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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:
  • Engelska
  • ISBN:
  • 9789811985720
  • Format:
  • Häftad
  • Sidor:
  • 236
  • Utgiven:
  • 3 Februari 2024
  • Utgåva:
  • 24001
  • Mått:
  • 155x13x235 mm.
  • Vikt:
  • 365 g.
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Leveranstid: 2-4 veckor
Förväntad leverans: 29 Maj 2024

Beskrivning 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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