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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