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Ear semantic segmentation in natural images with Tversky loss function supported DeepLabv3+ convolutional neural network

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dc.contributor.author İnan, Tolga
dc.contributor.author Kaçar, Ümit
dc.date.accessioned 2024-03-12T13:25:39Z
dc.date.available 2024-03-12T13:25:39Z
dc.date.issued 2022
dc.identifier.citation İnan, Tolga; Kaçar, Ümit. (2022). "Ear semantic segmentation in natural images with Tversky loss function supported DeepLabv3+ convolutional neural network", Balkan Journal of Electrical and Computer Engineering, Vol.10, No.3, pp.337-346. tr_TR
dc.identifier.issn 2147-284X
dc.identifier.uri http://hdl.handle.net/20.500.12416/7556
dc.description.abstract Semantic segmentation is a fundamental problem for computer vision. On the other hand, for studies in the field of biometrics, semantic segmentation is gaining more importance. Many successful biometric recognition systems require a high- performance semantic segmentation algorithm. In this study, we present an effective ear segmentation technique in natural images. A convolutional neural network is trained for pixel-based ear segmentation. DeepLab v3+ network structure, with ResNet-18 as the backbone and Tversky lost function layer as the last layer, has been trained with natural and uncontrolled images. We perform the proposed network training using only the 750 images in the Annotated Web Ears (AWE) training set. The corresponding tests are performed on the AWE Test Set, University of Ljubljana Test Set, and the Collection A of In-The-Wild dataset. For the Annotated Web Ears (AWE) dataset, intersection over union (IoU) is measured as 86.3% for the AWE database. To the best of our knowledge, this is the highest performance achieved among the algorithms tested on the AWE test set. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.17694/bajece.1024073 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Semantic Segmentation tr_TR
dc.subject Ear Segmentation tr_TR
dc.subject Convolutional Neural Networks tr_TR
dc.subject Tversky Loss Function tr_TR
dc.subject Biometrics tr_TR
dc.title Ear semantic segmentation in natural images with Tversky loss function supported DeepLabv3+ convolutional neural network tr_TR
dc.type article tr_TR
dc.relation.journal Balkan Journal of Electrical and Computer Engineering tr_TR
dc.identifier.volume 10 tr_TR
dc.identifier.issue 3 tr_TR
dc.identifier.startpage 337 tr_TR
dc.identifier.endpage 346 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü tr_TR


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