Computer vision is an exciting facet of the burgeoning artificial intelligence (AI) scene. We're already seeing this technology in action through modern facial recognition. But its potential goes beyond our current uses.

Simply put, computer vision models are processing blocks built to predict what it sees. Think of this technology as the computer-based equivalent of human eyesight. You go through life seeing a million things a day. The more you see and identify certain objects, the better you predict them later. For example, say that you grew up spending afternoons in a garden full of numerous rose varieties. Later in life, your years of seeing roses equip you with the knowledge to identify specific variants in places beyond where you spent your childhood.

The concept of computer vision works similarly. Computer vision models process data to learn. With a video annotation platform, it can pre-learn thousands of variables and labels before eventually learning to predict what a piece of visual medium contains independently.

How Computer Vision Works

Computer vision takes time to develop. It's the same as a child who can't identify what they see around them. That child learns through exposure.

Computer vision models need ample training. It's an application of machine learning, and models must process thousands of pieces of information before deployment. That's why using a video annotation platform is so crucial to developing these systems.

Video annotation is the educator. It labels all the variables you want the model to understand. From detecting specific objects to learning about localization and image concepts, annotation is key. Computer vision models must pre-learn the information it needs to identify, and annotation plays a critical role in that process.

The beauty of computer vision is that it continues to improve over time. The initial machine learning training process prepares it to make predictions. But the more accurate predictions it makes, the better it understands what it sees.

Many industries are taking advantage of computer vision models. It's useful in healthcare to accelerate research and improve diagnostic accuracy. In retail, it boosts the customer experience. The possibilities are endless, and technology will only get better.

Author Resource:-

Emily Clarke writes about tech for automated annotation, AI labeling, data evaluation and more. You can find her thoughts at AI solutions blog.