Computer vision is a growing field of computer science which is a modern-day application of machine learning. This field focuses on creating systems that can acquire images/video, process and analyze them to make sense of it.

Consider computer vision as an attempt to replicate our visual systems that relies on our eyes to acquire the data and uses brain to process and decipher what it’s seen.

Steps of Computer Vision

We can categorize the working of computer vision into three major steps. Firstly, such systems have to acquire the data. Camera technology has come a long way in order to take high quality digital pictures/videos.

There might be a requirement to compress these high-quality input files in order to achieve a higher efficiency during processing. Recently, there has been a rising use of deep learning models to automate the processing step. For this, the engineers develop advanced deep learning models which allow the computer to learn the patterns. Finally, outputs are provided in classified forms provided after processing.

Some use cases of CV

Deep learning is a subset of machine learning. In computer vision, there’s a general use of an algorithm called neural network to extract patterns. We can think of neural network as the mathematical representation of the biological structure of brain which is driven by neuron and its receptors.

Neurons are fired inside our brains depending upon the provided input which helps reach a prediction on what we’ve seen. Neural networks use the same basic principle to provide judgement, which is an educated guess to what the image is.

There’s a variety of use cases for computer vision. For instance, we can see cellphones with an organized gallery where contents are grouped together into selfies, landscapes or foods. That’s called content organization, which uses computer vision as its backbone. Facial recognition is also being widely used.

Computer vision (CV) for augmented reality enables computers to obtain, process, analyze and understand digital videos and images. By looking at an object and its appearance, location, and the settings, it identifies what the object is. More simply, this is how Instagram recognizes your friends by photo tags, how you can log in into your bank account with your eyes, and how you can get yourself a flower crown on Snapchat.

Computers have learnt to distinguish between different people just by providing an image as an input. Facebook uses such a mechanism to suggest users during tagging. Tesla uses LiDAR sensors and cameras to create self-driving cars.

Computer vision as an application is on a rise. With varied applications being produced using the technology, we will surely see some further developments in the field.

Some real world uses of Computer Vision:

  • Image segmentation partitions an image into multiple regions or pieces to be examined separately.
  • Object detection identifies a specific object in an image. Advanced object detection recognizes many objects in a single image: a football field, an offensive player, a defensive player, a ball and so on. These models use an X,Y coordinate to create a bounding box and identify everything inside the box.
  • Facial recognition is an advanced type of object detection that not only recognizes a human face in an image, but identifies a specific individual.
  • Edge detection is a technique used to identify the outside edge of an object or landscape to better identify what is in the image.
  • Pattern detection is a process of recognizing repeated shapes, colors and other visual indicators in images.
  • Image classification groups images into different categories.
  • Feature matching is a type of pattern detection that matches similarities in images to help classify them.

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