[4eyes] New Computer Vision Textbook

[Chris Sweeney]

Free PDF version available on the website

---------- Forwarded message ----------
From: Simon Prince <s.prince at cs.ucl.ac.uk>
Date: Sat, Sep 1, 2012 at 5:57 PM
Subject: [Imageworld] New vision textbook now available
To: imageworld at diku.dk


COMPUTER VISION: MODELS, LEARNING AND INFERENCE

Simon J.D. Prince


This new computer vision textbook from Cambridge University Press is now
available from all good booksellers. The final electronic version has also
been posted at the book website:

http://www.computervisionmodels.com

This website also includes more than 1000 powerpoint slides to accompany
the text as well as answers to the problems, and an algorithm booklet
including pseudocode for more than 70 of the algorithms discussed in the
book.

The book would be suitable for an advanced undergraduate or graduate course
in computer vision or machine learning. It would also be useful for most
computer vision MSc and PhD students in these areas.

The book is divided into six parts.


PART I: PROBABILITY

1. Introduction. 2. Joint, marginal, conditional probability, independence,
expectation. 3. Probability distributions, conjugacy. 4. ML, MAP and
Bayesian approaches. 5. The multivariate normal distribution.


PART II: MACHINE LEARNING

6. Introduction to machine learning, generative and discriminative models.
7. Hidden variables, MoG, t-distributions, factor analysis, EM algorithm.
8. Linear regression, non-linear regression, Gaussian process regression,
relevance vector regression. 9. Logistic regression, Gaussian process
classification, boosting, classification trees.


PART III: CONNECTING LOCAL MODELS

10. Conditional independence, directed and undirected graphical models,
ancestral sampling, Gibbs sampling, contrastive divergence. 11. Hidden
Markov models, dynamic programming, belief propagation. 12. Markov random
fields, binary graph cuts, multi-label graph cuts, alpha-expansion
algorithm.


PART IV: PREPROCESSING

13. Image preprocessing, filtering, edge and corner detectors, local binary
patterns, SIFT, HoG, principal components analysis, k-means algorithm.


PART V: GEOMETRY

14. Pinhole camera model, homogeneous coordinates, estimating camera pose,
calibration, reconstruction. 15. Transformations, fitting transformations,
estimating pose to planem, calibration from planes, homographies, RANSAC,
PEaRL. 16. Epipolar geometry, essential and fundamental matrices, bundle
adjustment, rectification, multi-view reconstruction.


PART VI: COMPUTER VISION MODELS

17. Snakes, shape template models, statistical shape models, active shape
models, active appearance models, GPLVM, articulated models. 18. Identity
recognition, subspace models, probabilistic linear discriminant analysis,
bilinear models, multi-linear models, multi-factor GPLVM. 19. Kalman
filter, extended Kalman filter, unscented Kalman filter, particle filters.
20. Bag of words, latent Dirichlet allocation, spatial extensions to BoW.


APPENDICES

A. Notation. B. Optimization. C. Linear algebra.

Each part contains illustrative applications including background
subtraction, skin detection, body pose regression, face detection,
pedestrian detection, segmentation, semantic segmentation, stereo vision,
image denoising, image synthesis, shape from silhouette, structured light,
augmented reality tracking, creating visual panoramas, multi-view
reconstruction, 3D morphable models, face recognition, tracking objects in
2D and 3D, SLAM, and object recognition.



--

Dr. Simon J.D. Prince
Department of Computer Science
University College London
Gower Street
London, WC1E 6BT
Tel. 020 7679 3692
Fax. 020 7387 1397

http://pvl.cs.ucl.ac.uk
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