Orthogonal Moment Features for Use With Parametric and Non-Parametric Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Supervised Training Based Hand Gesture Recognition System
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Multimodal human-computer interaction: A survey
Computer Vision and Image Understanding
Real-time hand posture recognition using range data
Image and Vision Computing
A curvature estimation for pen input segmentation in sketch-based modeling
Computer-Aided Design
Methodological foundation for sign language 3d motion trajectory analysis
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Automatic recognition of object size and shape via user-dependent measurements of the grasping hand
International Journal of Human-Computer Studies
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For an effective vision-based HCI system, inference from natural means of sources (i.e. hand) is a crucial challenge in unconstrained environment. In this paper, we have aimed to build an interaction system through hand posture recognition for static finger spelling American Sign Language (ASL) alphabets and numbers. Unlike the interaction system based on speech, the coarticulation due to hand shape, position and movement influences the different aspects of sign language recognition. Due to this, we have computed the features which are invariant to translation, rotation and scaling. Considering these aspects as the main objectives of this research, we have proposed a three-step approach: first, features vector are computed using two moment based approaches namely Hu-Moment along with geometrical features and Zernikemoment. Second, the categorization of symbols according to the fingertip is performed to avoidmis-classification among the symbols. Third, the extracted set of two features vectors (i.e. Hu-Moment with geometrical features and Zernike moment) are trained by Support Vector Machines (SVM) for the classification of the symbols. Experimental results of the proposed approaches achieve recognition rate of 98.5% using Hu-Moment with geometrical features and 96.2% recognition rate using Zernikemoment for ASL alphabets and numbers demonstrating the dominating performance of Hu-Moment with geometrical features over Zernike moments.