Open AccessPerformance comparison of segmentation algorithms for hand gesture recognition
Author(s) -
Priyanka Parvathy D,
Kamalraj Subramaniam
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.12842
Subject(s) - artificial intelligence , computer science , pattern recognition (psychology) , preprocessor , canny edge detector , segmentation , image segmentation , computer vision , gesture recognition , gesture , edge detection , image (mathematics) , image processing
The gestures presented in diverse backgrounds have to be accurately processed and segmented, for it to be classified precisely by the hand gesture recognition system. This study compares performance of the proposed Image Segmentation Algorithm with a standard Canny Edge Detection Algorithm by comparing the statistical values of the features obtained from the feature extraction stage, thus validating the importance of having a robust preprocessing stage for the hand gestures. The proposed algorithm uses Non-local Mean filter for noise removal and then an improved Global Swarm Optimization based Canny edge detection for extracting the edges. Features are extracted using two dimensional Multi-resolution Discrete Wavelet Transform (2D-DWT) combined with Gray-level Co-occurrence Matrix. The efficiency of the proposed Image Segmentation Algorithm is evaluated using Radial Basis Function Neural Network as the classifier.