CIS 601 Image Fundamentals Dr. Rolf Lakaemper

Parts of these slides base on the textbook Digital Image Processing Fundamentals Parts of these slides base on the textbook Digital Image Processing by Gonzales/Woods Chapters 1 / 2

basic concepts about digital images Fundamentals These slides show basic concepts about digital images

Let’s have a look at the human eye Fundamentals Let’s have a look at the human eye

Fundamentals

consists of cones and rods Fundamentals We are mostly interested in the retina: consists of cones and rods Cones color receptors About 7 million, primarily in the retina’s central portion for image details Rods Sensitive to illumination, not involved in color vision About 130 million, all over the retina General, overall view

Maps our 3D reality to a 2 dimensional image ! Fundamentals The human eye Is able to perceive electromagnetic waves in a certain spectrum Is able to distinguish between wavelengths in this spectrum (colors) Has a higher density of receptors in the center Maps our 3D reality to a 2 dimensional image !

maps our continous (?) reality to a (spatially) DISCRETE 2D image Fundamentals …or more precise: maps our continous (?) reality to a (spatially) DISCRETE 2D image

Some topics we have to deal with: Sharpness Brightness Fundamentals Some topics we have to deal with: Sharpness Brightness Processing of perceived visual information

The eye is able to deal with sharpness in different distances Fundamentals Sharpness The eye is able to deal with sharpness in different distances

The eye is able to adapt to different ranges of brightness Fundamentals Brightness The eye is able to adapt to different ranges of brightness

Processing of perceived information: optical illusions Fundamentals Processing of perceived information: optical illusions

Fundamentals optical illusions: Digital Image Processing does NOT (primarily) deal with cognitive aspects of the perceived image !

Fundamentals What is an image ?

Fundamentals The retinal model is mathematically hard to handle (e.g. neighborhood ?)

Easier: 2D array of cells, modelling the cones/rods Fundamentals Easier: 2D array of cells, modelling the cones/rods Each cell contains a numerical value (e.g. between 0-255)

The position of each cell defines the position of the receptor Fundamentals The position of each cell defines the position of the receptor The numerical value of the cell represents the illumination received by the receptor 5 7 1 12 4 … … …

With this model, we can create GRAYVALUE images Fundamentals With this model, we can create GRAYVALUE images Value = 0: BLACK (no illumination / energy) Value = 255: White (max. illumination / energy)

A 2D grayvalue - image is a 2D -> 1D function, Fundamentals A 2D grayvalue - image is a 2D -> 1D function, v = f(x,y)

As we have a function, we can apply operators to this function, e.g. Fundamentals As we have a function, we can apply operators to this function, e.g. H(f(x,y)) = f(x,y) / 2 Operator Image (= function !)

H(f(x,y)) = f(x,y) / 2 Fundamentals 6 8 2 3 4 1 12 200 20 10 6 100 10 3 4 1 12 200 20 10 6 100 10 5

Remember: the value of the cells is the illumination (or brightness) Fundamentals Remember: the value of the cells is the illumination (or brightness) 6 8 2 3 4 1 12 200 20 10 6 100 10 5

Image Acquisition and Representation Fundamentals The mandatory steps: Image Acquisition and Representation

Fundamentals Acquisition

Fundamentals Acquisition

Typical sensor for images: CCD Array (Charge Couple Devices) Fundamentals Typical sensor for images: CCD Array (Charge Couple Devices) Use in digital cameras Typical resolution 1024 x 768 (webcam)

Fundamentals CCD

Fundamentals CCD

Fundamentals CCD (3.2 million pixels)

Fundamentals Representation The Braun Tube

Fundamentals Representation Black/White and Color

Color Representation: Red / Green / Blue Fundamentals Color Representation: Red / Green / Blue Model for Color-tube Note: RGB is not the ONLY color-model, in fact its use is quiet restricted. More about that later.

Color images can be represented by Fundamentals Color images can be represented by 3D Arrays (e.g. 320 x 240 x 3)

But for the time being we’ll handle 2D grayvalue images Fundamentals But for the time being we’ll handle 2D grayvalue images

Digital vs. Analogue Images Fundamentals Digital vs. Analogue Images Analogue: Function v = f(x,y): v,x,y are REAL Digital: Function v = f(x,y): v,x,y are INTEGER

Stepping down from REALity to INTEGER coordinates x,y: Sampling Fundamentals Stepping down from REALity to INTEGER coordinates x,y: Sampling

Stepping down from REALity to INTEGER grayvalues v : Quantization Fundamentals Stepping down from REALity to INTEGER grayvalues v : Quantization

Sampling and Quantization Fundamentals Sampling and Quantization

MATLAB demonstrations of sampling and quantization effects Fundamentals MATLAB demonstrations of sampling and quantization effects