DIGITAL IMAGE PROCESSING 4th phần 3 doc

... T 1 P ˜ 3 () T 2 P ˜ 1 ()T 2 C() T 2 P ˜ 3 () T 3 P ˜ 1 ()T 3 C() T 3 P ˜ 3 () T 1 P ˜ 1 ()T 1 W ˜ ()T 1 P ˜ 3 () T 2 P ˜ 1 ()T 2 W ˜ ()T 2 P ˜ 3 () T 3 P ˜ 1 ()T 3 W ˜ ()T 3 P ˜ 3 () = T ˜ 3 C() T 1 P ˜ 1 ()T 1 P ˜ 2 ()T 1 C() T 2 P ˜ 1 ()T 2 P ˜ 2 ()T 2 C() T 3 P ˜ 1 ()T 3 P ˜ 2 ()T 3 C() T 1 P ˜ 1 ()T 1 P ˜ 2 ()T 1 W ˜ () T 2 P ˜ 1 ()T 2 P ˜ 2 ()T 2 W ˜ ()...

Ngày tải lên: 14/08/2014, 01:22

... 729 21. 13 Geometrical Image Modification Exercises, 729 21.14 Morphological Image Processing Exercises, 730 21.15 Edge Detection Exercises, 732 21.16 Image Feature Extraction Exercises, 733 21.17 Image ... Properties 23 2.1 Light Perception, 23 2.2 Eye Physiology, 26 2 .3 Visual Phenomena, 29 2.4 Monochrome Vision Model, 33 2.5 Color Vision Model, 39 3 Photometry and Co...

Ngày tải lên: 14/08/2014, 01:22

... 9. ( b ) 11 21 31 0 0 11 21 31 31 0 11 21 21 31 0 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 23 33 0 0 13 23 33 33 0 13 23 23 33 0 13 12 22 32 0 0 12 22 32 32 0 12 22 22 32 0 12 12 22 32 0 0 12 22 32 32 0 12 22 22 32 0 12 11 21 31 0 0 11 21 31 31 0 11 21 21 31 0 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 23 33 0 0 13 23 33 33 0 13 23 23 33 0 13...

Ngày tải lên: 14/08/2014, 02:20

... gg min 2α 2 1 1 P f f()– ⎩⎭ ⎨⎬ ⎧⎫ ln 12⁄ += p g g() 1 3 g 2 3 g max 13 g min 13 – = gg max 13 g min 13 – P f f()[]g max 13 + 3 = p g g() 1 gg max {}ln g min {}ln–[] = gg min g max g min ... is defined as (10 .3- 3) Gjk,() Fjk,() LL× Hjk,() Gjk,() Fmn,()Hm j Cn k C++,++() ∑∑ = Gjk,() L 1–()2⁄ 33 × H 1 9 111 111 111 = H 1 10 111 121 111 = H 1 16 121 242 121 = 33 ×...

Ngày tải lên: 14/08/2014, 02:20

... order as ( 13. 1-17) where for translation ( 13. 1-18a) ( 13. 1-18b) ( 13. 1-18c) ( 13. 1-18d) ( 13. 1-18e) ( 13. 1-18f) and for scaling ( 13. 1-19a) ( 13. 1-19b) ( 13. 1-19c) ( 13. 1-19d) ( 13. 1-19e) ( 13. 1-19f) and ... entries contain Y = X. 33 × 33 × 000 010 000 2 9 512= Gjk,() Gjk,() XX 0 X 1 … X 7 ∪∪∪()∩= ∩∪ 33 × 2 8– 2 7– 2 6– 2 1– 2 0 2 5– 2 2– 2 3 2 4– 400 GEOMETRICAL IMAGE...

Ngày tải lên: 14/08/2014, 02:20

... morphological image processing techniques. FIGURE 15 .3- 3. Cross section of continuous domain Laplacian of Gaussian impulse response. s 1 s 2 < s 2 s 1 ⁄ 1.6= WW× c 22s= 11 11× 33 × 33 × 498 EDGE ... slightly higher. FIGURE 15.5-4. Edge models for edge localization analysis. 33 × 22× 33 × 33 × ( a ) 2 × 2 model ( b ) 3 × 3 model FIRST-ORDER DERIVATIVE EDGE DETECTION 485 The...

Ngày tải lên: 14/08/2014, 02:20

... 6 .33 4.59 Grass – wool 4.17 4. 03 1.87 1.70 4.64 4.59 2.48 2 .31 5.62 4.05 1.87 1.72 Sand – raffia 15.26 15.08 13. 22 12.98 3. 85 3. 76 2.74 2.49 6.75 6.40 5 .39 5. 13 Sand – wool 19.14 19.08 17. 43 ... 14. 43 14 .38 12.72 10.86 18.75 12 .3 10.52 8.29 Raffia – wool 13. 29 13. 14 10 .32 7.96 13. 93 13. 75 10.90 8.47 17.28 11.19 8.24 6.08 Average 11.69 11.57 9.72 8.42 8.98 8.89 7.20 5....

Ngày tải lên: 14/08/2014, 02:20

... Computer Processing of ERTS Images,” Report USCIPI 640, University of Southern California, Image Processing Institute, Los Angeles, September 1975. CONTINUOUS IMAGE SPATIAL FILTERING RESTORATION 34 1 If ... density, and follows the exponential law of absorption of Eq. 11 .3- 2. Thus, from Eqs. 11 .3- 3 and 11 .3- 4, one obtains directly (11 .3- 5) (11 .3- 6) where d x is an appropri...

Ngày tải lên: 14/08/2014, 02:20

... 1,028 .31 1,104 .36 1,2 13. 73 Rotated spade 8,215.99 4,186 .39 3, 968 .30 2,149 .35 2,021.65 1,949.89 1,111.69 1, 038 .04 9 93. 20 9 73. 53 Heart 8,616.79 4,2 83. 65 4 ,34 1 .36 2,145.90 2,158.40 2,2 23. 79 1,0 83. 06 ... –0.017 0 .36 3 Rotated spade 0.510 0.4 83 16.207 –0 .36 6 33 .215 –0.0 13 0.284 –0.002 –0 .35 7 Heart 0.497 0.504 16 .38 0 0.194 36 .506 –0.012 0 .37 1 0.027 –0. 831...

Ngày tải lên: 14/08/2014, 02:20

... b a+c 3. b a /b c = b a − c . 220 For example, we have the following: • 256 = 16 2 = (2 4 ) 2 = 2 4.2 = 2 8 = 256 (Exponent Rule 1) • 2 43 = 3 5 = 3 2 +3 = 3 2 3 3 = 9 · 27 = 2 43 (Exponent ... 4.11: 20n 3 + 10n log n + 5 is O(n 3 ). Justification: 20n 3 + 10n log n + 5 ≤ 35 n 3 , for n ≥ 1. Example 4.12: 3log n + 2 is O(log n). Justification: 3 log n...

Ngày tải lên: 14/08/2014, 01:21