Annotation with Points

Annotation with Points

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Annotation with Points

The fourth type of image annotation for computer vision systems is Point annotation. Point annotation also known as Landmark annotation or Dot annotation is used to detect shape variations and count minute objects. Owing to the fact that it involves the creation of dots or points across an image it is called so. Just a few dots can be used to label objects in images containing many small objects, but it is common for many dots to be joined together to represent the outline or skeleton of an object.

The size of the dots can be varied. Larger dots are sometimes used to distinguish important landmark areas from surrounding areas. Because landmark annotation or dot annotation uses small dots to represent objects, one of its primary usage is in detecting and quantifying small objects. For instance, aerial views of cities may require the use of landmark detection to find objects of interest like cars, houses, trees, or ponds. That said, point annotation can have other use cases as well. Combining many landmarks together can create outlines of objects, like a connect-the-dots puzzle. These dot outlines can be used to recognize facial features or analyze the motion and posture of people.

Some of the use cases are:

Facial Recognition

Computer Vision helps you detect faces, identify faces by name, understand emotion, recognize complexion and that is not the end of it. Thanks to the fact that tracking multiple landmarks can make the recognition of emotions and other facial features easier.

The use of this powerful annotation shape is not limited to just fancying photos. You can implement it quickly through customer databases, or even for surveillance and security for identifying fraudsters.