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One of the very recent results of the group is a method
called shape metamorphosis for object recognition (signatures, faces,
cursive script, etc). Shape metamorphosis is a new physics-based technique
that performs accurate recognition of semi-rigid or deformable objects
(minimum training is required). The innovation of the approach is that it
involves a segmentation method that locates corners and some key low
curvature points. Due to this segmentation strategy, the proposed method
can handle collectively cursive words, hand-drawn line figures,
signatures, and deformable objects. The proposed system uses to its
advantage the intuitive fact that two shapes (test and reference) which
are similar don't have to go through an extensive metamorphosis in order
for one to assume the shape of the other. Thus, the degree of morphing
between a test shape and a reference shape can be used as a shape-matching
criterion. The proposed recognition paradigm is invariant to translation,
rotation, and scaling. The recognition results in large image databases
are impressive. |
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Automatic signature verification is a well-established and active research
area with numerous applications. In contrast, automatic signature
identification has been given little attention although there is a vast
array of potential applications that could use the signature as an
identification tool (ATM access, check verification, etc).
Papanikolopoulos' work presents a novel approach to the problem of
automatic signature identification. In contrast to traditional approaches
that are based exclusively in an on-line or off-line system, the proposed
approach uses a hybrid system. Methods based on hybrid systems not only
have a definite advantage in terms of information quantity and diversity
but they are also conveniently supported by technological advances in the
area of hand-held communicators. The proposed method capitalizes upon the
conclusions of past experimental efforts that the majority of signers have
distinctive and stable signature patterns. Instead of a one-step
processing model, it adopts a pyramidal processing technique by utilizing
both a general and a detailed structure processing module. The general
structure module has already been implemented. It deals successfully and
efficiently with the bulk of the signature patterns and is intended to
yield to the detailed structure module (a database of 600 signatures is
used). The method deals successfully and efficiently with the bulk of the
signature patterns and is intended to yield to the detailed structure
module only for the problematic cases. It features a parallel
physics-based algorithm, the revolving active deformable model, which
affords significant discriminating power. The detailed structure module
is almost complete. Its perspective role is to analyze the difficult
signature images in sufficient detail by introducing the use of image
metamorphosis as a powerful discriminating tool of highly variable
patterns. Image metamorphosis is being implemented by following a
physics-based approach. Initial experimental results show recognition
rates around 85% in the database of 600 signatures.
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