  This paper proposes and illustrates a general framework to integrate the
areas of vision research and complex networks. Each image pixel is associated
to a network node and the Euclidean distance between the visual properties
(e.g. gray-level intensity, color or texture) at each possible pair of pixels
is assigned as the respective edge weight. In addition to investigating the
therefore obtained weight and adjacency matrices in terms of node degree
densities, it is shown that the combination of the concepts of network hub and
\emph{2-}expansion of the adjacency matrix provides an effective means to
separate the image elements, a challenging task in computer vision known as
segmentation.
