Annotation with Auto-Segmentation

Annotation with Auto-Segmentation

Apart from the four annotation shapes box, polygon, polyline and points mCycloid provides a special annotation feature which eases the annotation job in terms of speed and accuracy of annotation. It is called Auto-Segmentation. It is used to create a polygon semi-automatically. This guarantees customer satisfaction in terms of both the quality and timescale. The theory behind it is just an extension of the theory behind polygon annotation.

Sometimes objects in an image don’t fit well in a bounding box due to their shape, size, or orientation within the image. As well, sometimes developers want more precise annotation for objects in an image like cars in traffic images or landmarks and buildings within aerial images. In these cases, developers might opt for auto-segmentation.

Auto-segmentation is mainly used to annotate objects with irregular shapes. Unlike boxes, which can capture a lot of unnecessary objects around the target, leading to confuse training, auto-segmentation with polygons are more precise when it comes to localization.

Auto-segmentation tells a computer vision system where to look for an object using complex polygons. Object’s location and boundaries can be determined with much greater accuracy. The advantage of using auto-segmentation over bounding boxes is that it cuts out much of the noise and unnecessary pixels around the object that can potentially confuse the classifier.

Auto-segmentation solution enables you to annotate data with precision and high quality which helps in building state-of-the-art computer vision models. With auto-segmentation, annotators draw lines by placing minimal imaginary points around the outer edge of the object they want to annotate. The process is like a connect the points exercise while placing the dots at the same time. The space within the area surrounded by the dots is then automatically annotated.

It is one of the fastest and smartest way of annotating the various types of objects for machine learning. In this process of image annotation, the borders of an object in frame with best level of accuracy that help to identify the object with right shape and size. This type of image annotation techniques is used to detect various types of objects like street sings, logos and facial features in sports analytics to more detailed recognition of such objects.

Few of the use cases are:

Precise Detection in Agriculture-Tech:

Analyse plant health. Annotating all the irregular objects of the plants to detect the plant disease at an early stage.

Logo Recognition:

Annotate the logo’s which are in irregular shapes and build a model to detect the logo.

Autonomous Vehicle:

Detect lanes, traffic, potholes. Polygonal annotation is used to train the autonomous driving model to understand the real-world scenario

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