Discrete cosine transform: algorithms, advantages, applications
Discrete cosine transform: algorithms, advantages, applications
Task-Specific Gesture Analysis in Real-Time Using Interpolated Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
An HMM-Based Threshold Model Approach for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solution of the matrix equation AX + XB = C [F4]
Communications of the ACM
Dictionary learning algorithms for sparse representation
Neural Computation
Vision-Based Gesture Recognition: A Review
GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
Gesture Modeling and Recognition Using Finite State Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Recognition-based gesture spotting in video games
Pattern Recognition Letters
Enabling fast and effortless customisation in accelerometer based gesture interaction
Proceedings of the 3rd international conference on Mobile and ubiquitous multimedia
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
An Implementation of Multi-Modal Game Interface Based on PDAs
SERA '07 Proceedings of the 5th ACIS International Conference on Software Engineering Research, Management & Applications
IEEE Transactions on Computers
Gesture recognition with a Wii controller
Proceedings of the 2nd international conference on Tangible and embedded interaction
Hand gesture recognition and virtual game control based on 3D accelerometer and EMG sensors
Proceedings of the 14th international conference on Intelligent user interfaces
Detecting gesture force peaks for intuitive interaction
IE '08 Proceedings of the 5th Australasian Conference on Interactive Entertainment
Gesture Recognition with a 3-D Accelerometer
UIC '09 Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing
A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries in Wavelet Domain
ICIG '09 Proceedings of the 2009 Fifth International Conference on Image and Graphics
Recognition and segmentation of 3-d human action using HMM and multi-class adaboost
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
IEEE Transactions on Robotics
Gesture Spotting and Recognition for Human–Robot Interaction
IEEE Transactions on Robotics
Enabling Multimodal Human–Robot Interaction for the Karlsruhe Humanoid Robot
IEEE Transactions on Robotics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A comparison of 3D hand gesture recognition using dynamic time warping
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
SAPHE: simple accelerometer based wireless pairing with heuristic trees
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
Hidden Markov Model on a unit hypersphere space for gesture trajectory recognition
Pattern Recognition Letters
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Hand gesture recognition has been intensively applied in various human-computer interaction (HCI) systems. Different hand gesture recognition methods were developed based on particular features, e.g., gesture trajectories and acceleration signals. However, it has been noticed that the limitation of either features can lead to flaws of a HCI system. In this paper, to overcome the limitations but combine the merits of both features, we propose a novel feature fusion approach for 3D hand gesture recognition. In our approach, gesture trajectories are represented by the intersection numbers with randomly generated line segments on their 2D principal planes, acceleration signals are represented by the coefficients of discrete cosine transformation (DCT). Then, a hidden space shared by the two features is learned by using penalized maximum likelihood estimation (MLE). An iterative algorithm, composed of two steps per iteration, is derived to for this penalized MLE, in which the first step is to solve a standard least square problem and the second step is to solve a Sylvester equation. We tested our hand gesture recognition approach on different hand gesture sets. Results confirm the effectiveness of the feature fusion method.