Various techniques in Image Processing and Analysis

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