We present a method to automate acquisition of MR brain scans to allow consistent alignment of diagnostic images for
patient follow-up, and to depict standardized anatomy for all patients. The algorithm takes as input a low-resolution
acquisition that depicts the patient position within the scanner. The mid-sagittal plane dividing the brain hemispheres is
automatically detected, as are bony landmarks at the front and back of the skull. The orientation and position of a
subsequent diagnostic, high resolution scan is then aligned based on these landmarks. The method was tested on 91 data
sets, and was completely successful in 93.4% of cases, performed acceptably in 4.4% of cases, and failed for 1.1%. We
conclude that the method is suitable for clinical use and should prove valuable for improving consistency of acquisitions.
KEYWORDS: Cameras, Imaging systems, 3D modeling, 3D image processing, Motion estimation, Video, Signal to noise ratio, Error analysis, Computer simulations, Chemical elements
A method is introduced to track the object's motion and estimate its pose from multiple cameras. We focus on direct estimation of the 3D pose from 2D image sequences. We derive a distributed solution that is equivalent to the centralized pose estimation from multiple cameras. Moreover, we show that, by using a proper rotation between each camera and a fixed camera view, we can rely on independent pose estimation from each camera. Then, we propose a robust solution to the centralized pose estimation problem by deriving a best linear unbiased estimate from the rotated pose estimates obtained from each camera. The resulting pose estimation is therefore robust to errors obtained from specific camera views. Moreover, the computational complexity of the distributed solution is efficient and grows linearly with the number of camera views. Finally, the computer simulation experiments demonstrate that our algorithm is fast and accurate.
In this paper, we introduce a method to jointly track the object motion and estimate pose within the framework of particle
filtering. We focus on direct estimation of the 3D pose from a 2D image sequence. Scale-Invariant Feature Transform
(SIFT) is used to extract feature points in the images. We show that pose estimation from the corresponding feature points
can be formed as a solution to Sylvester's equation. We rely on a solution to Sylvester's equation based on the Kronecker
product method to solve the equation and determine the pose state. We demonstrate that the classical Singular Value
Decomposition (SVD) approach to pose estimation provides a solution to Sylvester's equation in 3D-3D pose estimation.
The proposed approach to the solution of Sylvester's equation is therefore equivalent to the classical SVD method for
3D-3D pose estimation, yet it can also be used for pose estimation from 2D image sequences. Finally, we rely on computer
simulation experiments to demonstrate the performance of our algorithm on video sequences.
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