Different type of object marking techniques.
Image annotation helps to make images readable for computer vision. Annotated images are useful for performance calculation of other fully automatic algorithm results. They are called as benchmark, ground truth or reference data. Comparing to the annotated images, it is possible to calculate true positives and false alarms of a fully automatic algorithm.
Annotation in machine learning is the process of labeling data, which could be in the form of text, images, audio, etc. In machine learning, computers can use the annotated data to learn to recognize similar patterns when presented with new data. Annotation is typically done manually by humans, but crowdsourcing can speed up the process and spread out the workload.There are many marking techniques for image annotation that are used conventionally.
Bounding box
Bounding boxes are an important method of image annotation for computer vision. Bounding perfect boxes around the objects at the given frame for general recognition.
Polygonal
Generate boundaries of objects in a frame with optimum precision and gives a well-defined idea about the shape and size of the object. This is the fastest, smartest and collaborative way to classify objects for machine learning.
Keypoint
Accurately marking all the required parts of the object in the image, and helps to analyze the positioning and size of the object. Mainly marking outermost points of objects.
E.g. for a vehicle we pointing the outermost points like wheels, mirrors, and lights separately.
Cuboidal
Moulding 3D high-quality label around the needed gadgets, vehicle, building or even humans for getting the overall space or volume of the object. Mainly used in the field of construction and object recognition.
Semantic segmentation
In image annotation for computer vision, semantic segmentation is the process of partitioning a digital image into multiple segments and thereby changes the representation of an image into something that is more meaningful and easy to analyze.
