Matrix analysis
Image and Vision Computing
Computer Vision and Image Understanding
Differential Invariants for Color Images
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Boosting Color Saliency in Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color Image Segmentation in a Quaternion Framework
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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We propose a quaternion optical flow algorithm for robust object tracking. Unlike previous works of color optical flow methods that treat color as separating channels, the proposed algorithm exploits quaternion representation of color and processes color as a holistic signal. In this way, it enables more accurate flow estimation at the pixel locations of spatial color variations, and reduces tracking errors by leaving more features points at their correct locations on the target. For successful and efficient object tracking, we also proposed a novel type of quaternion color corners that are reliable features during tracking. Together with grayscale corners, they form a good feature point set, especially when used with the proposed quaternion optical flow algorithm. We conduct a quantitative evaluation on publicly available dataset to verify the efficacy of the proposed algorithm. And object tracking experiments demonstrate that robust tracking can be achieved for real-time applications.