Grabber: A tool to improve convergence in interactive image segmentation

Autor(es)

Bragantini, Jordao ^[1] ; Moura, Bruno ^[2] ; Falcao, Alexandre X. ^[1] ; Cappabianco, Fabio A. M. ^[2]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Inst Comp, Lab Image Data Sci, R Saturnino Brito 573, BR-13083851 Campinas - Brazil

^[2] Univ Fed Sao Paulo, Inst Ciencia & Tecnol, Grp Innovat Based Images & Signals, Av Cesare Mansueto Giulio Lattes, BR-12247014 Sao Jose Dos Campos - Brazil

• Tipo de documento: Artigo Científico
• Fonte: PATTERN RECOGNITION LETTERS; v. 140, p. 267-273, DEC 2020.
• Citações Web of Science: 0

Resumo

Interactive image segmentation has considerably evolved from techniques that do not learn the parameters of the model to methods that pre-train a model and adapt it from user inputs during the process. However, user control over segmentation still requires significant improvements to avoid that corrections in one part of the object cause errors in other parts. We address this problem by presenting Grabber - a tool to improve convergence (user control) in interactive image segmentation. Grabber is thought to complete segmentation of some other initial method. From a given segmentation mask, Grabber quickly estimates anchor points in one orientation along the boundary of the mask and delineates an optimum contour constrained to pass through those points. The user can control the process by adding, removing, and moving anchor points. Grabber can also explore object properties from the initial coarse segmentation to improve boundary delineation. We integrate Grabber with two recent methods, a region based approach and a pixel classification method based on deep neural networks. Extensive experiments with robot users on two datasets show in both cases that Grabber can significantly improve convergence, with faster delineation, higher effectiveness, and less user effort. The code of Grabber is available at https://github.com/LIDS-UNICAMP/grabber. (c) 2020 Elsevier B.V. All rights reserved. (AU)

• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 19/11349-0 - Segmentação de imagens baseada em dynamic-trees e neural networks
• Beneficiário: Jordão Okuma Barbosa Ferraz Bragantini
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica
• Processo FAPESP: 16/21591-5 - Desenvolvimento de métodos robustos para delineamento de bordas em imagens utilizando grafos
• Beneficiário: Fábio Augusto Menocci Cappabianco
• Modalidade de apoio: Auxílio à Pesquisa - Regular