July 2004
Intermediate to advanced
688 pages
19h 3m
English
Content preview from Emerging Topics in Computer VisionBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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2.8. Appendix: Estimating Homography Between the Model Plane and Its Image
There are many ways to estimate the homography between the model plane and its image. Here, we present a technique based on maximum likelihood criterion. Let Mi and mi be the model and image points respectively. Ideally, they should satisfy (2.18). In practice, they do not because of noise in the extracted image points. Let's assume that mi is corrupted by Gaussian noise with mean 0 and covariance matrix 
. Then, the maximum likelihood estimation of H is obtained by minimizing the following functional
where , with being the i-th row of H.
In practice, we simply assume ...
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