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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.
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