A comparison of different types of image annotation
Computer vision is a big discipline of AI and machine learning that helps computers to get useful information from digital media such as images and videos, and act or make suggestions based on that information.
Image annotation is the technology that lets the computer gain a deep understanding by labeling an image using text or annotation tools to show data features the user wants it to recognize on its own.
- Image classification
- Object recognition
- Image Segmentation
- Boundary recognition
Definition
They are planes that act as a citation for detecting an object. Bounding boxes are used to set a class to the interested object by surrounding the target. This image annotation makes it simple for algorithms to get what they're finding in an image and relate the object detected with what it was trained on at first.
Bounding boxes are also used to teach autonomous vehicles to detect the different objects on the streets.
Use Case
Detecting Objects for self-driving cars. Bounding box data helps machines identify objects on the road and beyond e.g. cars, pedestrian, street signs and lights
Definition
They are used to recognize lanes and boundaries in an image. They are used when the annotated part is considered as a boundary but it is small for bounding boxes or other annotations.
Use Case
Can be used to reach robots in the warehouse to accurately put items in a row. Can also be used to annotate sidewalks and lanes for autonomous vehicles to know boundaries and stay in the lane.
Definition
Involves creating dots or points throughout an image. It uses dots to mark objects in images with many little items. Many objects are connected together to show the outline of an object while larger dots are used to show landmark sites from around areas.
Use Case
Landmark annotation is used to get better accuracy in human figures and sentiment analysis. Can also be used for facial recognition in security systems and in video games to track movements of characters.
Definition
3D cuboids are used to predict the shape and volume of an object not only recognize it. They also have depth, height and width. Anchors are put at the edges of the item and the space between is filled with a line, this will create a 3D representation of the object.
Use Case
3D cuboids are mostly used for autonomous systems capable of locomotion e.g locomotive robots because they can make predictions about the object in its surrounding environment.
Definition
Polygons are used when objects in an image don’t fit well in a 3D cuboid or bounding box due to their size or shape, as well as when someone wants more accurate annotation for objects. Polygons cut out unnecessary pixels surrounding the object which can confuse annotators.
Use Case
Polygons are also used to annotate asymmetrical objects within an image, such as vegetation, and houses. In autonomous driving polygons can be used to identify asymmetrical objects such as street signs and also accurately locate cars.
Definition
Semantic segmentation involves dividing an image into different areas and setting labels to all the pixels in an image. Areas that have non-identical semantic definitions can be separated from other areas. Areas are defined according to the semantic information, and are given labels to each pixel in that region.
Use Case
Semantic segmentation is used in autonomous vehicles where the AI of the vehicle will differentiate between different sections e.g. road or sidewalk or grass sections.
In the field of medicine, semantic segmentation is used in recognition of images for diagnosis. Semantic segmentation is also used to detect crops and weeds in a farm.
The time taken in image annotation depends on various things such as: how complex the image is, how many objects are there, the type of annotation (each annotation differs in complexity) and how accurate or the level of detail.
Simple objects will need less time to annotate compared to complex objects.
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