Monte Carlo EM for data association and its applications in computer vision

Monte Carlo EM for data association and its applications in computer vision (2001)

by Frank Dellaert

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Datum	Value	Source
TITLE	Monte Carlo EM for data association and its applications in computer vision	user correction
AUTHOR NAME	Frank Dellaert	user correction
AUTHOR AFFIL	School of Computer Science; Carnegie Mellon University	user correction
AUTHOR ADDR	Pittsburgh, PA 15213	user correction
ABSTRACT	Estimating geometry from images is at the core of many computer vision applications, whether it concerns the imaging geometry, the geometry of the scene, or both. Examples include image mosaicking, pose estimation, multibaseline stereo, and structure from motion. All these problems can be modeled probabilistically and translate into well-understood statistical estimation problems, provided the correspondence between measurements in the different images is known. I will show that, if the correspondence is not known, the statistically optimal estimate for the geometry can be obtained using the expectation-maximization (EM) algorithm. In contrast to existing techniques, the EM algorithm avoids the estimation bias associated with computing a single “best ” set of correspondences, but rather considers the distribution over all possible correspondences consistent with the data. While the latter computation is intractable in general, I show that it can be approximated well in practice using Markov chain	SVM HeaderParse 0.2
YEAR	2001	SVM HeaderParse 0.2
VENUE TYPE	TECHREPORT	INFERENCE
TECH	Ph.D. dissertation	INFERENCE
CITATIONS	190 found	ParsCit 1.0