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Computer vision is a domain of computer science that works on facilitating computers to identify and process images as human visualization and imagination do, and then provide output. Computer vision technology is predominantly found, in game consoles that can recognize gestures to cell phone cameras that can automatically set focus on people.
• Early Application
The surveillance sector is one of the early adopters of image processing techniques and video analytics. The ability to automatically detect predefined patterns in real situations represents a vast opportunity with several use cases. Video analytics tools use algorithms that identify specific features in images and videos which are accurate in laboratory settings and simulation environments.
• Deep Learning
The evolution of deep learning algorithms has revitalized computer vision leveraging Artificial Neural Networks algorithms that mimic the neurons of the human brain. Researchers have several libraries of video and image data to train their neural networks besides partly fueled by video sites and IoT devices. A new version of Deep Neural Network demonstrated a huge leap in accuracy, which drove renewed interest into the field of computer vision engagement. Deep learning algorithm has exceeded human counterparts in applications requiring image classification and facial recognition today. With deep learning, an era of cognitive technology is approaching where computer vision and deep learning integrate to address high-level problems.
These are just some illustrations of how computer vision can assist significantly in productivity in many domains. With this the world is entering the new age of the Internet of Things evolution, where the focus from connecting devices and building data platforms will be shifted, to making things more intelligent through technologies like computer vision and deep learning, providing more actionable data. The computer vision industry is moving towards tailoring a generic framework that solves computer vision related challenges for specific domains.