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Image-based camera motion estimation from video or still images is a difficult problem in the field of computer vision.
Many algorithms have been proposed for estimating intrinsic camera parameters, detecting and matching features
between images, calculating extrinsic camera parameters based on those features, and optimizing the recovered
parameters with nonlinear methods. These steps in the camera motion inference process all face challenges in practical
applications: locating distinctive features can be difficult in many types of scenes given the limited capabilities of current
feature detectors, camera motion inference can easily fail in the presence of noise and outliers in the matched features,
and the error surfaces in optimization typically contain many suboptimal local minima. The problems faced by these
techniques are compounded when they are applied to medical video captured by an endoscope, which presents further
challenges such as non-rigid scenery and severe barrel distortion of the images. In this paper, we study these problems
and propose the use of prior probabilities to stabilize camera motion estimation for the application of computing
endoscope motion sequences in colonoscopy.
Colonoscopy presents a special case for camera motion estimation in which it is possible to characterize typical motion
sequences of the endoscope. As the endoscope is restricted to move within a roughly tube-shaped structure,
forward/backward motion is expected, with only small amounts of rotation and horizontal movement. We formulate a
probabilistic model of endoscope motion by maneuvering an endoscope and attached magnetic tracker through a
synthetic colon model and fitting a distribution to the observed motion of the magnetic tracker. This model enables us to
estimate the probability of the current endoscope motion given previously observed motion in the sequence. We add
these prior probabilities into the camera motion calculation as an additional penalty term in RANSAC to help reject
improbable motion parameters caused by outliers and other problems with medical data. This paper presents the
theoretical basis of our method along with preliminary results on indoor scenes and synthetic colon images.
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