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In general edges are considered to be abrupt changes or discontinuities in two dimensional image signal intensity
distributions. The accuracy of front-end edge detection methods in image processing impacts the eventual
success of higher level pattern analysis downstream. To generalize edge detectors designed from a simple ideal
step function model to real distortions in natural images, research on one dimensional edge pattern analysis to
improve the accuracy of edge detection and localization proposes an edge detection algorithm, which is composed
by three basic edge patterns, such as ramp, impulse, and step. After mathematical analysis, general rules for
edge representation based upon the classification of edge types into three categories-ramp, impulse, and step
(RIS) are developed to reduce detection and localization errors, especially reducing "double edge" effect that is
one important drawback to the derivative method.
But, when applying one dimensional edge pattern in two dimensional image processing, a new issue is naturally
raised that the edge detector should correct marking inflections or junctions of edges. Research on human visual
perception of objects and information theory pointed out that a pattern lexicon of "inflection micro-patterns" has
larger information than a straight line. Also, research on scene perception gave an idea that contours have larger
information are more important factor to determine the success of scene categorization. Therefore, inflections or
junctions are extremely useful features, whose accurate description and reconstruction are significant in solving
correspondence problems in computer vision. Therefore, aside from adoption of edge pattern analysis, inflection
or junction characterization is also utilized to extend traditional derivative edge detection algorithm. Experiments
were conducted to test my propositions about edge detection and localization accuracy improvements. The results
support the idea that these edge detection method improvements are effective in enhancing the accuracy of edge
detection and localization.
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