#computer_vision

Computer Vision with deep learning is another advanced technique that employs neural networks to process and analyze visual data, such as images and videos. With deep learning, Computer Vision has become more accurate and sophisticated, enabling machines to perform highly accurate tasks such as object detection, image recognition, facial recognition, and scene segmentation. CNNs are the most commonly used deep learning models in Computer Vision because they can extract features from images and learn spatial relationships between objects.

Computer Vision applications based on deep learning have numerous practical applications in various industries, including healthcare, automotive, retail, and security. Some examples include self-driving cars, medical image analysis, surveillance systems, and augmented reality. The advancements in deep learning have enabled machines to understand the world through visual data, making computer vision an essential component in developing intelligent systems.

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Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.

New work from the same scientists at MIT CSAIL who recovered sound from video of movements so small you cannot see it with the naked eye:

Video magnification reveals subtle variations that would be otherwise invisible to the naked eye. Current techniques require all motion in the video to be very small, which is unfortunately not always the case. Tiny yet meaningful motions are often combined with larger motions ...

Here's the paper (PDF): Video Magnification in Presence of Large Motions

Here's another video with more examples: