Image annotation is a complicated task. The best image annotation companies are able to deal with the complexity of image annotation in a timely and efficient manner, annotating images with precision and speed. Professional image annotators know how to deal with the many problems and considerations that come up when carrying out image annotation.
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Creation of bounding boxes is one of the primary image annotation services offered by ImageAnnotation.ai. Bounding boxes are used to identify objects. Colored boxes are assigned to objects in the image, surrounding the object in question. This specifies where the network should be looking for the image, describing the object’s target location. The process of assigning bounding boxes to an object can be complicated, as one must take into account things like overlapping objects.
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.
Binary Masks are the simplest image segmentation option. They provide images with 2 possible values for each pixel - 0 or 1. They can be used to label a single class image, while if there’s more than one class - the result will be several binary images. Binary masks are a great choice if you want to define the exact shape of an object and its size in pixels in the image.
Semantic segmentation doesn't just put bounding boxes and labels around more objects, it actually classifies every single pixel within an image. The output is typically a high-resolution image that usually maintains the same size as the original image. The “semantic” part of the term semantic segmentation describes how regions of the image are classified based on their semantic meaning.
Another type of image annotation is instance segmentation, which essentially takes the concept of semantic segmentation and applies it to every object of interest/every instance within an image. This means that instead of labeling people as one group comprising a “person” class, every individual in the image is assigned a different class, classifying every instance of that object.
Combining semantic and instance segmentation results in panoptic segmentation. While in semantic segmentation all entities of one class are displayed with one exact color, instance segmentation is focused on labeling different individuals of one class separately. Semantic segmentation is used to label “stuff” - objects harder to quantify, and the instance is used to label “things” - objects that could have more than 1 countable instance. Having stuff and things in the same image is provided by an image annotation method called panoptic segmentation.
Keypoint annotations are used to annotate a single pixel in the image, which has found its use in movement tracking and prediction, human body parts detection, gesture and facial recognition. Keypoints together could portray the object’s shape. They are commonly used in sports and security.
There are many ways to perform text annotation, and you can choose which one is the best fit for your project. We can annotate letters, words, sentences, or numbers, and give them meaning. Text can be tagged with a bounding box, assigned to a proper class, and used in NLP to make written text recognizable to machines.