By John G. Webster (Editor)
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Extra resources for 16.Control Systems Technology
Smoothing Filters. Smoothing filters remove noise and rough edges by approximating low-pass filters. The three most common smoothing filters are mean, median, and Gaussian. Mean and Gaussian filters are linear and obey the general convolution laws described previously. The median filter is not linear but is easy to implement using similar ideas to those already developed. There is a trade-off in using smoothing filters to eliminate noise: smoothing can eliminate noise, but it also blurs the image, obscuring edges and making object detection less accurate (3).
The simplest thresholding operation is mapping an image from a gray-scale image to a binary image. The image is scanned left to right and top to bottom. Any pixel with a grayscale value greater than the threshold is changed to white, and any pixel with a gray-scale value less than the threshold is changed to black, as shown in Eq. (2). A more general version of the thresholding operation is shown in Eq. (3) (2). The operation will turn a pixel white if it belongs to set F and black if it does not.
The morphological approach has the additional advantage of mathematical rigor. Instead of each image being expressed as the result of a convolution filter as with the frequency case, morphological operations are built from primitive operations and allow the user to express imaging processing as a kind of algebra. Morphological operations are based on set theory, where the main set (the image) is compared with a subset to determine values such as shape. Morphological operations are performed on binary images, but they can be performed on gray-scale images as well.
16.Control Systems Technology by John G. Webster (Editor)