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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

INTELLIGENT UNDERSTANDING OF USER INTERACTION IN IMAGE SEGMENTATION

Texto completo
Autor(es):
Spina, Thiago V. [1] ; De Miranda, Paulo A. V. [2] ; Falcao, Alexandre X. [1]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Univ Campinas UNICAMP, Inst Comp, Campinas, SP - Brazil
[2] Univ Sao Paulo, Inst Math & Stat, Dept Comp Sci, Sao Paulo - Brazil
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE; v. 26, n. 2 MAR 2012.
Citações Web of Science: 4
Resumo

We have developed interactive tools for graph-based segmentation of natural images, in which the user guides object delineation by drawing strokes (markers) inside and outside the object. A suitable arc-weight estimation is paramount to minimize user time and maximize segmentation accuracy in these tools. However, it depends on discriminative image properties for object and background. These properties can be obtained from some marker pixels, but their identification is a hard problemduring delineation. Careless arc-weight re-estimation reduces user control and drops performance, while interactive arc-weight estimation in a step before interactive object extraction is the best option so far, albeit it is not intuitive for nonexpert users. We present an effective solution using the unified framework of the image foresting transform (IFT) with three operators: clustering for interpreting user interaction and determining when and where arc weights need to be re-estimated; fuzzy classification for arc-weight estimation; and marker competition based on optimum connectivity for object extraction. For validation, we compared the proposed approach with another interactive IFT-based method, which computes arc weights before extraction. Evaluation involved multiple users (experts and nonexperts), a dataset with several natural images, and measurements to quantify accuracy, precision, efficiency (user time and computation time), and user control, being some of them novel measurements, proposed in this work. (AU)

Processo FAPESP: 09/16428-4 - Segmentação de estruturas em múltiplas modalidades de imagens de ressonância magnética do cérebro humano
Beneficiário:Paulo André Vechiatto de Miranda
Linha de fomento: Bolsas no Brasil - Pós-Doutorado
Processo FAPESP: 09/11908-8 - Edição interativa de imagens naturais baseada na transformada imagem-floresta
Beneficiário:Thiago Vallin Spina
Linha de fomento: Bolsas no Brasil - Mestrado
Processo FAPESP: 07/52015-0 - Métodos de aproximação para computação visual
Beneficiário:Jorge Stolfi
Linha de fomento: Auxílio à Pesquisa - Temático