This lecture focused on two major topics: Noise Reduction and Edge Detection.
Sometimes images may not have optimal clarity, and may contain random speckles of noise. There are several ways of reducing the noise present in an image. They work on the idea that the noisy pixels have values that are very different than their neighbors. The noise is compared with the neighboring pixels to smooth out the area, and reduce noise.
Edge detection involves deriving the probable edges of objects in an image. Area processes are used to determine areas where there is a great change in the pixel values. These are possible object edges. Various operators can be used to find edges. They vary in their sensitivity, as well as the direction of the edges that they can better detect.
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