Here is a mixed collection of sales and academic presentations on the topic of machine learning for embedded vision. Where available, descriptions are provided from the web source.
Real time image processing is absolutely critical for most autonomous systems, and understanding current capabilities is important in developing new applications for business and in consumer technology. Deep learning is proving essential to making these systems work.
Visual Intelligence in Computers – Fei Fei Li (Stanford Vision Lab)
As director of one of the top Artificial Intelligence (AI) labs in the world, Dr. Fei-Fei Li is leading the next wave of AI that is rapidly being integrated into companies, governments and the lives of individual consumers. The way we work, drive, entertain and live our lives will never be the same. Dr. Li heads a team of the world’s top scientists and students who enable computers and robots to see and think, as well as conduct cognitive and neuroimaging experiments to understand how our brains function. She is a world-renowned expert on computer vision, machine learning, artificial intelligence, cognitive neuroscience and big data analytics. She directs both the Stanford Artificial Intelligence Lab (SAIL) and the Stanford Vision Lab, as well as teaching Computer Science at Stanford University.
CEVA’s Jeff VanWashenova interviewed at AutoSens 2016 (AutoSens)
CEVA is a leader in developing DSP technologies for image recognition. Here, Alex Lawrence-Berkeley interviews CEVA’s Jeff VanWashenova at AutoSens 2016, held at AutoWorld in Brussels, Belgium.
Computer Vision System Toolbox Overview (MATLAB)
Design and simulate computer vision and video processing systems using Computer Vision System Toolbox™. The Toolbox provides algorithms, functions, and apps for designing and simulating computer vision and video processing systems. You can perform feature detection, extraction, and matching; object detection and tracking; motion estimation; and video processing. For 3-D computer vision, the system toolbox supports camera calibration, stereo vision, 3-D reconstruction, and 3-D point cloud processing. With machine learning based frameworks, you can train object detection, object recognition, and image retrieval systems.
Movidius Demonstration of Its Machine Intelligence Technology (Embedded Vision Alliance)
Jack Dashwood, Marketing Communications Director at Movidius, demonstrates the company’s latest embedded vision technologies and products at the May 2016 Embedded Vision Summit. Specifically, Dashwood demonstrates the Fathom neural compute framework, running an image classifier in an ultra-low power embedded environment (under 1W), and enabling a whole new class of miniature robot overlords of which to be fearful.