As discussed earlier, background subtraction methods are affected by many factors. Their accuracy depends on how we capture the data and how it's processed. One of the biggest factors that tend to affect these algorithms is the noise level. When we say noise, we are talking about things, such as graininess in an image, isolated black/white pixels, and so on. These issues tend to affect the quality of our algorithms. This is where morphological image processing comes into picture. Morphological image processing is used extensively in a lot of real-time systems to ensure the quality of the output.
Morphological image processing refers to processing the shapes of features in the image. For example, you can make a shape thicker or thinner. Morphological operators rely on how the pixels are ordered in an image, but on their values. This is the reason why they are really well suited to manipulate shapes in binary images. Morphological image processing can be applied...
