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Real-Life Computer Vision Applications

(Interlaken, Switzerland - Alvin Wei-Cheng Wong)


- AI and Computer Vision 

The method of perceiving images and movies in digital representation is called computer vision.  Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and video, and deep learning models, machines can accurately identify and classify objects - and then react to what they “see.”

Computer vision is one of the most compelling things in the world of deep learning and artificial intelligence. The contributions of deep learning to the field of computer vision really set the field apart.


- Computer Vision Models

Computer vision is used in machine learning (ML) and AI to train models to detect specific patterns and store the data in artificial memory, which can then be used to predict real-life outcomes. The goal of using computer vision techniques in machine learning and artificial intelligence is to build a model that works without human involvement. The entire process includes acquiring data, processing, analyzing and understanding digital images in order to use them in real-world settings.

A computer vision model is a software program that is trained to detect objects in images. The model learns to recognize a set of objects by first analyzing images of those objects through training. A computer vision model takes an image as input and outputs information about the objects it detects, such as the type of object and its location.

Computer vision in today's world, from recognizing faces to processing the live action of football games, computer vision can match or even surpass human vision in many fields.


- Building a Computer Vision Model

Data is the key to clearer computer vision. Building a simple computer vision model is not rocket science - you just need access to high-quality data and a solid training data platform to get started. To incorporate machine learning algorithms, a computer vision application requires massive quantities of data and significant computing power.

Computer vision is one of the hottest topics in artificial intelligence. But it's easy to get confused when trying to figure out the best way to learn and master the field. Don't get stuck on analytical theory concepts. Instead, combine your conceptual knowledge with practical experience and start building your own computer vision models! For example, 

  • People counting tool
  • Colors detection
  • Object tracking in a video
  • Pedestrian detection
  • Hand gesture recognition
  • Human emotion recognition
  • Road lane detection
  • Business card scanner
  • License plate recognition
  • Handwritten digit recognition
  • Iris Flowers Classification 
  • Family photo face detection
  • LEGO Brick Finder
  • PPE Detection
  • Face mask detection
  • Traffic light detection 



[More to come ...]



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