Contributo in atti di convegno, 2019, ENG, 10.2312/egs.20191014
Pavoni G.; Corsini M.; Palma M.; Scopigno R.
CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; University of Modena and Reggio Emilia, Modena, Italy; Università Politecnica delle Marche, Ancona, Italy; CNR-ISTI, Pisa, Italy
The automatic recognition of natural structures is a challenging task in the supervised learning field. Complex morphologies are difficult to detect both from the networks, that may suffer from generalization issues, and from human operators, affecting the consistency of training datasets. The task of manual annotating biological structures is not comparable to a generic task of detecting an object (a car, a cat, or a flower) within an image. Biological structures are more similar to textures, and specimen borders exhibit intricate shapes. In this specific context, manual labelling is very sensitive to human error. The interactive validation of the predictions is a valuable resource to improve the network performance and address the inaccuracy caused by the lack of annotation consistency of human operators reported in literature. The proposed tool, inspired by the Yes/No Answer paradigm, integrates the semantic segmentation results coming from a CNN with the previous human labeling, allowing a more accurate annotation of thousands of instances in a short time. At the end of the validation, it is possible to obtain corrected statistics or export the integrated dataset and re-train the network.
Eurographics 2019, pp. 57–60, Genova, 6/5/2019-10/5/2019
Human-center Computing, Graphical User Interfaces, Visual Analytics, Image Segmentation
Pavoni Gaia, Corsini Massimiliano, Scopigno Roberto
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 423390
Year: 2019
Type: Contributo in atti di convegno
Creation: 2020-06-04 00:26:27.000
Last update: 2020-10-16 12:26:09.000
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:423390
DOI: 10.2312/egs.20191014
Scopus: 2-s2.0-85065652703