Computer vision is considered to be one of the important areas in the development of machine learning and artificial intelligence. In short, computer vision is the field of artificial intelligence research dedicated to giving computers the ability to see and interpret the world visually. Image annotation is a subset of computer vision and one of the important tasks of computer vision.

1. What is image annotation

Today, with the continuous development of artificial intelligence, computer vision systems play a very important guiding role in many fields, and image annotation is the most basic data support for computer vision. Image annotation is to add tags to the target objects in the image a process. Due to the difference between image annotation types and actual needs, image annotation can be marked with a single label, or multiple labels can be added according to the pixel situation. The use of image annotation can help artificial intelligence to make more precise operations in the commercialization of algorithm recognition, image detection and other fields.

2. Image annotation method and application field

1. Semantic Segmentation

This type of image annotation method performs regional annotation on object attributes and irregular or complex pictures, and adds more attribute factors to the annotation content, thereby supporting the operation of systems such as image recognition training models. In general, semantic segmentation and labeling methods will be applied in the fields of human-computer interaction and automatic driving.

2. Rectangular frame labeling

This is an image annotation method that can be commonly used in many fields at present, and uses a convenient and simple way to frame the specified target object.

3. Labeling of key points

This type of labeling method is to mark key points at specified positions according to corresponding requirements, such as bone connection points or facial features, etc., and is often used in statistical models and facial recognition systems.

4. Point cloud labeling

As an important presentation method of 3D data, it is often used in the field of automatic driving to mark various position coordinates and obstacles in a very dense point cloud, and to mark them according to their attributes.

The role of image annotation is introduced here. In a word, whether it is computer vision or artificial intelligence, it is inseparable from the strong support of image annotation data. The accuracy of data also determines the degree of implementation of artificial intelligence. Therefore, only by fully understanding the relevant content of image annotation can we make better data work.