| Full text | |
| Author(s): |
Total Authors: 4
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| Affiliation: | [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
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | PATTERN RECOGNITION LETTERS; v. 140, p. 267-273, DEC 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
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) | |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 19/11349-0 - Image segmentation based on dynamic trees and neural networks |
| Grantee: | Jordão Okuma Barbosa Ferraz Bragantini |
| Support Opportunities: | Scholarships in Brazil - Scientific Initiation |
| FAPESP's process: | 16/21591-5 - Development of robust methods for edge delineation in images using graphs |
| Grantee: | Fábio Augusto Menocci Cappabianco |
| Support Opportunities: | Regular Research Grants |