This book deals with novel machine vision architecture ideas that make
real-time projection-based algorithms a reality. The design is founded
on raster-mode processing, which is exploited in a powerful and flexible
pipeline. We concern ourselves with several image analysis algorithms
for computing: projections of gray-level images along linear patterns
(i. e., the Radon transform) and other curved contours; convex hull
approximations; the Hough transform for line and curve detection;
diameters; moments and principal components, etc. Addition- ally, we
deal with an extensive list of key image processing tasks, which involve
generating: discrete approximations of the inverse Radon transform
operator; computer tomography reconstructions; two-dimensional
convolutions; rotations and translations; multi-color digital masks; the
discrete Fourier transform in polar coordinates; autocorrelations, etc.
Both the image analysis and image processing algorithms are supported by
a similar architecture. We will also of some of the above algorithms to
the solution of demonstrate the applicability various industrial visual
inspection problems. The algorithms and architectural ideas surveyed
here unleash the power of the Radon and other non-linear transformations
for machine vision applications. We provide fast methods to transform
images into projection space representa- tions and to backtrace
projection-space information into the image domain. The novelty of this
approach is that the above algorithms are suitable for implementa- tion
in a pipeline architecture. Specifically, random access memory and other
dedicated hardware components which are necessary for implementation of
clas- sical techniques are not needed for our algorithms.