Digital Image Fundamentals Human Vision Lights and Electromagnetic spectrum Image Sensing & Acquisition Sampling & Quantization Basic Relationships b/w.

Digital Image Fundamentals

Human Vision

Lights and Electromagnetic spectrum

Image Sensing & Acquisition

Sampling & Quantization

Basic Relationships b/w Pixels

Digital Image Processing 2

Important dates 9/29: Project grouping (2~3 members/group) 10/6: First image processing GUI due!!

OpenCV ImageMagick ImageJ Ximage or ImageX

Digital Image Processing 3

A Cross Section of the Human Eye

• Iris – 虹膜• Lens – 水晶體• Cornea – 角膜• Sclera – 鞏膜• Choroid – 脈絡膜

• Retina – 視網膜• Fovea – 視乳頭• Ciliary body – 睫狀體

Digital Image Processing 4

Human Vision

Rods: 108

Shape/form perception Large dynamic range Limited contrast Scotopic (dim-light) vision

Cones 5 X 106

3-channel color perception Photopic (bright-light) vision

Digital Image Processing 5

Distribution of Rods and Cones in the Retina

Digital Image Processing 6

Image Formation in the Eye

Digital Image Processing 7

Range of Subjective Brightness

• Visual system cannot operate over full range of subjective brightness simultaneous

• Via brightness adaptation

Digital Image Processing 8

Brightness Discrimination

Weber ratio / intensity

• ∆Ic – increment of illumination

discriminable 50% of the time I

• Small ∆Ic/I => good discrimination; otherwise, poor.

Digital Image Processing 9

Perceived Brightness

Two phenomena

• Undershoot or overshoot around the boundaries; Mach band pattern

• Simultaneous contrast

Digital Image Processing 10

Optical Illusions

Digital Image Processing 11

Electromagnetic Spectrum

Digital Image Processing 12

Image Sensing

• Single sensor

• Sensor strip

• Sensor array

Digital Image Processing 13

A Simple Image Model

i(x,y) – illumination (from light source) r(x,y) – reflectance of illuminated

surface (reflectivity)

Lambertian surface Looks the same in all directions

Specular (mirror-like) surface Incidence angle = reflectance angle

Digital Image Processing 14

A Simple Image Model (continued)

f(x,y) = i(x,y) X r(x,y) >= 0

r(x,y) 0.93 white snow 0.01 black velvet

i(x,y) 9000 foot-candle Sun 0.01 foot-candle full moon

Digital Image Processing 15

Sampling & Quantization

Digital Image Processing 16

A Digital Image of MXN Array

)1,1()1,1()0,1(

)0,2(

)1,1(

)1,0()2,0(

)1,1()0,1(

)1,0()0,0(

),(

NMfMfMf

Mf

Nf

Nff

ff

ff

yxf

Digital Image Processing 17

A Digital Image (continued)

• Image Sampling – Spatial-coordinate digitization

• Gray-level Quantization – amplitude digitization

N = size of image = (number of columns) X (number of rows)

G (number of gray levels) = 2k

Disk storage needed = N * ceiling(k/8)

Digital Image Processing 18

Storage Bits for N and k

Digital Image Processing 19

Spatial Resolution

Digital Image Processing 20

Amplitude Quantization

Digital Image Processing 21

Level of Detail (LOD)

Low level of detail High level of detail

Digital Image Processing 22

Isopreference

Digital Image Processing 23

Scaling and Interpolation

Digital Image Processing 24

Basic Image Topology

Neighbors of a Pixel 4-neighbor and 8-neighbor 4-adjacent and 8-adjacent

Connectivity 4-connectivity 8-connectivity M-connectivity (mixed connectivity)

Digital Image Processing 25

M-Connectivity

})()({)( (ii)

or ),( (i)

:set foregroundrt adjacent w are and

44

4

VqNpNpNq

pNq

Vm-qp

D

Digital Image Processing 26

Further Pixel Relationships

Connected Component Labeling Relations, Equivalence, and Transitive

Closure Distance Measures Arithmetic/Logic Operations Mask Operations

Digital Image Processing 27

Logic Mask Operations

Digital Image Processing 28

Weighted Mask Operation

Digital Image Processing 29

Utilizing ALU Parallel Processing