A Computational Introduction to Digital Image ProcessingCRC Press, 28 oct. 2015 - 551 pages Highly Regarded, Accessible Approach to Image Processing Using Open-Source and Commercial SoftwareA Computational Introduction to Digital Image Processing, Second Edition explores the nature and use of digital images and shows how they can be obtained, stored, and displayed. Taking a strictly elementary perspective, the book only covers topics that |
Table des matières
Chapter 1 Introduction | 1 |
Chapter 2 Images Files and File Types | 19 |
Chapter 3 Image Display | 43 |
Chapter 4 Point Processing | 67 |
Chapter 5 Neighborhood Processing | 85 |
Chapter 6 Image Geometry | 125 |
Chapter 7 The Fourier Transform | 149 |
Chapter 8 Image Restoration | 193 |
Chapter 13 Color Processing | 371 |
Chapter 14 Image Coding and Compression | 403 |
Chapter 15 Wavelets | 431 |
Chapter 16 Special Effects | 459 |
Appendix A Introduction to MATLAB and Octave | 479 |
Appendix B Introduction to Python | 505 |
Appendix C The Fast Fourier Transform | 517 |
| 525 | |
Chapter 9 Image Segmentation | 223 |
Chapter 10 Mathematical Morphology | 271 |
Chapter 11 Image Topology | 309 |
Chapter 12 Shapes and Boundaries | 351 |
Color Insert | 529 |
Back Cover | 537 |
Autres éditions - Tout afficher
A Computational Introduction to Digital Image Processing ALASDAIR. MCANDREW Aucun aperçu disponible - 2021 |
A Computational Introduction to Digital Image Processing Alasdair McAndrew Aucun aperçu disponible - 2016 |
Expressions et termes fréquents
8-connected algorithm apply array averaging filter bilinear interpolation binary image blur buffer bytes cameraman image chain code Chapter color image color map columns commands component compression compute create DC coefficient defined deleted dilation display distance transform edge detection erosion example foreground pixel Fourier Transform function Gaussian filter Gaussian noise gray level gray values grayscale image Haar wavelet high pass filter histogram Huffman code image processing implemented indexed integer interpolation inverse JPEG label linear lookup table low pass filter MATLAB and Octave MATLAB or Octave MATLAB/Octave MATLAB/Octave MATLAB/Octave Python median filter method morphology multiplying nearest neighbor interpolation neighborhood Note numpy object obtained operations original image output parameter pepper noise pixel pixel values produce Python Python quantization Radon transform result is shown retinex rotated shown in Figure simple skeleton spatial filtering square structuring element Suppose threshold type double vector wavelet transform zero