Image annotation services
Image Annotation for
Computer Vision
Image annotation is a task of marking and outlining of objects and entities on a image and offering various keywords to classify it which is readable for machines. This is very important task as this data helps generate datasets which helps a computer vision models work in a real world scenario. We assure optimization of image annotation for computer vision models in variety of functions, tools, and formats with highly skilled workforce. ROORA provides various image annotation services that will match your project’s needs, Bounding Box Annotation, Polygonal Annotation, Cuboid Annotation, Road and Lane Annotation, Pixel Level Semantic Segmentation, Point & Dot Annotation, 3D Lidar Labeling Annotation, Video Annotation and more…
Bounding Box annotation
Bounding Box
Bounding box annotation is the most common and widly used model for Machine learning task. The annotators will draw a bounding box over an object and label them with and object class. Generally the bounding boxes are drawn with no loose ends. The task seems simple but it requires meticulous effort to keep up the consistency. ROORA offers 2D & 3D bounding box annotation outsourcing services for computer vision and machine learning applications. The use cases are for autonomous vehicle, SKU classification in retail industry, food tray, House objects, and many more.
PolyLine annotation
PolyLine
We ROORA making the irregular shaped coarse objects recognizable to computer vision based machines like self-driving cars and autonomous vehicles. We are providing a human- powered image polygon annotation service with next level of precision. Polygon annotation Detecting the irregular shapes or coarse objects on the road is not possible for computer vision-based machines, unless it has been properly labeled. Polygon annotation is the right image annotation technique makes such polygonal shaped objects recognizable to autonomous vehicles and self-driving cars to drive in the right direction.
Cuboid annotation
Cuboid
We offer is Cuboid Annotation, which annotates your two-dimensional images with projections of cuboids enclosing objects such as cars, trucks, pedestrians, traffic cones, you name it. With some additional information, we can turn those two-dimensional box annotations into full, three-dimensional boxes, with height, width, depth, rotation, and relative positioning info.
Road and Lane annotation
Road and Lane
Road and lane annotation detecting the Lane Lines, Lane Marking on the road, Traffic Sign and Signals , different type / section of road and Parking Area , etc…… Road Lane annotation is helpful for autonomous vehicle to have boundary lines of a road
Pixel level Semantic Segmentation annotation
Pixel level Semantic Segmentation
Semantic segmentation refers to the process of linking each pixel in an image to a class label. These labels could include a person, car, flower, piece of furniture, etc. Semantic segmentation is image classification at a pixel level. For example, in an image that has many cars, segmentation will label all the objects as car objects. However, a separate class of models known as instance segmentation is able to label the separate instances where an object appears in an image.
Point and Dot annotation
Point and Dot
The points and dots have distinct characteristics and functions. The proper connections of points and dots can forming desire shapes also can precise marking of required parts of an object in the image. In point and dot annotation object in the image are labeled using points/dots to determine shape of it and accurate marking of body parts,automotive parts, etc...
3D LiDAR annotation
3D LiDAR
3D Lidar Labeling Annotation is Label the objects at every single point with highest accuracy. 3D Lidar Labeling annotation is capable to detect objects up to 1 cm with 3D boxes with definite class annotation. Used for autonomous vehicles to identify objects in the both environment indoor and outdoor.
Video annotation
Video
Video Annotation is simple terms annotating the content of the video. It doesn’t mean transcribing the video content but to annotate required parameters in a video. The Object Detection, Marking, Labeling and Tracking are done in frame by frame.
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