Deep Image Segmentation by Quality Inference

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2018

Autores

Kelwin Fernandes (Autor) Outra; Ricardo Cruz (Autor) Outra; Jaime S. Cardoso (Autor) FEUP

Ata de Conferência Internacional

Título: Proceedings of the International Joint Conference on Neural Networks

Páginas: 1-8

2018 International Joint Conference on Neural Networks, IJCNN 2018

8 July 2018 through 13 July 2018

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 10 Citações

INSPEC

Outras Informações

ID Authenticus: P-00P-SWF

DOI: 10.1109/ijcnn.2018.8489696

Abstract (EN): Traditionally, convolutional neural networks are trained for semantic segmentation by having an image given as input and the segmented mask as output. In this work, we propose a neural network trained by being given an image and mask pair, with the output being the quality of that pairing. The segmentation is then created afterwards through backpropagation on the mask. This allows enriching training with semi-supervised synthetic variations on the ground-truth. The proposed iterative segmentation technique allows improving an existing segmentation or creating one from scratch. We compare the performance of the proposed methodology with state-of-the-art deep architectures for image segmentation and achieve competitive results, being able to improve their segmentations. © 2018 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8