@Article{schwartz1995:space-variant,
  author =       {Eric Schwartz and Douglas Greve and Giorgio
                  Bonmassar},
  title =        {Space-variant active vision: Definition, overview
                  and examples},
  journal =      {Neural Networks},
  year =         1995,
  volume =       8,
  number =       {7/8},
  pages =        {1297--1308},
  DOI =          {10.1016/0893-6080(95)00092-5},
  PII =          {0893-6080(95)00092-5},
  INSPEC =       5174316,
  datestr =      1995011,
  abstract =     {The term space-variant vision was introduced in the
                  late 1980s to refer to sensor architectures based on
                  a smooth variation of resolution across the
                  workspace, like that of the human visual system. The
                  use of such sensor architectures is rapidly becoming
                  an important factor in machine vision in which the
                  constraints of size, weight, cost and performance
                  must be jointly optimized. The structure of this
                  paper consists of four parts. A review of the four
                  generic architectures for vision will be presented,
                  providing a context for the term "active vision",
                  and a justification for the importance, and the
                  connection between, space-variant architectures and
                  active vision methods. A brief quantitative review
                  of the specific space-variant properties of primate
                  visual cortex topography will be provided, in the
                  context of sensor design. The engineering and
                  algorithmic problems that are associated with
                  exploiting space-variant systems will be
                  stated. Examples of several recently constructed
                  miniature space-variant active vision systems will
                  be briefly reviewed, along with a brief discussion
                  of solutions to the basic problem areas in
                  space-variant vision.},
  keywords =     {Fovea; Space-varient, Active vision, Pyramid,
                  Computer vision, Visual cortex}
}