Abstract
This paper gives a mathematical formulation to the computer vision task of inferring 3-D structures of the scene based on image data and geometric constraints. Introducing a statistical model of image noise, we define a geometric model as a manifold determined by the constraints and view the problem as model fitting. We then present a general mathematical framework for proving optimality of estimation, deriving optimal schemes, and selecting appropriate models. Finally, we illustrate our theory by applying it to structure from motion.
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© 1997 Springer-Verlag Berlin Heidelberg
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Kanatani, K. (1997). Statistical optimization and geometric visual inference. In: Sommer, G., Koenderink, J.J. (eds) Algebraic Frames for the Perception-Action Cycle. AFPAC 1997. Lecture Notes in Computer Science, vol 1315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017875
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DOI: https://doi.org/10.1007/BFb0017875
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