Scale-space recognition based on the retino-cortical transform

Visual transformations are often employed to achieve size and rotation invariances in computational approaches to vision. Nature appears to have adopted a similar strategy: research workers studying biological vision systems have known for over a decade that a particular spatial transformation appears to be applied to the visual signals that are conveyed between the retina and the primary visual cortex in mammals. This transformation, the retino-cortical (RC) transform, has the property of re-mapping visual information to remove the effects of scale, 2D rotation and certain projective distortions. In addition, this general class of transformations maps a dilating optical flow-field, i.e. a centred focus-of-expansion, to a translating flow-field. Although the RC transform has been known for some time, and is recognised as being potentially useful in machine vision applications, this potential has not yet been fully realised in practical vision system implementations. This paper reports an attempt to combine the invariant properties of the RC transform with a scale-space approach to image representation and image matching to develop an anthropomorphic visual recognition architecture