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– Euclidean distance
– City-block distance
– Chessboard distance
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digital image processing chapter two, fundamentals
- 1. Digital Image Processing Chapter 2: Digital Image Fundamentals
- 2. Elements of Visual Perception • Structure of the human eye
- 3. • Rods and cones in the retina
- 4. • Image formation in the eye
- 5. • Brightness adaptation and discrimination
- 11. Light and the Electromagnetic Spectrum
- 13. Image Sensing and Acquisition
- 14. • Image acquisition using a single sensor
- 15. • Using sensor strips
- 16. A simple image formation model
- 17. • Illumination and reflectance • Illumination and transmissivity ) , ( ) , ( ) , ( y x r y x i y x f
- 18. Image Sampling and Quantization
- 20. • Representing digital images
- 22. • Saturation and noise
- 23. • Number of storage bits
- 24. • Spatial and gray-level resolution
- 29. • N and k in different-details images
- 30. • Isopreference
- 31. • Interpolations
- 33. Some Basic Relationships Between Pixels • Neighbors of a pixel – : 4-neighbors of p , , , : four diagonal neighbors of p , , , : 8-neighbors of p and ) ( 4 p N ) , 1 ( y x ) 1 , ( y x ) , 1 ( y x ) 1 , ( y x ) 1 , 1 ( y x ) 1 , 1 ( y x ) 1 , 1 ( y x ) 1 , 1 ( y x ) (p ND ) ( 8 p N ) ( 4 p N ) (p ND
- 34. • Adjacency – : The set of gray-level values used to define adjacency – 4-adjacency: Two pixels p and q with values from V are 4-adjacency if q is in the set – 8-adjacency: Two pixels p and q with values from V are 8-adjacency if q is in the set V ) ( 4 p N ) ( 8 p N
- 35. – m-adjacency (mixed adjacency): Two pixels p and q with values from V are m-adjacency if • q is in , or • q is in and the set has no pixels whose values are from V ) ( 4 p N ) (p ND ) ( ) ( 4 4 q N p N
- 37. • Subset adjacency – S1 and S2 are adjacent if some pixel in S1 is adjacent to some pixel in S2 • Path – A path from p with coordinates to pixel q with coordinates is a sequence of distinct pixels with coordinates – , ,…, where = , = , and pixels and are adjacent ) , ( y x ) , ( t s ) , ( 0 0 y x ) , ( 1 1 y x ) , ( n n y x ) , ( 0 0 y x ) , ( y x ) , ( n n y x ) , ( t s ) , ( i i y x ) , ( 1 1 i i y x
- 38. • Region – We call R a region of the image if R is a connected set • Boundary – The boundary of a region R is the set of pixels in the region that have one or more neighbors that are not in R • Edge – Pixels with derivative values that exceed a preset threshold
- 39. • Distance measures – Euclidean distance – City-block distance – Chessboard distance 2 1 2 2 ] ) ( ) [( ) , ( t y s x q p De | ) ( | | ) ( | ) , ( 4 t y s x q p D |) ) ( | |, ) ( max(| ) , ( 8 t y s x q p D
- 40. m D distance: The shortest m-path between the points
- 41. An Introduction to the Mathematical Tools Used in Digital Image Processing • Linear operation – H is said to be a linear operator if, for any two images f and g and any two scalars a and b, ) ( ) ( ) ( g bH f aH bg af H
- 47. • Set operations
- 48. • Complements
- 54. • Registration