Titlebook: ;

Feature

https://doi.org/10.1007/978-3-031-59193-8ding chapter can be applied to perspective cameras if we introduce new unknowns called projective depths. They are determined so that the observation matrix can be factorized, for which two approaches exist. One, called the primary method, iteratively determines the projective depths with the result

難管

Michael ten Hompel,Michael Henkea more generalized mathematical framework. We do a detailed error analysis in general terms and derive explicit expressions for the covariance and bias of the solution. The hyper-renormalization procedure is derived in this mathematical framework.

嘲弄

dominant

同音

Christina De La Rocha,Daniel J. ConleyThis chapter states the background and organization of this book and describes distinctive features of the volume.

切掉

Introduction,This chapter states the background and organization of this book and describes distinctive features of the volume.

cardiovascular

inflate

Ahmet Bindal,Sotoudeh Hamedi-Haghirst derive the Sampson error as a first approximation to the Mahalanobis distance (a generalization of the geometric distance or the reprojection error) of ML. Then we do high-order error analysis to derive explicit expressions for the covariance and bias of the solution. The hyperaccurate correction procedure is derived in this framework.

Cpap155

Accuracy of Geometric Estimationa more generalized mathematical framework. We do a detailed error analysis in general terms and derive explicit expressions for the covariance and bias of the solution. The hyper-renormalization procedure is derived in this mathematical framework.

agenda

Maximum Likelihood of Geometric Estimationirst derive the Sampson error as a first approximation to the Mahalanobis distance (a generalization of the geometric distance or the reprojection error) of ML. Then we do high-order error analysis to derive explicit expressions for the covariance and bias of the solution. The hyperaccurate correction procedure is derived in this framework.