It started with Mars

Imagine a rover hurtling at the surface of Mars with no communication. How does an autonomous craft pick a safe and productive landing spot in less than seven minutes? That dilemma is at the heart of NeuralEye. How do you get a machine to see and react like a human when there is no opportunity for training or mistakes? When conventional methodologies won’t get you there, you have to rethink your fundamental approach.

Dr. Tuan Duong and his team at Caltech/JPL invented a new computer vision science inspired by the mechanics of human sight and analysis. This revolutionary approach and the next generation recognition capabilities that derive from it are the foundation of NeuralEye technology.

Unsupervised Learning
Low Resolution Image Recognition

Our Core Recognition Engine is content-agnostic, built on purely correlative algorithms that mimic the human ability to extract sense from input. These algorithms are covered by patents. NeuralEye continues to patent additional inventions that separate signal from noise with the greatest processing economy.

NeuralEye successfully recognizes images that cause conventional systems to fail. While most image recognition technologies require a good resolution, straight-on shot of the entire object or face, NeuralEye succeeds with a broad range of images – low resolution, angled, partial, etc. Nowhere is this more important than in Facial Recognition. NeuralEye leads the way in unconstrained facial recognition.