Computer vision in AI

22/05/2024 20mins
Prasobh V Nair


Technology has managed to attain great heights in the past few years. However, if there is one aspect of the technology in which there is room for improvement it is the 'vision'. There is hardly any machine which is visually enabled, and this is what computer vision is all about. Computer vision refers to the ability of the machine to analyze and understand an image. The computer vision provides input to the machine which commences the whole process via the convolution Neural Network.

Convolution Neural Network
The whole neural network work is based on a neuron which comprises input and output channels with a processing body in between. An artificial neural network is formed via a multitude of these nodes. The number is so large that a humungous system is created owing to the same.

It is computer vision which ensures that the image which is provided to the machine as an input is detected by the machine and some intelligence is derived from it.

The convolution neural network is responsible for processing the data for its receptive field, the organization of which is done such that an entire image is represented. The network comprises several layers, the responsibility of which is the extraction of features. The arrangement of the layer is in accordance with the complexity of the visual representation.  Thus, with the help of this network, complex images can be understood by the machine.

Three main layers are found in the convolution neural network

  • Convolutional Layer: It is responsible for emitting the response of a specific neural for its receptive field.

  • Pooling Layer:  It reduces the spatial size of the output which is generated by the convolutional layer. It also detects higher-level details.

  • Fully Connected Layer: It is in this layer that the nodes are connected to one another. It maps the information derived from the layers to the proper output.

Several other components form part of the convolutional neural network such as the batch normalization layer and dropout among others.

Uses of computer vision
Now that we have elaborated on how computer vision works let us talk a bit about the purposes that we can be put to. Computer vision applications include:

  • Inspection via imaged-based automation such as in manufacturing
  • Identification of specific objects or species via their properties
  • Controlling processes
  • Monitoring events by providing visual surveillance
  • Navigation and organization of information

How to extract the best use from a computer vision application
The question which now arises is how we can use computer vision application to get maximum benefits. Let us provide you with some ideas.

  • Look for changes in the existing jobs: some ideas can be sought by taking a look at the existing jobs. For instance, detection of vehicles breaking the traffic rules and generating a fine slip for the same is one situation where computer vision can be applied. We can look for cases wherein some assistance is required and determine whether computer vision can make things easier.
  • Solutions to problems: problem-solving can be attempted via computer vision to see if the application proves to be of some use.
  • Assistance in research: it is research which will provide you with the best ideas. Your research will enable you to acquire an understanding of the market so that computer vision can be put to better use.


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