Robust Seeded Image Segmentation Using Adaptive Label Propagation and Deep Learning-Based Contour Orientation

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorBruzadin, Aldimir José-
Autor(es): dc.creatorColnago, Marilaine-
Autor(es): dc.creatorNegri, Rogério Galante-
Autor(es): dc.creatorCasaca, Wallace-
Data de aceite: dc.date.accessioned2025-08-21T15:12:50Z-
Data de disponibilização: dc.date.available2025-08-21T15:12:50Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-031-36808-0_2-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/298655-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/298655-
Descrição: dc.descriptionDeep Learning has become a popular tool for addressing complex tasks in many computer vision applications. Label diffusion methods have also been a very effective technique for getting accurate segmentations of real-world images, as they combine user autonomy, versatility and accurateness through a user-friendly interface. In this paper, we propose a seeded segmentation framework for partitioning real-world images by combining deep contour learning and graph-based label propagation models. More precisely, our approach takes a CNN-type contour detection network to learn graph edge weights, which are used as input to solve a coupled energy minimization problem that diffuses the user-selected annotations to the desired targets. To accurately extract deep features from image contours while generating diffusion maps, we train a deep learning architecture that integrates a hierarchical neural network, a graph-based label propagation model and a loss function, allowing the coupled training mechanism to refine the results until convergence. We attest to the effectiveness and accuracy of the proposed approach by conducting both quantitative and qualitative assessments with existing seeded image segmentation methods.-
Descrição: dc.descriptionIBILCE São Paulo State University-
Descrição: dc.descriptionIQ - São Paulo State University-
Descrição: dc.descriptionICT São Paulo State University-
Descrição: dc.descriptionIBILCE São Paulo State University-
Descrição: dc.descriptionIQ - São Paulo State University-
Descrição: dc.descriptionICT São Paulo State University-
Formato: dc.format19-31-
Idioma: dc.languageen-
Relação: dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectContour Learning-
Palavras-chave: dc.subjectSeeded Segmentation-
Título: dc.titleRobust Seeded Image Segmentation Using Adaptive Label Propagation and Deep Learning-Based Contour Orientation-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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