What types of annotations are there?

 

INTRODUCTION

Image annotation is the act of labelling images. This can be done either with one label that covers the entire image or multiple labels that cover each object. An image dataset is required to start an image annotation project. It is a marking tool that highlights the content or objects of a photo by drawing a circle surrounding them. Image annotation is vital in the development object detection models that are used frequently in computer vision applications.

Types of image annotation

Let's look at some of most commonly used Image Annotation type that are used in Computer Vision project development.

Annotation for the Bounding Box

It is the process of drawing a border around objects in a photograph to annotate them. It is usually done by drawing a circle around the objects. Annotations are made to match the requirements of data scientists. It is one the most used type of picture annotation. It is vital in training self driving vehicles by labelling pedestrians, other vehicles, and other impediments in traffic photos.

Annotation on a Cuboid

Cuboid annotation: A Cuboid box or cuboid is used to draw around objects in an image. This type of annotation is comparable to bounding containers. Cuboid annotations show the depth, length and width of the items, and also highlight 3D objects. Bounding box, on the contrary, shows only the width and the length of the objects.

 

Cuboid annotation is used primarily in construction and building constructions, as it gives accurate item measurements. It is used for annotating medical photographs in radiation imaging.

Semantics-based segmentation

The more specific and precise semantic segmentation, also known by pixel level labelling, can be used to identify a pixel in an image. It labels each pixel in an images, while the edges and edges of an object are highlighted. This makes it different from other types. It is easier to describe an object in a meaningful way by breaking down the image into smaller pieces. Semantic Segmentation is used in several areas, including medical image analysis and industrial inspection.

Annotation for a Line

Line Annotation, which is used to train machine learners models to recognize lanes and limit by drawing lines on streets and roads, is primarily used for training them. Line Annotation has the most widespread use. It is used to teach autonomous vehicular vehicles to stay in one lane without swerving or to detect borders.

Segmentation Polygons

Polygonal segments are one of the fastest and smartest ways to annotate things for machine learning. It assists in identifying the limits and dimensions of objects. It can also be used to accurately determine the shape and size objects that have been captured with distant cameras. Polygonal Segmentation is able to detect logos and facial features as well as street signs.

Annotation of a Landmark

It can identify the differences between items and assist in the counting of microscopic objects within photos. It is also called Dot Annotation. It can be used to predict pedestrian movement for driverless vehicles, locate distant objects in satellite pictures, and identify different athlete stances. The above-mentioned picture annotation techniques are among the most widely used for training ML modelers.

How can we help?

Are you considering outsourcing image dataset tasks? Global Technology Solutions is the right place to go for all your AI data gathering and annotation needs for your ML or AI models. We offer many quality dataset options, including Image Data Collection, Video Data Collection, Speech Data Collection, and Text Data Collection. 

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