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File Format | PDF
File Size | 4.44 MB
Pages | 325
Language | English
Category | Programming
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Description: Two important
subproblems of computer vision are the detection and recognition of 2D objects
in gray-level images. This book discusses the construction and training of
models, computational approaches to efficient implementation, and parallel
implementations in biologically plausible neural network architectures.
The approach is
based on statistical modeling and estimation, with an emphasis on simplicity,
transparency, and computational efficiency.The book describes a range of
deformable template models, from coarse sparse models involving discrete, fast
computations to more finely detailed models based on continuum formulations,
involving intensive optimization.
Each model is
defined in terms of a subset of points on a reference grid (the template), a
set of admissible instantiations of these points (deformations), and a
statistical model for the data given a particular instantiation of the object
present in the image. A recurring theme is a coarse to fine approach to the
solution of vision problems. The book provides detailed descriptions of the
algorithms used as well as the code, and the software and data sets are
available on the Web.
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2D Object Detection and Recognition: Models, Algorithms, and Networks
Download | Free
