Used by companies, organizations, and even individuals to
promote recognition of their brand, logos can also act as a
valuable means of identifying the source of a document. E-business
applications can retrieve and catalog products according to their
logos. Governmental agencies can easily inspect goods using smart
mobile devices that use logo recognition techniques. However,
because logos are two-dimensional shapes of varying complexity, the
recognition process can be challenging. Although promising results
have been found for clean logos, they have not been as robust for
noisy logos.
Logo Recognition: Theory and Practice is the first book to focus
on logo recognition, especially under noisy conditions. Beginning
with an introduction to fundamental concepts and methods in pattern
and shape recognition, it surveys advances in logo recognition. The
authors also propose a new logo recognition system that can be used
under adverse conditions such as broken lines, added noise, and
occlusion.
The proposed system introduces a novel polygonal approximation,
a robust indexing scheme, and a new line segment Hausdorff distance
(LHD) matching method that can handle more distortion and
transformation types than previous techniques. In the first stage,
raw logos are transformed into normalized line segment maps. In the
second stage, effective line pattern features are used to index the
database to generate a moderate number of likely models. In the
third stage, an improved LHD measure screens and generates the best
matches. A comprehensive overview of logo recognition, the book
also presents successful applications of the technology and
suggests directions for future research.
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