Semantic Segmentation
With Semantic Segmentation, every pixel needs to be associated with a labelled image such as a car, road, pedestrian etc.
- Enhances algorithmic efficiency
- Removes background, therefore increases accuracy
Tagging
Tagging refers to labeling the people and objects that are located in the image. This form of image annotation is widely used in the retail, e-commerce, marketing.
- Sort and arrange the images quickly
- Prompt access to a certain image using keywords
3D Bounding Boxes
3D Cuboid annotation is taking regular 2D images and videos and annotating them into 3D models by applying various elements.
- Calculates the distance between various objects to define their size and surrounding space
- Helps to figure out the traffic situation in the automotive industry
Polygon Annotation
If you encounter the exact shape of an irregular object, our polygon annotation service will use segmentation masks to get the most precise image annotation.
- Ensures precise localization
- Detects the exact shape and size of the objects
2D Bounding Box
Bounding boxes are superimposing a box around the object you would like the computer to recognize in a given image. It is used in the automotive, and agriculture.
- Ensures the shortest time for annotation
- Requires minimal costs
LandMarks
Annotating all of the various landmarks out there is done by marking key points in a specified area. It was designed to help capture human figures and poses in 2D images and videos.
- Recognizes face details, emotions, and gestures
Image Masking
For many tasks that process annotated data such as training machine learning algorithms, it can be useful to have a mask image representation where pixel values encode ground truth information.
- Generate a mask image from a region defined by user-specified coordinates.
- Generate mask images from annotations contained within region-of-interest annotations. This involves mapping annotations to these regions of interest and creating one image for each one.
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