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

  • Conference paper
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Computational Science and Its Applications – ICCSA 2023 (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13957))

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Abstract

Deep 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.

This research has been funded by the São Paulo Research Foundation (FAPESP – grants 2013/07375-0, #2021/03328-3 and #2021/01305-6), the Coordination for the Improvement of Higher Education Personnel (CAPES - Funding Code 001), and the National Council for Scientific and Technological Development (CNPq – grants #316228/2021-4 and #305220/2022-5).

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Acknowledgment

The authors would like to thank the São Paulo Research Foundation (FAPESP – grants #2013/07375-0, #2021/03328-3 and #2021/01305-6), the Coordination for the Improvement of Higher Education Personnel (CAPES - Funding Code 001), and the National Council for Scientific and Technological Development (CNPq – grants #316228/2021-4 and #305220/2022-5) for providing resources that greatly contributed to the development of this research.

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Authors and Affiliations

  1. IBILCE, São Paulo State University, São José do Rio Preto - SP, Brazil

    Aldimir José Bruzadin & Wallace Casaca

  2. IQ - São Paulo State University, Araraquara - SP, Brazil

    Marilaine Colnago

  3. ICT, São Paulo State University, São José dos Campos - SP, Brazil

    Rogério Galante Negri

Authors
  1. Aldimir José Bruzadin
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  2. Marilaine Colnago
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  3. Rogério Galante Negri
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  4. Wallace Casaca
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Corresponding author

Correspondence to Wallace Casaca .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Monash University, Clayton, VIC, Australia

    David Taniar

  4. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  5. University of Minho, Braga, Portugal

    Ana Cristina Braga

  6. University of Cagliari, Cagliari, Italy

    Chiara Garau

  7. National Technical University of Athens, Athens, Greece

    Anastasia Stratigea

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Bruzadin, A.J., Colnago, M., Negri, R.G., Casaca, W. (2023). Robust Seeded Image Segmentation Using Adaptive Label Propagation and Deep Learning-Based Contour Orientation. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, vol 13957. Springer, Cham. https://doi.org/10.1007/978-3-031-36808-0_2

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  • DOI: https://doi.org/10.1007/978-3-031-36808-0_2

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