Various techniques in Image Processing
and Analysis
In the industrial applications the algorithms and
programs are developed to process the images captured, digitized and stored in
the computer memory.
The size of data to be processed is huge, of the
order of 106 which is to be substantially executed in seconds.
The difficult and time consuming task of processing
is handled effectively by the following techniques.
(1)Image
data reduction
(2)Segmentation
(3)Feature
extraction
(4)Object
recognition
Image Data Reduction:
The purpose of image data reduction is to reduce the
volume of data either by elimination of some or part processing, leading to the
following sub-techniques.
(a)
Digital conversion
Digital conversion is characterized by reduction in
number of gray levels. For a 8-bit register each pixel would have 28=256 gray
levels. When fewer bits are used to represent pixel intensity the digital
conversion is reduced, to suit the requirements.
An image can be broken into regions that can then be
used for later calculations. In effect this method looks for different self
contained regions, and uses region numbers instead of pixel intensities.
A simple segmentation
algorithm might be,
1.Threshold
image to have values of 1 and 0.
2.Create
a segmented image and fill it with zeros (set segment number variable to one).
3.Scanning
the old image left to right, top to bottom.
4.
If a pixel value of 1 is found, and the
pixel is 0 in the segmented image, do a flood fill for the pixel onto the new
image using segment number variable.
5.
Increment segment # and go back to step
3.
6.Scan
the segmented image left to right, top to bottom.
If a pixel is found to be fully contained in any
segment, flood fill it with a new segment as in steps 4 and 5.
Object Recognition:
Form Fitting
It can sometimes help
to relate a shape to some other geometric primitive using compactness,
perimeter, area, etc.
o
Square
o
Ellipse
o
Circle
o Rectangle
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