The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Structured Learning and Prediction in Computer Vision
Abstract
Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structure reflecting task-specific relations and constraints. Structured Learning and Prediction in Computer Vision introduces the reader to the most popular classes of structured models in computer vision. The focus is on discrete undirected graphical models which are covered in detail together with a description of algorithms for both probabilistic inference and maximum a posteriori inference. It also discusses separately recently successful techniques for prediction in general structured models. The second part of Structured Learning and Prediction in Computer Vision describes methods for parameter learning, distinguishing the classic maximum likelihood based methods from the more recent prediction-based parameter learning methods. It highlights developments to enhance current models and discusses kernelized models and latent variable models. Throughout Structured Learning and Prediction in Computer Vision the main text is interleaved with successful computer vision applications of the explained techniques. For convenience the reader can find a summary of the notation used at the end of the book.
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Cites
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Fast Edge Detection Using Structured Forests
Dollar, P., & Zitnick, C. L. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2377715 · Cited by 962
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
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Human Pose Estimation with Iterative Error Feedback
Carreira, J., Agrawal, P., Fragkiadaki, K., & Malik, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.512 · Cited by 805
Smart “Predict, then Optimize”
Elmachtoub, A. N., & Grigas, P. (2022) · Management Science · doi:10.1287/mnsc.2020.3922 · Cited by 773
An Introduction to Conditional Random Fields
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Metadata
- Authors
- Nowozin, S.
- Venue
- now publishers, Inc. eBooks
- Publisher
- now Publishers Inc
- Published
- 2010
- OpenAlex
- W2136064009
- Cited by
- 239
- References
- 11
- FWCI
- 4.17
- Type
- book
- Language
- en
- Open access
- closed
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