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Rectlabel price12/30/2023 ![]() So we will now come to the point where would we need this kind of an algorithm Use-cases of image segmentation With semantic segmentation all of them would have been assigned the same colour. As can be seen in the image above all 3 dogs are assigned different colours i.e different labels. Instance segmentation :- Instance segmentation differs from semantic segmentation in the sense that it gives a unique label to every instance of a particular object in the image.For example if there are 2 cats in an image, semantic segmentation gives same label to all the pixels of both cats It doesn't different across different instances of the same object. Semantic segmentation :- Semantic segmentation is the process of classifying each pixel belonging to a particular label.There are two types of segmentation techniques Source Image segmentation is the process of classifying each pixel in an image belonging to a certain class and hence can be thought of as a classification problem per pixel. We know an image is nothing but a collection of pixels. In this article we will go through this concept of image segmentation, discuss the relevant use-cases, different neural network architectures involved in achieving the results, metrics and datasets to explore. Image segmentation takes it to a new level by trying to find out accurately the exact boundary of the objects in the image. In object detection we come further a step and try to know along with what all objects that are present in an image, the location at which the objects are present with the help of bounding boxes. In the plain old task of image classification we are just interested in getting the labels of all the objects that are present in an image. The most important problems that humans have been interested in solving with computer vision are image classification, object detection and segmentation in the increasing order of their difficulty. Deep learning has been very successful when working with images as data and is currently at a stage where it works better than humans on multiple use-cases.
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