Digital Image Processing

The app is a complete free handbook of Digital Image Processing which covers important all topics with detailed notes, diagrams, equations, formulas & course material. The App is designed for quick learning, revisions, references at the time of exams and interviews. This Engineering eBook cover most of related topics and Detailed explanation with all the basics topics. Some of topics Covered in this Digital Image Processing application are: 1. Global properties using B-rep model 2. Compression fundamentals 3. Lossless data compression 4. Image restoration 5. Linear position invariant degradation models 6. Elements of visual perception 7. Color perception 8. Image sampling and quantization 9. Basic relationship between pixels 10. Basic geometric transformations 11. Introduction to Fourier transform and DFT 12. Fast Fourier transform 13. Properties of two dimensional Fourier transform 14. Separable image transforms 15. Walsh, Hadamard, discrete cosine, Haar and Slant transforms 16. Spatial domain methods 17. Grey scale manipulation and histogram equalization 18. Image subtraction and Image averaging 19. Spatial filtering on images 20. Smoothing filters on images 21. Sharpening filters on images 22. Derivative filters on images 23. Laplacian filters on images 24. Frequency domain 25. Homomorphic filtering 26. Image restoration process 27. Model of image degradation 28. Noise models 29. Characteristics of the noise models 30. Order-Statistics Filters 31. Adaptive filters - Digital Image Processing 32. Least-Mean-Square (LMS) Algorithm 33. Blind Image Restoration and Recognition 34. Pseudo-Inverse Filtering 35. Singular value decomposition 36. Lossless compression 37. Lossy compression 38. Lempel-Ziv-Welch (LZW) Compression 39. Lempel-Ziv-Welch (LZW) decompression 40. Bit plane coding 41. Differential Pulse Code Modulation (DPCM) 42. Image representation 43. Transform Coding 44. Wavelet Image Coding 45. JPEG Compression 46. MPEG Compression 47. Basics of Vector quantization 48. Image Segmentation 49. Edge Detection 50. Image thresholding 51. Region based segmentation 52. Boundary Representation Models 53. Data structure of boundary representation models 54. Vertex-based boundary model 55. Face-based boundary models 56. Graph representation 57. Validity of boundary models 58. Faceted modelers for some B-rep systems 59. Two-and-half dimensional drawing 60. Advantage and disadvantage of boundary representation 61. Polygonal approximation 62. Algorithm for closed contours 63. Solution for closed contours 64. Heuristic algorithms 65. Multiple-object polygonal approximation 66. Optimal algorithms for min-# problem 67. Iterative reduced search 68. Boundary Descriptors 69. Radial Distance Measures 70. Linear Discriminant Analysis 71. Reduced search algorithm 72. The Hough transform 73. Edge detection techniques 74. Laplacian of Gaussian 75. Canny - Edge Detection Algorithm 76. Fundamentals of color 77. The RGB Color Model 78. Additive vs. Subtractive Color Models 79. Interactive Specification of Color 80. Colour Image Processing 81. CIE primaries 82. The HSI Model 83. The YIQ Model 84. Transferring Color to Greyscale Images 85. Basics of Full-Colour Image Processing 86. Colour Slicing 87. Tone and Colour Corrections 88. Intensity slicing 89. Intensity to color transformation 90. Morphological Image Processing 91. Dilating an Image 92. Structuring Elements 93. Structuring Element Decomposition All topics are not listed because of character limitations. Each topic is complete with diagrams, equations and other forms of graphical representations for better learning and quick understanding. This app will useful for quick reference. The revision of all concepts can be finished within Several hour using this app. Digital Image Processing is part of engineering education courses and technology degree programs of various universities.