An approach for estimating the fundamental matrix
Daniel Barragan Calderon, María Trujillo
Abstract
Daniel Barragan Calderon, María Trujillo
Abstract
The camera calibration problem consists in estimating the intrinsic and the extrinsic parameters. This problem can be solved by computing the fundamental matrix. The fundamental matrix can be obtained from a set of corresponding points. However in practice, corresponding points may be inaccurately estimated, falsely matched or badly located, due to occlusion and ambiguity, among others. On the other hand, if the set of corresponding points does not include information on different depth planes, the estimated fundamental matrix may not be able to correctly recover the epipolar geometry. In this paper a method for estimating the fundamental matrix is introduced. The estimation problem is posed as finding a set of corresponding points. Fundamental matrices are estimated using subsets of corresponding points and an optimisation criterion is used to select the best estimated fundamental matrix. The experimental evaluation shows that the least range of residuals is a tolerant criterion to large baselines.
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The camera calibration problem consists in estimating the intrinsic and the extrinsic parameters. This problem can be solved by computing the fundamental matrix. The fundamental matrix can be obtained from a set of corresponding points. However in practice, corresponding points may be inaccurately estimated, falsely matched or badly located, due to occlusion and ambiguity, among others. On the other hand, if the set of corresponding points does not include information on different depth planes, the estimated fundamental matrix may not be able to correctly recover the epipolar geometry. In this paper a method for estimating the fundamental matrix is introduced. The estimation problem is posed as finding a set of corresponding points. Fundamental matrices are estimated using subsets of corresponding points and an optimisation criterion is used to select the best estimated fundamental matrix. The experimental evaluation shows that the least range of residuals is a tolerant criterion to large baselines.
Key concepts: Fundamental matrix (linear differential equation), Epipolar geometry, Essential matrix, Eight-point algorithm, Matrix (chemical analysis), Mathematics, Set (abstract data type), Algorithm