Intensity Transformation and Spatial Filtering

– Square or rectangular subimage area centered at (x, y) (Figure 3.1) ∗ Typically, the neighborhood is much smaller than the image – Center moves over each pixel in the image – T is applied at each point to get g at that location ∗ Compute the average intensity of the neighborhood – Also possible to have neighborhood approximations in the form of a circle – The above application is also called spatial filtering ∗ Neighborhood may be extracted by a spatial mask, or kernel, or template, or window – Handling pixels at image border ∗ Part of the neighborhood is outside the image frame ∗ Outside pixels can be ignored; replaced by a uniform gray scale; replaced by 0; or inner pixels can be reflected outside