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Object detection is connected with computer vision and image processing. Its main role is to detect objects with given semantic features (common for a given category) in movies or digital images. In this stage, the system associates an object label to a section in the image/video. Deep learning is often used at this stage.
Semantic segmentation is the process by which we assign labels to each pixel in the image. In this process, the system assigns a generic label to each pixel of the image. The labels are “class” labels so many objects share the same class as a single element. In other words, the semantic segmentation is the process of associating an image pixel with the label of a given class.
Instance segmentation is the detection of an individual object of interest that appears in the image. Instance segmentation is slightly more difficult than semantic segmentation, as it assigns a specific object to a class so if the semantic segmentation assigns the class value the instance one assigns the specific instance to that object so from Dog → German Shepard → Fido
Context classifies information about the surrounding environment. In the real world, the human eye does not see a single thing but the whole environment in which the object is located. In machine vision, objects also do not consist of a single-pixel. The more complex the environment where the image is presented, the greater the possibility of contextual associations to use. Context analysis means that the machine focuses on the pixel’s relationship with other elements in the surrounding area. The larger the context, the higher the complexity but the more complete the correlation.