Orthogonal Moment Features for Use With Parametric and Non-Parametric Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical color models with application to skin detection
International Journal of Computer Vision
An Appearance-Based Framework for 3D Hand Shape Classification and Camera Viewpoint Estimation
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Gesture Recognition via Pose Classification
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Sketched Symbol Recognition using Zernike Moments
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Visual Hand Tracking Using Nonparametric Belief Propagation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Automatic 2D Hand Tracking in Video Sequences
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Parametric Hand Tracking for Recognition of Virtual Drawings
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Zernike velocity moments for sequence-based description of moving features
Image and Vision Computing
An HMM based gesture recognition for perceptual user interface
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
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Hand-gesture recognition presents a challenging problem for computer vision due to the articulated structure of the human hand and the complexity of the environments in which it is typically applied. Solving such a problem requires a robust tracking mechanism which in turn depends on an effective feature descriptor and classifier. Moment invariants, as discriminative feature descriptors, have been used for shape representation for many years. Zernike moments have been particularly attractive for their scale, translation and rotation invariance. More recently, Zernike moments have been extended to a spatio-temporal descriptor, known as the Zernike velocity moment, through combining with the displacement vector of the centre of mass of the target object between video frames. This descriptor has hitherto been demonstrated successfully in human gait analysis. In this paper, we introduce and evaluate the application of Zernike velocity moments in hand-gesture recognition, and compare with a bank of hidden Markov models with Zernike moments as observations. We demonstrate good performance for both approaches, with a substantial increase in performance for the latter method.