IDEC DATAVS2 Series - Manual (Page 79)

Instruction Manual SVS2 Series
74
16.6. ► Binarization
This is a process that converts any given image to a two-level image (in our instance, black and
white). Conversion is based on a threshold that classifies the individual pixels as "level 255" (or
"white") or "level 0" (or "black") depending on whether they are above or below set threshold.
Let us consider a shot of a dark object on a bright background. Binarization is expressed as follows:
⎩
⎨
⎧
>
<
=
S j i I if
S j i I if
j i B
) , ( 255
) , ( 0
) , (
In other words, the pixel with coordinates (i,j) of the source image I will be coloured black in the
binarized image B if its (brightness) value is found to be below the threshold S, or will be coloured
white if its value is above the threshold.
The purpose of this operation is to highlight just the key features of the image to minimise the
computational load associated with its analysis.
The critical issue in this process is how to select the threshold S.
Let us consider a histogram of a gray scale image (representing the number of pixels as a function of
brightness value)
number of pixels
brightness value: 0 – 255
gray scale image image histogram
As you can see, the brightness values of image pixels are clustered around two values ("bimodal"
histogram): a low value that represents dark points (the object) and a high value that represents bright
points (the background).
The most effective value is found mid-way between the two (128): this will turn out a binarized image
where the object is neatly and accurately represented. With a lower threshold (90), part of the object
would be treated as background; a higher threshold (175) would cause part of the background to be
classified as object.
Image binarized with S = 128 Image binarized with S = 90 Image binarized with S = 175
Obviously enough, an image where object and background are not clearly differentiated (similar
brightness values) will turn out a histogram with all pixels clustered around one value, and setting the
appropriate threshold will prove quite difficult.