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• W1629222029 abstract "Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved with nonlinear optimization methods. It is generally accepted that second order methods are the most robust, fast, and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, second order methods have two main drawbacks: (1) the function might not be analytically differentiable and numerical approximations are impractical, and (2) the Hessian may be large and not positive definite. To address these issues, this paper proposes generic maps, which are average descent directions and rescaling factors learned in a supervised fashion. Using generic maps, we derive a practical algorithm - Supervised Descent Method (SDM) - for minimizing Nonlinear Least Squares (NLS) problems. During training, SDM learns a sequence of decent maps that minimize the NLS. In testing, SDM minimizes the NLS objective using the learned maps without computing the Jacobian or the Hessian. We prove the conditions under which the SDM is guaranteed to converge. We illustrate the effectiveness and accuracy of SDM in three computer vision problems: rigid image alignment, non-rigid image alignment, and 3D pose estimation. In particular, we show how SDM achieves state-of-the-art performance in the problem of facial feature detection. The code has been made available at www.humansensing.cs.cmu.edu/intraface." @default.
• W1629222029 created "2016-06-24" @default.
• W1629222029 creator A5003508045 @default.
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• W1629222029 date "2014-05-03" @default.
• W1629222029 modified "2023-10-11" @default.
• W1629222029 title "Supervised Descent Method for Solving Nonlinear Least Squares Problems in Computer Vision" @default.
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• W1629222029 hasPublicationYear "2014" @default.
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