HANDS19 Challenge

Our colleagues Marek Hrúz, Jakub Kanis, and Zdeněk Krňoul won the HANDS19 Challenge Task 2. HANDS19 Challenge is a public competition designed for the evaluation of the task of 3D hand pose estimation in both depth and color modalities in the presence and absence of objects. The goal of Task 2 is depth-based 3D Hand Pose Estimation while Interacting with Objects: This task builds on F-PHAB dataset. Objects appear to be manipulated by a subject in an egocentric viewpoint. Some hand shapes and objects are strategically excluded from the training set in order to measure the interpolation and extrapolation capabilities of submissions. The first prize was announced and presented at a workshop held at the International Conference on Computer Vision (ICCV) 2019.

Towards Regression Task-Oriented Annotation Tool for Microscopic Images

Annotating a dataset for training a Supervised Machine Learning algorithm is time and annotator’s attention intensive. Our colleagues from the Department of Cybernetics (Miroslav Jirik, Ivan Gruber, and Milos Zelezny) developed a procedure for producing the datasets based on microscopic whole slide images for regression tasks. Together with colleagues from the Biomedical Center of Charles University, they prepared an open-source application for creating annotations of the dataset with minimal demands on the expert’s time. The work was published in Lecture Notes in Computer Science (SJR=0.283).

Jirik M. et al. (2020) MicrAnt: Towards Regression Task Oriented Annotation Tool for Microscopic Images. In: Lukić T., Barneva R., Brimkov V., Čomić L., Sladoje N. (eds) Combinatorial Image Analysis. IWCIA 2020. Lecture Notes in Computer Science, vol 12148. Springer, Cham. https://doi.org/10.1007/978-3-030-51002-2_15