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Single Objects
This means giving one image as input to the Computer Vision application. It can be further divided into two
categories:
Classification
Classification is the process of finding out the class/category of
the input image. The input image is processed using a machine
learning algorithm and classified into predefined categories. These
predefined categories are created in a computer by a set of sample
images. The most popular architecture used for image classification
is Convolutional Neural Networks (CNNs). For example, identifying
the image of monument as India Gate.
Classification + Localisation
Localisation means where the object is in the image. So, this combined task of classification and localisation
means processing the input image to identify its category along with the location of the object in the image. For
example, Identifying the above image of the monument as India Gate and its location in Delhi, India. These two
processes together are applicable only for single objects.
Multiple Objects
This means giving multiple images as input to the Computer Vision application. It can be further divided into
two categories:
Object Detection
Object detection is the process of identifying or detecting the instances of real-world objects like cars,
bicycles, buses, animals, humans, or anything on which the detection model has been trained. This kind of
system uses Object detection algorithms to extract the features of the object and after that machine learning
algorithms will recognize the instances of an object category by matching it with the sample images already
fed into the system. It is commonly used in applications such as image retrieval and automated vehicle
parking systems.
Instance Segmentation
Instance Segmentation is the process of division of an image into smaller objects so that the machine can
identify an object from the background or by using information about other objects present along with it in
the input image. After it has identified it, then each pixel is given a label on the basis of that. A segmentation
algorithm takes an image as input and outputs a collection of regions (or segments).
Classification Instance
Classification Object Detection
+ Localization Segmentation
CAT CAT CAT, DOG CAT, DOG
Single object Multiple objects
Computer Vision 255

