Digital Image Processing Part 3 Spatial Domain Processing

Neighbourhood Processing As in the point processing examples, the pixel value at (x,y) is modified but this time it depends on the values of its 8 surrounding neighbours. The mask (3x3 square array in this case) is moved over all x,y and each pixel is modified in the area of interest (usually the whole image)

Convolution Mask Consider modifying a pixel based on the average value of itself and its neighbours 1/9

Smoothing Image after using the averaging mask. Sharpness has deteriorated so image is slightly blurred

Spatial Frequency

Spatial Filtering Adjacent pixels which change little or “slowly” in value have a low spatial frequency Adjacent pixels which change greatly or “fast” in value have a high spatial frequency As the smoothed picture of the bird has less detail, edges or fast changes have been reduced so the spatial frequency has been reduced. The smoothing mask is effectively a low pass filter

Varying mask size Although 3x3 is a typical mask size, any odd numbered size can be used but processing time increases. Diagram shows the original image then the results of using the averaging masks 3x3, 5x5 and 7x7

Sharpening Filter High pass filter, boosts high frequencies so accentuates detail by exaggerating large changes in adjacent pixel values 9

Image subtraction Used to detect defects by subtracting component image from good image Used in component placement Low pass filter an image then subtract result to perform high pass filtering

Component placement - =

High pass filter -= Subtract blurred image from original. Contrast stretch the result to provide an outline of the image edges.

Edge detection Pewitt filters X highlights horizontals and Y highlights verticals Laplace highlights X and Y directions 24