Spatial operators



In the last lecture we looked at image processing operations, where each pixel in the output image is computed from the corresponding pixel in the input image; or from two input images in what we called "The Diatic Case".  In this lecture we're going to consider what we call "Spacial Imaging Processing Operations" and here the output pixel is a function of the corresponding pixel in the input image and a region of pixels around that input pixel.  And this enables us to do some pretty interesting things.  It allows us to smooth an image, it allows us to find edges with an image and edges are very useful structure to use for robotic vision. 

The other sort of filter we're going to talk about in this lecture is what we call "Morphological Filtering" and these are filters that are sensitive to particular shapes within the input image. 


There is no code in this lesson.

We will consider a very powerful group of functions, spatial operators, where each output pixel is a function of the corresponding input pixel and its neighbours.

Professor Peter Corke

Professor of Robotic Vision at QUT and Director of the Australian Centre for Robotic Vision (ACRV). Peter is also a Fellow of the IEEE, a senior Fellow of the Higher Education Academy, and on the editorial board of several robotics research journals.

Skill level

This content assumes an understanding of high school level mathematics; for example, trigonometry, algebra, calculus, physics (optics) and experience with MATLAB command line and programming, for example workspace, variables, arrays, types, functions and classes.

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