DIGITAL IMAGE PROCESSING 4th phần 2 docx

[...]... Exercises 21 .1 21 .2 21.3 21 .4 21 .5 21 .6 21 .7 21 .8 21 .9 21 .10 21 .11 21 . 12 21.13 21 .14 21 .15 21 .16 21 .17 21 .18 21 .19 715 Program Generation Exercises, 716 Image Manipulation Exercises, 717 Color Space Exercises, 718 Region-of-Interest Exercises, 720 Image Measurement Exercises, 721 Quantization Exercises, 722 Convolution Exercises, 723 Unitary Transform Exercises, 724 Linear Processing Exercises, 725 Image Enhancement... 623 CONTENTS 19 Image Detection and Registration 19.1 19 .2 19.3 19.4 651 Template Matching, 651 Matched Filtering of Continuous Images, 655 Matched Filtering of Discrete Images, 6 62 Image Registration, 664 PART 6 20 xi IMAGE PROCESSING SOFTWARE PIKS Image Processing Software 679 681 20 .1 PIKS Functional Overview, 681 20 .2 PIKS Scientific Overview, 704 21 PIKS Image Processing Programming Exercises 21 .1... Processing Exercises, 725 Image Enhancement Exercises, 726 Image Restoration Models Exercises, 728 Image Restoration Exercises, 729 Geometrical Image Modification Exercises, 729 Morphological Image Processing Exercises, 730 Edge Detection Exercises, 7 32 Image Feature Extraction Exercises, 733 Image Segmentation Exercises, 734 Shape Analysis Exercises, 735 Image Detection and Registration Exercises, 735 Appendix... standard library of image processing operators and associated utilities.” Again, for a fourth time, a new edition of Digital Image Processing is offered to the image processing community Why? One reason is because advances in the theoretical aspects of image processing technology continue at a rapid rate For example, in the year 20 05, the IEEE Transactions on Image Processing published 21 91 pages of research... φ + d y sin φ ∂z (l .2- 15) The gradient magnitude is then ∇{ F ( x, y ) } = 2 2 (l .2- 16) dx + dy Spatial second derivatives in the horizontal and vertical directions are defined as 2 d xx = ∂ F ( x, y ) -2 ∂x (l .2- 17a) 2 ∂ F ( x, y ) d yy = -2 ∂y (l .2- 17b) The sum of these two spatial derivatives is called the Laplacian operator: 2 2 ∂ F ( x, y ) ∂ F ( x, y ) 2{ F ( x, y ) } = ... 555 Image Segmentation 17.1 17 .2 17.3 17.4 17.5 17.6 421 Edge, Line and Spot Models, 465 First-Order Derivative Edge Detection, 471 Second-Order Derivative Edge Detection, 4 92 Edge-Fitting Edge Detection, 506 Luminance Edge Detector Performance, 508 Color Edge Detection, 522 Line and Spot Detection, 529 Image Feature Extraction 16.1 16 .2 16.3 16.4 16.5 16.6 419 Binary Image Connectivity, 421 Binary Image. .. an image The probability density of the luminance function must be a one-sided density because the luminance measure is positive Models that have found application include the Rayleigh density, ⎧ [ F ( x, y, t ) ] 2 ⎫ F ( x, y, t ) p { F ; x, y, t } = - exp ⎨ – ⎬ 2 2 ⎩ ⎭ α 2 (1.4-2a) the log-normal density, 2 2 p { F ; x, y, t} = [ 2 F ( x, y, t )σ F ( x, y, t ) ] –1 ⁄ 2 2... or Miss Transformations, 424 Binary Image Shrinking, Thinning, Skeletonizing and Thickening, 431 Binary Image Generalized Dilation and Erosion, 4 42 Binary Image Close and Open Operations, 453 Gray Scale Image Morphological Operations, 455 Edge Detection 15.1 15 .2 15.3 15.4 15.5 15.6 15.7 16 IMAGE ANALYSIS Topological Attributes, 623 Distance, Perimeter and Area Measurements, 625 Spatial Moments, 631... (1.3 -22 ) Performing the indicated Fourier transformation gives G ( ω x, ω y ) = H ( ω x, ω y )F ( ω x, ω y ) (1.3 -23 ) Then an inverse transformation of Eq 1.3 -23 provides the output image function 1 ∞ ∞ G ( x, y ) = ∫ ∫ H ( ω x, ω y )F ( ω x, ω y ) exp { i ( ω x x + ω y y ) } dω x dω y 2 4π –∞ –∞ (1.3 -24 ) Equations 1.3 -20 and 1.3 -24 represent two alternative means of determining the output image. .. fourth edition W K P PART 1 CONTINUOUS IMAGE CHARACTERIZATION Although this book is concerned primarily with digital, as opposed to analog, image processing techniques It should be remembered that most digital images represent continuous natural images Exceptions are artificial digital images such as test patterns that are numerically created in the computer and images constructed by tomographic systems . 728 21 . 12 Image Restoration Exercises, 729 21 .13 Geometrical Image Modification Exercises, 729 21 .14 Morphological Image Processing Exercises, 730 21 .15 Edge Detection Exercises, 7 32 21.16 Image. Quantization Exercises, 722 21 .7 Convolution Exercises, 723 21 .8 Unitary Transform Exercises, 724 21 .9 Linear Processing Exercises, 725 21 .10 Image Enhancement Exercises, 726 21 .11 Image Restoration. 6 62 19.4 Image Registration, 664 PART 6 IMAGE PROCESSING SOFTWARE 679 20 PIKS Image Processing Software 681 20 .1 PIKS Functional Overview, 681 20 .2 PIKS Scientific Overview, 704 21 PIKS Image Processing