A Validation Tool For Improving Semantic Segmentation of Complex Natural Structures

dc.contributor.authorPavoni, Gaiaen_US
dc.contributor.authorCorsini, Massimilianoen_US
dc.contributor.authorPalma, Marcoen_US
dc.contributor.authorScopigno, Robertoen_US
dc.contributor.editorCignoni, Paolo and Miguel, Ederen_US
dc.date.accessioned2019-05-05T17:49:54Z
dc.date.available2019-05-05T17:49:54Z
dc.date.issued2019
dc.description.abstractThe 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.sectionheadersLearning and Networks
dc.description.seriesinformationEurographics 2019 - Short Papers
dc.identifier.doi10.2312/egs.20191014
dc.identifier.issn1017-4656
dc.identifier.pages57-60
dc.identifier.urihttps://doi.org/10.2312/egs.20191014
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egs20191014
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectGraphical user interfaces
dc.subjectVisual analytics
dc.subjectComputing methodologies
dc.subjectImage segmentation
dc.titleA Validation Tool For Improving Semantic Segmentation of Complex Natural Structuresen_US
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