CIS 601 Image Fundamentals Longin Jan Latecki Slides by Dr. Rolf Lakaemper

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

Fundamentals Today we will Learn some basic concepts about digital images (Textbook chapters 1 / 2) Show how MATLAB can help in understanding these concepts Build a simple video – surveillance system using MATLAB !

Fundamentals In the beginning… we’ll have a look at the human eye

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

Fundamentals Distribution of cones and rods:

Fundamentals The human eye is sensible to electromagnetic waves in the ‘visible spectrum’ :

Fundamentals The human eye is sensible to electromagnetic waves in the ‘visible spectrum’, which is around a wavelength of m = mm

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 !

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

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

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

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

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 ?)

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

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 ………

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

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

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 !)

Fundamentals H(f(x,y)) = f(x,y) /

Fundamentals Remember: the value of the cells is the illumination (or brightness)

Fundamentals As we have a function, we can apply operators to this function… …but why should we ? some motivation for (digital) image processing

Fundamentals Transmission of images

Fundamentals Image Enhancement

Fundamentals Image Analysis / Recognition

Fundamentals The mandatory steps: Image Acquisition and Representation

Fundamentals Acquisition

Fundamentals Acquisition

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

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.

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

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

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

Fundamentals Stepping down from REALity to INTEGER grayvalues v : Quantization

Fundamentals Sampling and Quantization

Fundamentals MATLAB demonstrations of sampling and quantization effects in sampling.msampling.m