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Stochastic Modeling for Medical Image Analysis provides a brief
introduction to medical imaging, stochastic modeling, and
model-guided image analysis. Today, image-guided computer-assisted
diagnostics (CAD) faces two basic challenging problems. The first
is the computationally feasible and accurate modeling of images
from different modalities to obtain clinically useful information.
The second is the accurate and fast inferring of meaningful and
clinically valid CAD decisions and/or predictions on the basis of
model-guided image analysis. To help address this, this book
details original stochastic appearance and shape models with
computationally feasible and efficient learning techniques for
improving the performance of object detection, segmentation,
alignment, and analysis in a number of important CAD applications.
The book demonstrates accurate descriptions of visual appearances
and shapes of the goal objects and their background to help solve a
number of important and challenging CAD problems. The models focus
on the first-order marginals of pixel/voxel-wise signals and
second- or higher-order Markov-Gibbs random fields of these signals
and/or labels of regions supporting the goal objects in the
lattice. This valuable resource presents the latest state of the
art in stochastic modeling for medical image analysis while
incorporating fully tested experimental results throughout.
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Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, SSPR & SPR 2012, Hiroshima, Japan, November 7-9, 2012, Proceedings (Paperback, 2012)
Georgy Gimel'farb, Edwin Hancock, Atsushi Imiya, Arjan Kuijper, Mineichi Kudo, …
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R1,708
Discovery Miles 17 080
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Ships in 10 - 15 working days
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This volume constitutes the refereed proceedings of the Joint IAPR
International Workshops on Structural and Syntactic Pattern
Recognition (SSPR 2012) and Statistical Techniques in Pattern
Recognition (SPR 2012), held in Hiroshima, Japan, in November 2012
as a satellite event of the 21st International Conference on
Pattern Recognition, ICPR 2012. The 80 revised full papers
presented together with 1 invited paper and the Pierre Devijver
award lecture were carefully reviewed and selected from more than
120 initial submissions. The papers are organized in topical
sections on structural, syntactical, and statistical pattern
recognition, graph and tree methods, randomized methods and image
analysis, kernel methods in structural and syntactical pattern
recognition, applications of structural and syntactical pattern
recognition, clustering, learning, kernel methods in statistical
pattern recognition, kernel methods in statistical pattern
recognition, as well as applications of structural, syntactical,
and statistical methods.
This book constitutes the thoroughly refereed post-proceedings of
the 10th International Workshop on Theoretical Foundations of
Computer Vision, held at Dagstuhl Castle, Germany, in March
2000.
The 20 revised full papers presented have been through two rounds
of reviewing, selection, and revision and give a representative
assessment of the foundational issues in multiple-image processing.
The papers are organized in topical sections on 3D data acquisition
and sensor design, multi-image analysis, data fusion in 3D scene
description, and applied 3D vision and virtual reality.
Stochastic Modeling for Medical Image Analysis provides a brief
introduction to medical imaging, stochastic modeling, and
model-guided image analysis. Today, image-guided computer-assisted
diagnostics (CAD) faces two basic challenging problems. The first
is the computationally feasible and accurate modeling of images
from different modalities to obtain clinically useful information.
The second is the accurate and fast inferring of meaningful and
clinically valid CAD decisions and/or predictions on the basis of
model-guided image analysis. To help address this, this book
details original stochastic appearance and shape models with
computationally feasible and efficient learning techniques for
improving the performance of object detection, segmentation,
alignment, and analysis in a number of important CAD applications.
The book demonstrates accurate descriptions of visual appearances
and shapes of the goal objects and their background to help solve a
number of important and challenging CAD problems. The models focus
on the first-order marginals of pixel/voxel-wise signals and
second- or higher-order Markov-Gibbs random fields of these signals
and/or labels of regions supporting the goal objects in the
lattice. This valuable resource presents the latest state of the
art in stochastic modeling for medical image analysis while
incorporating fully tested experimental results throughout.
This textbook guides readers through their first steps into the
challenging world of mimicking human vision with computational
tools and techniques pertaining to the field of image processing
and analysis. While today's theoretical and applied processing and
analysis of images meet with challenging and complex problems, this
primer is confined to a much simpler, albeit critical, collection
of image-to-image transformations, including image normalisation,
enhancement, and filtering.It serves as an introduction to
beginners, a refresher for undergraduate and graduate students, as
well as engineers and computer scientists confronted with a problem
to solve in computer vision. The book covers basic image
processing/computer vision pipeline techniques, which are widely
used in today's computer vision, computer graphics, and image
processing, giving the readers enough knowledge to successfully
tackle a wide range of applied problems.
This textbook guides readers through their first steps into the
challenging world of mimicking human vision with computational
tools and techniques pertaining to the field of image processing
and analysis. While today's theoretical and applied processing and
analysis of images meet with challenging and complex problems, this
primer is confined to a much simpler, albeit critical, collection
of image-to-image transformations, including image normalisation,
enhancement, and filtering.It serves as an introduction to
beginners, a refresher for undergraduate and graduate students, as
well as engineers and computer scientists confronted with a problem
to solve in computer vision. The book covers basic image
processing/computer vision pipeline techniques, which are widely
used in today's computer vision, computer graphics, and image
processing, giving the readers enough knowledge to successfully
tackle a wide range of applied problems.
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