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 metho...
Պահպանված է:
| Հիմնական հեղինակներ: | , , , |
|---|---|
| Համատեղ հեղինակ: | |
| Ձևաչափ: | Էլեկտրոնային էլ․ գիրք |
| Լեզու: | անգլերեն |
| Հրապարակվել է: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2023.
|
| Հրատարակություն: | 1st ed. 2023. |
| Շարք: | Studies in Computational Intelligence,
1082 |
| Խորագրեր: | |
| Առցանց հասանելիություն: | https://doi.org/10.1007/978-981-19-8570-6 |
| Ցուցիչներ: |
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MARC
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| 024 | 7 | |a 10.1007/978-981-19-8570-6 |2 doi | |
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| 100 | 1 | |a Hati, Avik. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Image Co-segmentation |h [electronic resource] / |c by Avik Hati, Rajbabu Velmurugan, Sayan Banerjee, Subhasis Chaudhuri. |
| 250 | |a 1st ed. 2023. | ||
| 264 | 1 | |a Singapore : |b Springer Nature Singapore : |b Imprint: Springer, |c 2023. | |
| 300 | |a 1 online resource (XIV, 221 p. 118 illus.). |b 110 illus. in color. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
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| 490 | 1 | |a Studies in Computational Intelligence, |x 1860-9503 ; |v 1082 | |
| 505 | 0 | |a Introduction -- Survey of Image Co-segmentation -- Mathematical Background -- Co-segmentation using a Classification Framework -- Use of Maximum Common Subgraph Matching -- Maximally Occurring Common Subgraph Matching -- Co-segmentation using Graph Convolutional Neural Network -- Use of a Conditional Siamese Convolutional Network -- Few-shot Learning for Co-segmentation -- Conclusions. | |
| 520 | |a 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. | ||
| 588 | |a Print version record. | ||
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Image processing. | |
| 650 | 1 | 4 | |a Signal, Speech and Image Processing. |
| 650 | 2 | 4 | |a Digital and Analog Signal Processing. |
| 650 | 2 | 4 | |a Image Processing. |
| 700 | 1 | |a Velmurugan, Rajbabu. |e author. |0 (orcid)0000-0002-3511-1806 |1 https://orcid.org/0000-0002-3511-1806 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Banerjee, Sayan. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Chaudhuri, Subhasis. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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| 776 | 0 | 8 | |i Printed edition: |z 9789811985690 |
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| 830 | 0 | |a Studies in Computational Intelligence, |x 1860-9503 ; |v 1082 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-981-19-8570-6 |
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| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||