Google researchers have created a machine learning system that adds color to black & white videos, and can also choose which specific objects, people and pets receive the color treatment. The technology is based on what’s called a convolutional neural network, which is architecturally suited for object tracking and video stabilization. Meanwhile, Nvidia has debuted an algorithm that slows down video, without the jitters, after it’s been captured, by using a neural network to create “in between” frames required for smooth motion. Continue reading Google, Nvidia Train Neural Networks to Post-Process Video

Nvidia debuted a deep learning method that can edit or reconstruct an image that is missing pixels or has holes via a process called “image inpainting.” The model can handle holes of “any shape, size, location or distance from image borders,” and could be integrated in photo editing software to remove undesirable imagery and replace it with a realistic digital image – instantly and with great accuracy. Previous AI-based approaches focused on rectangular regions in the image’s center and required post processing. Continue reading Nvidia’s New AI Method Can Reconstruct an Image in Seconds

Image recognition, or computer vision, is the foundation of new opportunities in everything from automotive to advertising. Its growing importance is such that the upcoming LDV Vision Summit, an annual conference on visual technology, is now in its third year. Computer vision has expanded through trends that have benefited other forms of AI, including open source, deep learning technology, easier programming tools and faster, cheaper computing, opening up opportunities for a wide range of businesses. Continue reading Image Recognition Tech Paving the Way for Future Advances