Annotation with Points
A 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, and larger dots are sometimes used to distinguish important landmark areas from surrounding areas. Because landmark annotation or dot annotation draws small dots that represent objects, one of its primary uses 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 uses 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 analyse the motion and posture of people.
Use cases for landmark annotation are:
Computer Vision helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s 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 to quickly through customer databases, or even for surveillance and security by identifying fraudsters.