A Validation Tool For Improving Semantic Segmentation of Complex Natural Structures
| dc.contributor.author | Pavoni, Gaia | en_US |
| dc.contributor.author | Corsini, Massimiliano | en_US |
| dc.contributor.author | Palma, Marco | en_US |
| dc.contributor.author | Scopigno, Roberto | en_US |
| dc.contributor.editor | Cignoni, Paolo and Miguel, Eder | en_US |
| dc.date.accessioned | 2019-05-05T17:49:54Z | |
| dc.date.available | 2019-05-05T17:49:54Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | 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. | en_US |
| dc.description.sectionheaders | Learning and Networks | |
| dc.description.seriesinformation | Eurographics 2019 - Short Papers | |
| dc.identifier.doi | 10.2312/egs.20191014 | |
| dc.identifier.issn | 1017-4656 | |
| dc.identifier.pages | 57-60 | |
| dc.identifier.uri | https://doi.org/10.2312/egs.20191014 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/egs20191014 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | Human | |
| dc.subject | centered computing | |
| dc.subject | Graphical user interfaces | |
| dc.subject | Visual analytics | |
| dc.subject | Computing methodologies | |
| dc.subject | Image segmentation | |
| dc.title | A Validation Tool For Improving Semantic Segmentation of Complex Natural Structures | en_US |
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