Please use this identifier to cite or link to this item:
|Title:||Graph matching based hand posture recognition using neuro-biologically inspired features||Authors:||Kumar P, P.
|Keywords:||Biologically inspired features
Hand posture recognition
|Issue Date:||2010||Citation:||Kumar P, P.,Vadakkepat, P.,Poh, L.A. (2010). Graph matching based hand posture recognition using neuro-biologically inspired features. 11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010 : 1151-1156. ScholarBank@NUS Repository. https://doi.org/10.1109/ICARCV.2010.5707352||Abstract:||An elastic graph matching algorithm using biologically inspired features is proposed for the recognition of hand postures. Each node in the graph is labeled using an image feature extracted using the computational model of the ventral stream of visual cortex. The graph nodes are assigned to geometrically significant positions in the hand image, and, the model graphs are created. Bunch graph method is used for modeling the variability in hand posture appearance. Recognition of a hand posture is done by the elastic graph matching between the model graphs and the input image. A radial basis function is used as the similarity function for the matching process. The proposed algorithm is tested on a 10 class hand posture database which consists of 478 grey scale images with light and dark backgrounds. The algorithm provided better recognition accuracy (96.35%) compared to the reported results (93.77%) in the literature. ©2010 IEEE.||Source Title:||11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010||URI:||http://scholarbank.nus.edu.sg/handle/10635/70438||ISBN:||9781424478132||DOI:||10.1109/ICARCV.2010.5707352|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 25, 2020
checked on Oct 24, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.