Robust recognition of scaled shapes using pairwise geometric histograms
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Real-Time 3D Object Recognition for Automatic Tracker Initialization
ISAR '01 Proceedings of the IEEE and ACM International Symposium on Augmented Reality (ISAR'01)
Scale-based integrated microscopic computer vision techniques for micromanipulation and microassembly
Towards real-time object recognition using pairs of lines
Real-Time Imaging
Linear model hashing and batch RANSAC for rapid and accurate object recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Adaptive learning of an accurate skin-color model
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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3D tracking is of fundamental importance to the development of practical visual applications to micromanipulation and microassembly. In this paper, a new 3D tracking method based on CAMSHIFT and Depth-from-Defocus is developed for micromanipulation and microassembly task. CAMSHIFT algorithm is used to find the size and location of moving object even the micro-objects is highly blurred. And Depth-from-defocus method is used to recover the depth information of the object from a measure of the level of defocus. The experimental results of blurring tracking and depth recovery of micro gear validate the feasibility of the proposed 3D tracking method.