PythonArtificial Intelligence

Computer Vision : A New Vision

Artificial intelligence (AIfield )’s of computer vision enables computers and systems to extract useful information from digital photos, videos, and other visual inputs and to conduct actions or offer suggestions in response to that information. If AI gives computers the ability to think, computer vision gives them the ability to see, observe, and comprehend.

There are a ton of pictures online! In the era of the selfie, capturing and sharing pictures has never been simpler. In reality, every day, millions of photographs are uploaded and seen online. It’s critical that computers be able to perceive and comprehend images in order to fully utilize the enormous volume of images available online. And although people can accomplish this naturally and quickly, computers find it more challenging. Computer vision can help with this.

Computer Vision
Computer Vision

Back Story of Computer Vision

For almost 60 years, scientists and engineers have worked to create systems that would enable robots to see and comprehend visual information. In an effort to correlate a brain reaction, neurophysiologists started their first experiment in 1959 by exposing a cat to a variety of visuals. They found that it responded to hard edges or lines initially, and scientifically this indicated that basic forms like straight edges are where picture processing began.

Neuroscientist David Marr demonstrated that vision functions hierarchically in 1982 and developed methods enabling computers to recognize edges, corners, curves, and other fundamental structures. In parallel, Kunihiko Fukushima, a computer scientist, created a network of cells that could identify patterns. Convolutional layers of a neural network were part of the Neocognitron network.

The focus of research shifted to object identification in 2000, and the first real-time face recognition applications debuted in 2001. Through the 2000s, there was a standardization of the tagging and annotation of visual data sets. The ImageNet data collection was made accessible in 2010.

It served as the foundation for the current generation of CNNs and deep learning models and includes millions of annotated photos from a thousand different object classes. A University of Toronto team entered CNN in an image recognition competition in 2012. The Alex Net model drastically decreased the rate of error in picture recognition. Error rates have now decreased to only a few percent since this discovery.


  • By pointing a smartphone camera at a sign in another language, users may use Google Translate to get a translation of the sign in their favourite language practically instantly.
  • Computer vision is used in the development of self-driving cars to interpret the visual data from the cameras and other sensors. Identification of other vehicles, traffic signs, lane markings, bicycles, pedestrians, and any other visual elements encountered on the road is crucial.
  • A puppy, an apple, or a person’s face are examples of images that may be classified using image classification. More specifically, it can correctly guess which class a given image belongs to. A social network corporation would wish to utilise it, for instance, to automatically recognise and sort out offensive photographs shared by users.
  • In order to identify a certain class of picture and then recognise and tabulate its existence in an image or video, object detection can employ image classification. Detecting damage on a manufacturing line or locating equipment that needs repair are a few of examples.
  • After an item is found, it is followed or tracked. This operation is frequently carried out using real-time video streams or a series of sequentially taken pictures. 

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