A new training strategy for spatial transform networks (STN’s)

نویسندگان

چکیده

Abstract Spatial transform networks (STN) are widely used since they can images captured from different viewpoints to obtain an objective image. These use image any viewpoint as input and the desired a label. Usually, these segmented, but this could lead convergence problems if percentage of overlap between segmented is quite low. In paper, we propose new training method facilitate STN in cases, even when there no object’s projections two images. This strategy based on incorporation distance transformation training, thus increasing useful information provide gradients loss function. has been applied real case, with Caenorhabditis elegans , simulated which uses artificial ensure that for assays. assays carried out datasets, have shown strengthened, reaching precision level IoU metric 0.862 0.984, respectively, computational cost maintained compared assay images, case.

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ژورنال

عنوان ژورنال: Neural Computing and Applications

سال: 2022

ISSN: ['0941-0643', '1433-3058']

DOI: https://doi.org/10.1007/s00521-022-06993-0