Pattern Recognition and Image PreprocessingSing T. Bow Describing non-parametric and parametric theoretic classification and the training of discriminant functions, this second edition includes new and expanded sections on neural networks, Fisher's discriminant, wavelet transform, and the method of principal components. It contains discussions on dimensionality reduction and feature selection; novel computer system architectures; proven algorithms for solutions to common roadblocks in data processing; computing models including the Hamming net, the Kohonen self-organizing map, and the Hopfield net; detailed appendices with data sets illustrating key concepts in the text; and more. |
Contents
LXXIV | 299 |
LXXV | 329 |
LXXVI | 332 |
LXXVII | 339 |
LXXVIII | 348 |
LXXIX | 353 |
LXXX | 359 |
LXXXII | 370 |
34 | |
XIII | 38 |
XIV | 45 |
XV | 48 |
XVI | 53 |
XVII | 55 |
XVIII | 58 |
XX | 62 |
XXI | 68 |
XXII | 70 |
XXIII | 72 |
XXV | 75 |
XXVI | 78 |
XXVIII | 79 |
XXIX | 89 |
XXX | 98 |
XXXI | 108 |
XXXII | 113 |
XXXIII | 125 |
XXXIV | 141 |
XXXV | 142 |
XXXVI | 157 |
XXXVII | 160 |
XXXIX | 164 |
XLI | 166 |
XLII | 168 |
XLIII | 178 |
XLIV | 184 |
XLVI | 186 |
XLVII | 193 |
XLVIII | 197 |
L | 201 |
LI | 215 |
LII | 219 |
LIII | 221 |
LIV | 227 |
LV | 228 |
LVII | 230 |
LVIII | 231 |
LIX | 232 |
LXII | 242 |
LXIII | 249 |
LXIV | 252 |
LXVI | 254 |
LXVII | 257 |
LXVIII | 263 |
LXIX | 265 |
LXXI | 267 |
LXXIII | 294 |
LXXXIII | 373 |
LXXXIV | 374 |
LXXXV | 376 |
LXXXVI | 380 |
LXXXVII | 381 |
LXXXVIII | 388 |
LXXXIX | 390 |
XCI | 395 |
XCII | 397 |
XCIV | 399 |
XCV | 402 |
XCVII | 416 |
XCVIII | 433 |
XCIX | 450 |
C | 464 |
CI | 472 |
CII | 477 |
CV | 480 |
CVI | 482 |
CVII | 486 |
CVIII | 492 |
CIX | 505 |
CXI | 507 |
CXIII | 509 |
CXIV | 525 |
CXV | 541 |
CXVI | 547 |
CXVII | 557 |
CXX | 559 |
CXXII | 569 |
CXXIII | 575 |
CXXIV | 577 |
CXXV | 580 |
CXXVI | 582 |
CXXVII | 583 |
CXXVIII | 586 |
CXXIX | 587 |
CXXX | 591 |
CXXXI | 595 |
CXXXII | 608 |
CXXXIV | 608 |
CXXXV | 609 |
CXXXVI | 611 |
CXXXVII | 615 |
CXXXVIII | 619 |
CXXXIX | 639 |
685 | |
Other editions - View all
Common terms and phrases
algorithm array axis basis functions boundary C₁ cluster centers coefficients component computation convolution curve data set decision surface defined density function detection digitized image discrete discrete Fourier transform discrete wavelet transform discriminant function discussed distance error example feature filter Fourier spectrum Fourier transform gradient graphics gray levels gray-level Hadamard transform hidden layer high-pass filter histogram hyperplane image function image processing input vector inverse iteration line segments linear m₁ Mahalanobis distance matrix Maxnet method multilayer perceptron N₁ N₂ neighbors neuron node object obtained operator Original image output layer parameters pattern classification pattern points pattern recognition pattern samples pattern space pattern vector pixel problem processed image prototypes quadtree region represent respectively scaling function shown in Figure signal spatial subclusters textural threshold total number two-dimensional unsupervised learning variance w₁ w₂ Walsh transform wavelet transform weight vector x₁ z₁
Popular passages
Page 669 - A simple approach for the estimation of circular arc center and its radius," Computer Vision, Graphics and Image Processing, Vol.