Utvidet returrett til 31. januar 2025
Om Image Co-segmentation

This book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder¿decoder network, meta-learning, conditional variational encoder¿decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9789811985690
  • Bindende:
  • Hardback
  • Sider:
  • 236
  • Utgitt:
  • 3. februar 2023
  • Utgave:
  • 23001
  • Dimensjoner:
  • 160x18x241 mm.
  • Vekt:
  • 567 g.
  • BLACK NOVEMBER
  Gratis frakt
Leveringstid: Ukjent

Beskrivelse av Image Co-segmentation

This book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder¿decoder network, meta-learning, conditional variational encoder¿decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.

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