A Maximum Likelihood Framework for Determining Moving Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Artificial Intelligence in Medicine
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Flexible endoscopes are used in many diagnostic and interventional procedures. Physiological motions may render the physicians task very difficult to perform. Assistance could be achieved by using motorized endoscopes and real-time visual tracking algorithm to automatically follow a selected target. In order to control the motors, one needs to have an accurate estimation of the motion of the target in the endoscopic view, which requires an efficient tracking algorithm. In this paper, we compare existing tracking algorithms on various in vivo targets in order to assess their behavior under different conditions. The study shows that several issues have to be overcome by tracking algorithms in in vivo environment like illumination change and forward/backward motions of the target.