Mind, Body, World- Foundations of Cognitive Science, 2013a

was a low-level interpretation that was guided by knowledge, and they argued that
the more knowledge the better.
Barrow and Tenenbaum’s (1975) review described a New Look within computer
vision:
Higher levels of perception could involve partitioning the picture into ‘meaningful’
regions, based on models of particular objects, classes of objects, likely
events in the world, likely configurations, and even on nonvisual events. Vision
might be viewed as a vast, multi-level optimization problem, involving a
search for the best interpretation simultaneously over all levels of knowledge.
(Barrow & Tenenbaum, 1975, p. 2)
However, around the same time a very different data-driven alternative to computer
vision emerged (Waltz, 1975).
Waltz’s (1975) computer vision system was designed to assign labels to regions
and line segments in a scene produced by drawing lines and shadows. “These labels
describe the edge geometry, the connection or lack of connection between adjacent
regions, the orientation of each region in three dimensions, and the nature of the
illumination for each region” (p. 21). The goal of the program was to assign one and
only one label to each part of a scene that could be labelled, except in cases where a
human observer would find ambiguity.
Waltz (1975) found that extensive, general knowledge of the world was not
required to assign labels. Instead, all that was required was a propagation of local
constraints between neighbouring labels. That is, if two to-be-labelled segments
were connected by a line, then the segments had to be assigned consistent labels.
Two ends of a line segment could not be labelled in such a way that one end of the
line would be given one interpretation and the other end a different interpretation
that was incompatible with the first. Waltz found that this approach was very
powerful and could be easily applied to novel scenes, because it did not depend on
specialized, scene-specific knowledge. Instead, all that was required was a method
to determine what labels were possible for any scene location, followed by a method
for comparisons between possible labels, in order to choose unique and compatible
labels for neighbouring locations.
The use of constraints to filter out incompatible labels is called relaxation
labelling (Rosenfeld, Hummel, & Zucker, 1976); as constraints propagate through
neighbouring locations in a representation, the representation moves into a stable,
lower-energy state by removing unnecessary labels. The discussion of solving
Sudoku problems in Chapter 7 illustrates an application of relaxation labelling.
Relaxation labelling proved to be a viable data-driven approach to dealing with
visual underdetermination.
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