Experimental evaluation demonstrates 86.2% hand posture classification accuracy across complex grey-level backgrounds using elastic graph matching, indicating robust visual recognition.
A system for the classification of hand postures against complex backgrounds in grey-level images is presented. The system employs elastic graph matching, which has already been successfully employed for the recognition of faces. Our system reaches 86.2% correct classification on our gallery of 239 images of ten postures against complex backgrounds. The system is robust with respect to certain variations in size of hand and shape of posture.
No takes yet. Share an insight, caveat, or question.
Triesch et al. (2002) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: