Humans are equipped with “space-variant” vision, i.e. a concentration of photoreceptors, retinal ganglion cells and other visual resources at the central fovea, and a sparser coverage of other regions within a wide 180 degree field of view. If the entire visual field was equipped with foveal ganglion cell resolution, then the brain would have to cope with approximately 350 times more visual information. We will review the relatively small number of existing hardware implementations and patents involving space variant vision. Space-variant vision is challenging to implement, because it comes along with distorted image representations, complicating standard geometry-based processing. Recent learning algorithms for feature detection and transformations are more flexible and may cope with foveated images. Foveated vision requires an active vision system: ballistic eye-movements termed “saccades” frequently move the fovea to points of interest in the visual field. The metric of saccades is adjustable, and the resolution increase at the fovea may play a role in supplying the feedback to the system. Furthermore, saccades are related to visual space perception and embodied vision.
Retina, space variant vision, camera, machine learning