<p><span>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. Th
Image Co-segmentation
β Scribed by Avik Hati; Rajbabu Velmurugan; Sayan Banerjee; Subhasis Chaudhuri
- Publisher
- Springer Nature
- Year
- 2023
- Tongue
- English
- Leaves
- 231
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
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 encoderβdecoder network, meta-learning, conditional variational encoderβdecoder, 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.
π SIMILAR VOLUMES
<p>None.</p>
The sequel to the popular lecture book entitled Biomedical Image Analysis: Tracking, this book on Biomedical Image Analysis: Segmentation tackles the challenging task of segmenting biological and medical images. The problem of partitioning multidimensional biomedical data into meaningful regions is
The sequel to the popular lecture book entitled Biomedical Image Analysis: Tracking, this book on Biomedical Image Analysis: Segmentation tackles the challenging task of segmenting biological and medical images. The problem of partitioning multidimensional biomedical data into meaningful regions is