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Classification
Classification is the process of determining the class or category of an input
image. The input image is analysed using a Machine Learning Algorithm, which
assigns it to one of the predefined categories. These categories are established
by training the algorithm on a set of labelled sample images. This foundational
task in Computer Vision has numerous practical applications, such as medical
diagnostics, object recognition and autonomous vehicles. For example, in the
given image the object is identified as motorcycle.
Classification and Localisation
Localisation means where the object is located 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, in the given image the object is identified as motorcycle which is
classification and locating the motorcycle in the image is localisation.
Multiple Objects
This means giving multiple images as input to the Computer Vision application. It can be further divided into two
categories i.e. object detection and instance segmentation.
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 recognise 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. For example, in the given image motorcycle and car both are detected.
Instance Segmentation
Instance segmentation is the process of dividing an image into smaller segments
to identify individual objects within it, distinguishing them from the background
or other objects in the image. Each object instance is detected, classified, and
assigned a unique label. Following this, every pixel in the image is labelled based
on the corresponding object instance it belongs to. A segmentation algorithm
takes an image as input and outputs a collection of regions (or segments).
Another example of Computer Vision analysis can be seen as follows:
Classification Instance
Classification Object Detection
+ Localisation Segmentation
CAT CAT DOG, CAT DOG, CAT
Single object Multiple objects
Computer Vision (Theory) 313

