English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Many computer vision problems (e.g., camera calibration, image alignment,structure from motion) are solved with nonlinear optimization methods. It isgenerally accepted that second order descent methods are the most robust, fast,and reliable approaches for nonlinear optimization of a general smoothfunction. However, in the context of computer vision, second order descentmethods have two main drawbacks: (1) the function might not be analyticallydifferentiable and numerical approximations are impractical, and (2) theHessian may be large and not positive definite. To address these issues, thispaper proposes generic descent maps, which are average "descent directions" andrescaling factors learned in a supervised fashion. Using generic descent maps,we derive a practical algorithm - Supervised Descent Method (SDM) - forminimizing Nonlinear Least Squares (NLS) problems. During training, SDM learnsa sequence of decent maps that minimize the NLS. In testing, SDM minimizes theNLS objective using the learned descent maps without computing the Jacobian orthe Hessian. We prove the conditions under which the SDM is guaranteed toconverge. We illustrate the effectiveness and accuracy of SDM in three computervision problems: rigid image alignment, non-rigid image alignment, and 3D poseestimation. In particular, we show how SDM achieves state-of-the-artperformance in the problem of facial feature detection. The code has been madeavailable at www.humansensing.cs.cmu.edu/intraface.

Supervised Descent Method for Solving Nonlinear Least Squares Problems in Computer Vision