Semantic Segmentation by Example
Posted: Sat Jan 25, 2025 8:59 am
Here, objects in images are classified and their position is marked using a box. It is a classification of areas in the image. This operation is less complex and therefore faster. However, less information is recognized. Only the class and the approximate location are determined. A well-known network for this purpose is the Faster R-CNN. This time the zebra is marked with a box, the bounding box. This makes it possible to recognize and highlight the exact position of the zebra.
Probability: Zebra 100%
Object Detection using the Example
Keypoint Detection
Keypoint detection is used, for example, for human pose estimation. This is a classification of points. The point in a class with the highest probability is taken. The edges visible in the image are not predicted greece consumer email list by the neural network, but are defined as edges between specific points. This is used when it is necessary to recognize what position a person is in. One example is the detection of falls by senior citizens.
Keypoint Detection using the example
Instance Segmentation
Instance segmentation is related to semantic segmentation. Individual instances are segmented. For example, different people are recognized and segmented in images. A distinction can be made between different types of instances of a class. The advantage is that only relevant classes are segmented and used.
Instance Segmentation by Example
Panoptic Segmentation
The combination of semantic segmentation and instance segmentation is called panoptic segmentation. The entire image is segmented and different instances of a class are recognized and differentiated from each other. In the following image, it is clearly visible that the two dogs were recognized as dogs, but as different dogs, not in terms of breed, but in terms of instance.
Probability: Zebra 100%
Object Detection using the Example
Keypoint Detection
Keypoint detection is used, for example, for human pose estimation. This is a classification of points. The point in a class with the highest probability is taken. The edges visible in the image are not predicted greece consumer email list by the neural network, but are defined as edges between specific points. This is used when it is necessary to recognize what position a person is in. One example is the detection of falls by senior citizens.
Keypoint Detection using the example
Instance Segmentation
Instance segmentation is related to semantic segmentation. Individual instances are segmented. For example, different people are recognized and segmented in images. A distinction can be made between different types of instances of a class. The advantage is that only relevant classes are segmented and used.
Instance Segmentation by Example
Panoptic Segmentation
The combination of semantic segmentation and instance segmentation is called panoptic segmentation. The entire image is segmented and different instances of a class are recognized and differentiated from each other. In the following image, it is clearly visible that the two dogs were recognized as dogs, but as different dogs, not in terms of breed, but in terms of instance.