Irregularly shaped objects are a special challenge for image annotation tools. As bounding boxes can describe them to some extent, they can’t be used to track the contour and interpret the exact shape of an object, and the following problem is often a huge noise between an object and bounding box boundaries. Polygons also represent 2D annotations. They are the best fit if you want to be more precise when it comes to shape, but stick to the labels given to the pixels inside of a closed contour.
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Our in-house professionals, using our image annotation tools with built-in machine learning and multiple layers of quality control, create the tightest and most accurately bounding boxes for computer vision and deep learning applications.
Each pixel in an image is mapped into specific classes; for example 'cars' and 'pedestrians' in autonomous driving data.
Every pixel in an image is mapped to specific classes. Regularly used in drone and autonomous driving technology.
Each frame or key-frames annotated with or without object tracking.