Annotation types we create
Bounding box annotation
Bounding boxes are one of the most commonly used and recognizable tools when it comes to image processing for image and video annotation projects.
Polygon annotation is the go-to when it comes to irregular and complex shapes. It captures more lines and more angles than a regular bounding box.
Semantic segmentation treats multiple objects of a particular category as one collective entity. Objects shown in an image are grouped together based on defined categories.
Reveals body position and alignment. These are commonly used in sports analytics and security applications.
Key points annotation
Identify and mark the key points of any object. For example, eyes, nose, and lips on a human face.
Most commonly used for annotating roads, pipelines and rails.
Instance segmentation detects the instances of each category. It identifies individual objects within these categories. Categories like “vehicles” are split into “cars,” “motorcycles,” “buses,” and so on.
If your project requires a specific combination of annotation types, we can easily do that for you. Our innovative and experienced R&D team can evaluate your project and create a completely new annotation type based on your specific requirements.
Let us handle your image labeling needs. Our team of in-house professional annotators can work with large amounts of data without sacrificing speed or quality. Our workforce and our proprietary annotation tools form a winning combination for any project. We can assure you that no matter how big your project is, we will have all the necessary resources and solutions to facilitate your work.
Precise, scalable image annotation is a key element of any computer vision project. Annotators apply different annotation techniques to images in order to create datasets that meet the varied requirements of today’s AI innovators.
Image annotation with bounding box
Bounding boxes are rectangles used by annotators to frame objects in images so that they can be located by machine learning models.
Bounding boxes are used to define objects across a variety of industries, for example identifying cars and trucks in images of traffic for autonomous vehicles.
The most commonly used annotation type
Image annotation with polygon
Polygon annotation adds additional granular detail to object annotation. Specific points are created around the outline of an object that are then connected with vertices. This allows annotations to capture complex and irregular shapes in images.
Semantic segmentation divides images into sets of pixels that correspond to real-world categories. Using polygon lines annotators split images into classes, such as: road, sidewalk, building, sky.
Instance segmentation adds additional levels of detail to annotations. Instance segmentation defines each instance of each object appearing in a given image. In practice this means that, for example, every car in an image of traffic will be outlined and labeled.
Image annotation with points
This technique allows annotators to mark key points on any image. This annotation type is commonly deployed to locate facial features like eyes, noses, and lips.
Image annotation with lines
Line annotations are often applied to images of linear objects, such as: roads, pipelines, and electrical wires.
Skeletal annotation on images
This technique draws lines between points of articulation on the human body. Images annotated in this manner are used to indicate body position and alignment.
Image annotation is used in
Autonomous driving in different road and weather conditions, object detection, lane detection, in-cabin AI, accident prevention.
Face and emotion recognition, recognition of weapons or dangerous objects, object classification on security monitors.
We can annotate drone footage for a variety of use cases: AI-enabled drone applications, satellite image annotation, construction progress tracking, construction site safety, equipment tracking, property monitoring, road traffic reporting, pipeline monitoring, natural disaster monitoring and management, field monitoring.
- Precision Agriculture and Livestock Management
There are many use cases for AI in Precision Agriculture and Livestock management: plant recognition, crop monitoring, growth monitoring, ripeness detection, soil condition monitoring, plant disease detection, pest detection, animal health monitoring and abnormal behaviour detection.
Medical image annotation is an in-demand labeling technique, one which few companies are able to accomplish. If you need your data annotated by certified professionals, you are in the right place. We can collect medical imagery on demand and annotate it with the highest possible accuracy.
Are you excited with the progress being made in the robotics industry? We are here to support the development of computer vision based AI models in this sector.. Image annotation use cases in robotics include: object detection, environment perception, inventory handling, quality control, predictive maintenance, waste management.
- Waste Management
Waste management and recycling are increasingly important in our global struggle against plastic waste. We annotate data for sorting facilities, including: management of medical and biohazard waste, recyclable waste, marking annotation, aerial annotation for waste management, waste categorization.
Smart check-outs, stock management, planogram improvements, sentiment analysis, in-store traffic analysis, loss prevention, face recognition for loyalty programs, virtual fitting rooms.