O'Reilly logo

OpenCV By Example by Vinícius Godoy, David Millán Escrivá, Prateek Joshi

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Morphological image processing

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 ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required