Hand gesture recognition by analysis of codons

Poornima Ramachandra; Neelima Shrikhande

doi:10.1117/12.733193

10 September 2007 Hand gesture recognition by analysis of codons

Poornima Ramachandra, Neelima Shrikhande

Proceedings Volume 6764, Intelligent Robots and Computer Vision XXV: Algorithms, Techniques, and Active Vision; 67640M (2007) https://doi.org/10.1117/12.733193
Event: Optics East, 2007, Boston, MA, United States

Abstract

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.

Citation Download Citation

Poornima Ramachandra and Neelima Shrikhande "Hand gesture recognition by analysis of codons", Proc. SPIE 6764, Intelligent Robots and Computer Vision XXV: Algorithms, Techniques, and Active Vision, 67640M (10 September 2007); https://doi.org/10.1117/12.733193

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KEYWORDS

Gesture recognition

Image segmentation

Databases

Analytical research

Sensors

3D modeling

Edge detection

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