Pattern Theory: The Stochastic Analysis of Real-World SignalsPattern theory is a distinctive approach to the analysis of all forms of real-world signals. At its core is the design of a large variety of probabilistic models whose samples reproduce the look and feel of the real signals, their patterns, and their variability. Bayesian statistical inference then allows you to apply these models in the analysis o |
Contents
| 1 | |
1 English Text and Markov Chains | 17 |
2 Music and Piecewise Gaussian Models | 61 |
3 Character Recognition and Syntactic Grouping | 111 |
4 Image Texture Segmentation and Gibbs Models | 173 |
5 Faces and Flexible Templates | 249 |
6 Natural Scenes and Multiscale Analysis | 317 |
Bibliography | 387 |
Back Cover | 409 |
Other editions - View all
Pattern Theory: The Stochastic Analysis of Real-World Signals David Mumford,Agnès Desolneux No preview available - 2010 |
Common terms and phrases
algorithm approximation assume basic bits boundary Brownian motion called Chapter color compute constant contours curve defined denote density diffeomorphisms differential differential entropy discrete disk edges encode energy entropy equation example exponential face filter finite Fourier transform frequency function Gaussian distribution Gaussian model geodesics Gibbs given gradient graph histogram idea independent inner product integral Ising model kurtosis labels length level lines linear log2 Markov chain MATLAB matrix mean medial axis method metric n-gram Note objects parse parse graph pattern theory pixels points Poisson Poisson process probability distribution probability measure problem pyramid random variable result sample scale scale-invariant Section segmentation sequence shown in Figure shows signal simple smooth statistics stochastic model strings subset tangent term texture Theorem tree values variance vertices warping wavelet white noise words