  The SP theory of intelligence aims to simplify and integrate concepts in
computing and cognition, with information compression as a unifying theme. This
article discusses how it may be applied to the understanding of natural vision
and the development of computer vision. The theory, which is described quite
fully elsewhere, is described here in outline but with enough detail to ensure
that the rest of the article makes sense.
  Low level perceptual features such as edges or corners may be identified by
the extraction of redundancy in uniform areas in a manner that is comparable
with the run-length encoding technique for information compression.
  The concept of multiple alignment in the SP theory may be applied to the
recognition of objects, and to scene analysis, with a hierarchy of parts and
sub-parts, and at multiple levels of abstraction.
  The theory has potential for the unsupervised learning of visual objects and
classes of objects, and suggests how coherent concepts may be derived from
fragments.
  As in natural vision, both recognition and learning in the SP system is
robust in the face of errors of omission, commission and substitution.
  The theory suggests how, via vision, we may piece together a knowledge of the
three-dimensional structure of objects and of our environment, it provides an
account of how we may see things that are not objectively present in an image,
and how we recognise something despite variations in the size of its retinal
image. And it has things to say about the phenomena of lightness constancy and
colour constancy, the role of context in recognition, and ambiguities in visual
perception.
  A strength of the SP theory is that it provides for the integration of vision
with other sensory modalities and with other aspects of intelligence.
