A computational model of the log-polar retina mosaic for local feature extraction

This paper presents insights gained from applying biological vision mechanisms in the context of computer vision algorithm design, implementation and initial evaluation. In this paper a software-based space variant log(z) retina tessellation is used to sample the underlying image, maintaining the high resolution at the central foveal region and an increasingly sparse sampling density at the surrounding peripheral region, in a manner similar to that found in biological vision. This multiresolution sampling strategy differs fundamentaly from conventional image processing where whole field-of-view is processed with equal emphasis. We compare the log(z) retina tessellation sampling quality using simple point based to that of “touching circle” receptive field (RF) sampling. Multi-resolution, space-variant visual information is extracted on a scale-space continuum and SIFT features are then extracted from back-projected retinal response images for matching and recognition.