, Annaliina Skyttä, Tuomas Eerola +5
This paper considers open-set recognition (OSR) of plankton images. Plankton include a diverse range of microscopic aquatic organisms that have an important role in marine ecosyste…