Understanding Neuromorphic Image Sensing

Using neuromorphic computing to reduce inefficiency in image processing by focusing only on the dynamic areas of the image being processed. Using a technique called level-crossing sampling researchers can concentrate on the interesting dynamic part of the images similar to how the human eye and brain process the real world. This leads to efficiencies in processing as whole frames are no longer processed and processing resources can concentrate on areas of movement. This has the advantage of higher resolution of detailed movements an example being the detailed arm movement of a baseball pitcher