| Preface |
|
ix | |
|
|
|
1 | (30) |
|
|
|
1 | (1) |
|
Human and computer vision |
|
|
1 | (2) |
|
|
|
3 | (7) |
|
|
|
4 | (3) |
|
|
|
7 | (1) |
|
|
|
8 | (2) |
|
|
|
10 | (5) |
|
|
|
10 | (3) |
|
|
|
13 | (1) |
|
|
|
14 | (1) |
|
|
|
15 | (9) |
|
|
|
15 | (1) |
|
Hello Mathcad, hello images! |
|
|
16 | (5) |
|
|
|
21 | (3) |
|
|
|
24 | (4) |
|
|
|
24 | (1) |
|
|
|
25 | (2) |
|
|
|
27 | (1) |
|
|
|
28 | (3) |
|
Images, sampling and frequency domain processing |
|
|
31 | (36) |
|
|
|
31 | (1) |
|
|
|
31 | (4) |
|
|
|
35 | (5) |
|
|
|
40 | (5) |
|
The discrete Fourier transform (DFT) |
|
|
45 | (8) |
|
One-dimensional transform |
|
|
45 | (2) |
|
Two-dimensional transform |
|
|
47 | (6) |
|
Other properties of the Fourier transform |
|
|
53 | (4) |
|
|
|
53 | (1) |
|
|
|
54 | (1) |
|
|
|
55 | (1) |
|
Superposition (linearity) |
|
|
56 | (1) |
|
Transforms other than Fourier |
|
|
57 | (6) |
|
Discrete cosine transform |
|
|
57 | (1) |
|
Discrete Hartley transform |
|
|
58 | (2) |
|
Introductory wavelets; the Gabor wavelet |
|
|
60 | (2) |
|
|
|
62 | (1) |
|
Applications using frequency domain properties |
|
|
63 | (2) |
|
|
|
65 | (1) |
|
|
|
65 | (2) |
|
Basic image processing operations |
|
|
67 | (32) |
|
|
|
67 | (1) |
|
|
|
67 | (2) |
|
|
|
69 | (10) |
|
|
|
69 | (3) |
|
|
|
72 | (1) |
|
|
|
72 | (4) |
|
|
|
76 | (3) |
|
|
|
79 | (9) |
|
|
|
79 | (3) |
|
|
|
82 | (3) |
|
On different template size |
|
|
85 | (1) |
|
Gaussian averaging operator |
|
|
86 | (2) |
|
Other statistical operators |
|
|
88 | (7) |
|
|
|
88 | (1) |
|
|
|
89 | (3) |
|
|
|
92 | (3) |
|
Comparison of statistical operators |
|
|
95 | (1) |
|
|
|
95 | (1) |
|
|
|
96 | (3) |
|
Low-Level feature extraction (including edge detection) |
|
|
99 | (62) |
|
|
|
99 | (1) |
|
First-order edge detection operators |
|
|
99 | (21) |
|
|
|
99 | (4) |
|
Analysis of the basic operators |
|
|
103 | (2) |
|
Prewitt edge detection operator |
|
|
105 | (1) |
|
Sobel edge detection operator |
|
|
106 | (6) |
|
|
|
112 | (8) |
|
Second-order edge detection operators |
|
|
120 | (7) |
|
|
|
120 | (1) |
|
Basic operators: the Laplacian |
|
|
121 | (2) |
|
The Marr--Hildreth operator |
|
|
123 | (4) |
|
Other edge detection operators |
|
|
127 | (2) |
|
|
|
127 | (1) |
|
|
|
128 | (1) |
|
Comparison of edge detection operators |
|
|
129 | (1) |
|
Detecting image curvature |
|
|
130 | (15) |
|
Computing differences in edge direction |
|
|
132 | (2) |
|
Approximation to a continuous curve |
|
|
134 | (4) |
|
Measuring curvature by changes in intensity |
|
|
138 | (2) |
|
Autocorrelation as a measure of curvature |
|
|
140 | (5) |
|
|
|
145 | (11) |
|
|
|
146 | (3) |
|
|
|
149 | (7) |
|
|
|
156 | (1) |
|
|
|
157 | (4) |
|
Feature extraction by shape matching |
|
|
161 | (56) |
|
|
|
161 | (1) |
|
Thresholding and subtraction |
|
|
162 | (2) |
|
|
|
164 | (9) |
|
|
|
164 | (6) |
|
Fourier transform implementation |
|
|
170 | (3) |
|
Discussion of template matching |
|
|
173 | (1) |
|
|
|
173 | (26) |
|
|
|
173 | (1) |
|
|
|
174 | (5) |
|
|
|
179 | (5) |
|
|
|
184 | (2) |
|
Parameter space decomposition |
|
|
186 | (13) |
|
Generalised Hough transform (GHT) |
|
|
199 | (14) |
|
Formal definition of the GHT |
|
|
199 | (2) |
|
|
|
201 | (1) |
|
|
|
202 | (4) |
|
|
|
206 | (7) |
|
Other extensions to the HT |
|
|
213 | (1) |
|
|
|
214 | (1) |
|
|
|
214 | (3) |
|
Flexible shape extraction (snakes and other techniques) |
|
|
217 | (30) |
|
|
|
217 | (1) |
|
|
|
218 | (2) |
|
|
|
220 | (16) |
|
|
|
220 | (2) |
|
The greedy algorithm for snakes |
|
|
222 | (5) |
|
Complete (Kass) snake implementation |
|
|
227 | (5) |
|
|
|
232 | (1) |
|
Further snake developments |
|
|
233 | (3) |
|
Discrete symmetry operator |
|
|
236 | (4) |
|
|
|
240 | (3) |
|
|
|
243 | (1) |
|
|
|
243 | (4) |
|
|
|
247 | (44) |
|
|
|
247 | (1) |
|
|
|
248 | (30) |
|
|
|
248 | (1) |
|
|
|
249 | (2) |
|
|
|
251 | (27) |
|
|
|
278 | (10) |
|
|
|
278 | (2) |
|
|
|
280 | (8) |
|
|
|
288 | (1) |
|
|
|
288 | (3) |
|
Introduction to texture description, segmentation and classification |
|
|
291 | (20) |
|
|
|
291 | (1) |
|
|
|
292 | (2) |
|
|
|
294 | (7) |
|
|
|
294 | (1) |
|
|
|
294 | (3) |
|
|
|
297 | (2) |
|
|
|
299 | (2) |
|
|
|
301 | (5) |
|
The k-nearest neighbour rule |
|
|
301 | (4) |
|
Other classification approaches |
|
|
305 | (1) |
|
|
|
306 | (1) |
|
|
|
307 | (1) |
|
|
|
308 | (3) |
|
|
|
311 | (34) |
|
Appendix 1: Homogeneous co-ordinate system |
|
|
311 | (3) |
|
|
|
313 | (1) |
|
Appendix 2: Least squares analysis |
|
|
314 | (3) |
|
The least squares criterion |
|
|
314 | (1) |
|
Curve fitting by least squares |
|
|
315 | (2) |
|
Appendix 3: Example Mathcad worksheet for
Chapter 3 |
|
|
317 | (19) |
|
Appendix 4: Abbreviated Matlab worksheet |
|
|
336 | (9) |
| Index |
|
345 | |
Lisainfo e-raamatute kohta