Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Tracking via Level Set PDEs without Motion Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real time hand tracking by combining particle filtering and mean shift
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
IEEE Transactions on Image Processing
Active contours for tracking distributions
IEEE Transactions on Image Processing
Combining shape prior and statistical features for active contour segmentation
IEEE Transactions on Circuits and Systems for Video Technology
Gesture Recognition Based on Elastic Deformation Energies
Gesture-Based Human-Computer Interaction and Simulation
Hi-index | 0.00 |
This paper presents active contours based method for hand tracking using color information. The main problem in active contours based approach is that results are very sensitive to location of the initial curve. Initial curve far form the object induces more heavy computational cost, low accuracy of results, as well as missing the object that has a large movement. Therefore, this paper presents a hand tracking method using a mean shift algorithm and active contours. The proposed method consists of two steps: hand localization and hand extraction. In the first step, the hand location is estimated using mean shift. And the second step, at the location, evolves the initial curve using an active contour model. To assess the effectiveness of the proposed method, it is applied to real image sequences which include moving hand.