Describes a graph-based approach to image processing, intended for use
with images obtained from sensors having space-variant (SV) sampling
grids. The connectivity graph (CG) is presented as a fundamental
framework for posing image operations in any kind of SV sensor. SV
sensor systems cover wide solid angles yet maintain high acuity in
their central regions. Implementation of SV systems pose at least two
outstanding problems. First, such a system must be active, in order to
utilize its high acuity region; second, there are significant image
processing problems introduced by the non-uniform pixel size, shape
and connectivity. Familiar image processing operations take on new and
different forms when defined on SV images. This paper provides a
general method for SV image processing, based on a CG which represents
the neighbor relations in an arbitrarily structured sensor. We
illustrate this approach with the following applications: (1)
Connected components are reduced to a graph-theoretic counterpart. (2)
We show how to write local image operators in the CG that are
independent of the sensor geometry. (3) We relate the CG to pyramids
over irregular tesselations, and implement a local binarization
operator in a 2-level pyramid. (4) We expand the CG into a
transformation graph, which represents the effects of geometric
transformations in SV image sensors. Using this, we define an
efficient algorithm for matching in the logmap images and solve the
template matching problem for SV images. The CG approach to image
processing is suitable for real-time implementation.