Understanding Computer Vision in the Real World
With the increasing power of computers and also the much larger sample size of images currently available, computer vision has flourished. Used in tools like facial recognition, it can be used to identify specific people and while early tests had an accuracy of 50%, with the amount of data now available, that number has been far surpassed.
Today, computer vision is used in many different applications and areas. We can apply it in our online searches for example to find a specific breed of dog or it can enhance details on images, helping us zoom in and understand what would have once only been pixels.
Of course, the most common use of computer vision is facial recognition. At its most benign it can be used as a tool to unlock smartphones and automatically detect people in your uploaded social media snaps. Facial recognition does have some privacy implications however, and these should not be discounted.
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How Computer Vision Works in the World of AI
Computer vision is all about patterns and pattern recognition. By providing the underlying software with visual data, it learns to understand and extrapolate. It is critical that the data used is correct and labeled appropriately. If for example millions of cat pictures were provided to the algorithm but they were actually picturing dogs, the system would not be able to correctly identify a cat in the future.
Computer vision can be paired with AI in many areas and one of the most promising is healthcare. Doctors and hospitals are now using the technology to find different types of cancer at earlier stages, helping to save lives.
Another use case for the partnership of computer vision and AI is the autonomous vehicle. Self-driving cars need to see what is all around and understand the difference between a parked car and one that is moving. By using computer vision, autonomous vehicles are able to analyze the information from cameras all over the vehicle and make appropriate decisions.