The problem of recognizing gestures from images using computers can be approached by closely understanding
how the human brain tackles it. A full fledged gesture recognition system will substitute mouse and keyboards
completely. Humans can recognize most gestures by looking at the characteristic external shape or the silhouette of the
fingers. Many previous techniques to recognize gestures dealt with motion and geometric features of hands. In this thesis
gestures are recognized by the Codon-list pattern extracted from the object contour. All edges of an image are described
in terms of sequence of Codons. The Codons are defined in terms of the relationship between maxima, minima and
zeros of curvature encountered as one traverses the boundary of the object. We have concentrated on a catalog of 24
gesture images from the American Sign Language alphabet (Letter J and Z are ignored as they are represented using
motion) [2]. The query image given as an input to the system is analyzed and tested against the Codon-lists, which are
shape descriptors for external parts of a hand gesture. We have used the Weighted Frequency Indexing Transform
(WFIT) approach which is used in DNA sequence matching for matching the Codon-lists. The matching algorithm
consists of two steps: 1) the query sequences are converted to short sequences and are assigned weights and, 2) all the
sequences of query gestures are pruned into match and mismatch subsequences by the frequency indexing tree based on
the weights of the subsequences. The Codon sequences with the most weight are used to determine the most precise
match. Once a match is found, the identified gesture and corresponding interpretation are shown as output.
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