Image segmentation is a very important research and application direction in computer vision. It divides the pixels in the picture into different parts and labels them according to certain rules. The diagram is as follows:
1. Image classification
Identify the content existing in the image, as shown below, person, tree, grass, sky
2. Object detection
Identify the content existing in the image and detect its location, as shown in the figure below, taking the identification and detection of a person as an example
3. Semantic segmentation
Label each pixel in the image with a category label, as shown in the figure below, and divide the image into labels for people (red), trees (dark green), grass (light green), and sky (blue).
4. Instance segmentation
The combination of target detection and semantic segmentation detects the target in the image (target detection), and then labels each pixel (semantic segmentation). Comparing the above and the following figures, if the target is a person, semantic segmentation does not distinguish between different instances belonging to the same category (all are marked in red), and instance segmentation distinguishes between different instances of the same class (using different colors to distinguish different people) )
5. Panoptic segmentation
The combination of semantic segmentation and instance segmentation is to detect all objects and distinguish different instances in the same category. Comparing the above and below images, instance segmentation only detects and divides the target in the image (such as the person in the above image) and divides it by pixel to distinguish different instances (using different colors), while panorama segmentation is to all objects in the image including the background. Both detection and segmentation are performed to distinguish between different instances (using different colors)
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