Computer Vision

A Case Study

Computer Vision

Computer Vision

Computer vision is the magic that allows computers to see the world around them. It’s the technology that powers self-driving cars, facial recognition software, and even the filters on your social media photos. Computer vision is rapidly growing, and it’s already having a major impact on our lives.
Self-driving cars, Facial Recognition, Manufacturing, Healthcare, and retail are the areas that are getting improved day by day with the help of Computer Vision, these are just a few of the many ways that computer vision is being used today. As the technology continues to develop, we can expect to see even more innovative and exciting applications in the future.

(1). Our Vision

  • Make computer vision affordable and scalable
  • Accelerated and accurate decision making
  • Enterprise wide-implementation
  • Adoptable and explainable computer vision
  • Hardware agnostic solutions

(2). Computer Vision Involves

  • Object Classification: What broad category of object is in this photograph?
  • Object Identification: Which type of a given object is in this photograph?
  • Object Verification: Is the object in the photograph?
  • Object Detection: Where are the objects in the photograph?
  • Object Landmark Detection: What are the key points for the object in the photograph?
  • Object Segmentation: What pixels belong to the object in the image?
  • Object Recognition: What objects are in this photograph and where are they?

(3). Computer Vision Basic Function

  • Optical Character Recognition(OCR)
  • Retail automation(personalised search)
  • Machine inspection
  • 3D model building(photogrammetry)
  • Medical imaging
  • Match move(e.g. merging CGI with live actors in movies)
  • Motion capture(Mocap)
  • Surveillance
  • Automative safety
  • Fingerprint recognition and biometrics

(4). How does Computer Vision work?

○ Image acquisition:

This involves capturing images or videos of the scene or object that you want to analyze.

○ Preprocessing the image:

It involves cleaning up the images or videos, such as removing noise and adjusting the brightness and contrast.

○ Feature extraction:

This involves identifying the features of the objects or scenes in the images or videos

○ Classification:

This involves assigning a label to each object or scene based on its features.

○ Interpretation:

This involves understanding the meaning of the labels and making decisions based on them.


(5). Use-Cases

○ Marketing :
  • Retargeting
  • Recommendation personalize
  • Social analytics & automation
○ Sales :
  • Predictive sales
  • Sales data input automation
  • Sales forecasting
○ Healthtech :
  • Patient data analytics
  • Personalized medications and care
  • Image segmentation of scans
○ IT :
  • Analytics platform
  • Natural Language Processing
  • Analytics and predictive intelligence for security
○ Operations :
  • Robotic process automation
  • Predictive maintenance
  • Manufacturing analytics
○ Fintech :
  • Fraud detection
  • Financial analytics platform
  • KYC verification