Author/Authors
abadi, wassila badji mokhtar university - electronics departement, Annaba, Algeria , hamdi, rachid badji mokhtar university - electronics departement, Annaba, Algeria , fezari, mohamed badji mokhtar university - electronics departement, Annaba, Algeria
Title Of Article
Hand posture recognition using HOW and homogenous kernel
شماره ركورد
42785
Abstract
In this paper, we present a static hand gestures recognition system of Arabic Sign Language alphabets. The proposed method uses Histogram Of visual Words (HOW) descriptor and Support Vector Machine (SVM). First, the images of static hand gestures are converted into HOW features and grouped using k-means clustering to create histograms. Then they are converted from non linear space into linear space using Chi-squared kernel. The result is fed into One-vs-All SVM classifier to build signs models. Training and test stages of this technique are implemented on hand postures images using cluttered backgrounds for different lighting conditions, scales and rotations. The proposed method shows a satisfactory recognition rate and achieves good real-time performance regardless of the image resolution.
From Page
125
NaturalLanguageKeyword
arabic sign language , static hand gesture – how , one , vs , all support vector machine (svm)
JournalTitle
Revue Des Sciences Et De La Technologie, Synthèse
To Page
133
JournalTitle
Revue Des Sciences Et De La Technologie, Synthèse
Link To Document