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Pattern recognition
- Astronomical
Image and Data Analysis
by
Jean-Luc Starck & Fionn Murtagh (2nd ed, 2006). Survey of modern
methods of image, signal and data processing with emphasis on
astronomical applications. Covers filtering, deconvolution, image
compression, multichannel data, catalog analysis, data storage &
retrieval, multiresolution methods (wavelet & a trous transform).
- Pattern
Classification
by Trevor Hastie, Robert Tibishrani & Jerome Friedman (2001).
Well-respected text on computational methods for image and signal
analysis with MATLAB software. Includes maximum likelihood and Bayesian
estimation (PCA, Gibbs algorithm, EM algorithm, hidden Markov models),
nonparametric techniques (density estimation, nearest neighbor),
multilayer neural networks, annealing & genetic algorithms, tree
methods, machine learning (minimum description length, bootstrap,
boosting), unsupervised clustering (k-means, Bayesian classfiers).
- Image
Processing and Data Analysis: The Multiscale Approach
by
Jean-Luc Starck, Fionn Murtagh and Albert Bijaoui (1988). Presentation
of wavelet methods for image analysis with emphasis on astronomical
applications. Covers continuous and discrete wavelet transforms, median
transforms, multiresolution support and filtering, deconvolution
(including Lucy-Richardson algorithm, maximum entropy, and CLEAN),
multiresolution regression and forecasting, geometric registration,
disparity mapping (including kriging), image compression, source
detection, and multiscale vision models.
- Pattern
Recognition and Neural Networks
by B. D. Ripley (1996). Readable monograph on issues arising in
multivariate and image analysis, machine learning and computer vision.
Covers statistical decision theory, linear discriminant analysis,
feed-forward neural networks, non-parametric methods, tree-based
classifiers, belief networks, and unsupervised methods.
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