Paper Info
Title
Segregation Of Meaningful Strokes, A Pre-Requisite For Self Co-Articulation Removal In Isolated Dynamic Gestures
Abstract
Gesture formation, a pre-processing step, has its importance when variations in patterns, scale, and speed come into play. Self co-articulations are intentional movements performed by an individual to complete a gesture, whose presence in the trajectory alters its original meaning. For recognition, most researchers have directly used the trajectory formed along with these self co-articulated strokes, with a few removing it using visible trait-like velocity. Usage of velocity has shortcomings as gesturing in air differs from gesturing over a solid surface; hence, we propose a gesture formation model, which incorporates global and local measures to remove these self co-articulations. The global measure uses Euclidean distance, instantaneous velocity, and polarity calculated from the complete gesture, while the local measure segments the gesture into stroke-level segments by using the minimum-maximum-polarity algorithm and applies the selective bypass rules. The proposed model, when experimented on gestures patterns with premeditated speed variation, has a mean error rate of 0.0069 and 7.40% self co-articulations;individuals' natural gesticulation has a mean error rate of 0.0371 and 12.07% self co-articulations. Experimentation on each gesture of NITS hand gesture databases showed a relative improvement of 40% (accuracy 97%) over the existing baseline models.
Year
DOI
Venue
2021
10.1049/ipr2.12095
IET IMAGE PROCESSING
DocType
Volume
Issue
Journal
15
5
ISSN
Citations 
PageRank 
1751-9659
1
0.35
References 
Authors
0
6
Name
Order
Citations
PageRank
K. Anish Monsley110.35
Kuldeep Yadav242.08
Songhita Misra310.35
Taimoor Khan410.69
M. K. Bhuyan510.35
R. H. Laskar617622.70