Titlebook: Ellipse Fitting for Computer Vision; Implementation and A Kenichi Kanatani,Yasuyuki Sugaya,Yasushi Kanazawa Book 2016 Springer Nature Switz

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书目名称Ellipse Fitting for Computer Vision影响因子(影响力)




书目名称Ellipse Fitting for Computer Vision影响因子(影响力)学科排名




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书目名称Ellipse Fitting for Computer Vision网络公开度学科排名




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书目名称Ellipse Fitting for Computer Vision被引频次学科排名




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MUTE

一回合

极小量

Robust Fitting, case, we discuss how to remove unwanted segments, or “outliners,” that do not belong to the ellipse under consideration. In the latter case, which occurs when the segment is too short or too noisy, a hyperbola can be fit. We describe methods that force the fit to be an ellipse, although accuracy is

用不完

Ellipse-based 3-D Computation,their images. We start with techniques for computing attributes of ellipses such as intersections, centers, tangents, and perpendiculars. then, we describe how to compute the position and orientation of a circle and its center in the scene from its image. This allows us to generate an image of the c

缓和

缓和

Extension and Generalization,lems for computer vision applications. To illustrate this, we show two typical problems that have the same mathematical structure as ellipse fitting: computing the fundamental matrix from two images and computing the homography between two planar surface images. They are both themselves indispensabl

闲聊

Accuracy of Algebraic Fitting,essions for the covariance and bias of the solution. The hyper-renormalization procedure is derived in this framework. In order that the result directly applies to the fundamental matrix computation described in Section 7.1, we treat {itθ} and {itξ}{in{itga}} as {itn}-D vectors ({itn} = 6 for ellips

被告

Theoretical Accuracy Limit, “KCR lower bound,” on the covariance matrix of the solution {itθ}. The resulting form indicates that all iterative algebraic and geometric methods achieve this bound up to higher order terms in {itσ}, meaning that these are all optimal with respect to covariance. As in Chapters 8 and 9, we treat {i

使乳化

2153-1056 esults can be directly applied to other computer vision tasks including computing fundamental matrices and homographies between images. This book can serve not 978-3-031-00687-6978-3-031-01815-2Series ISSN 2153-1056 Series E-ISSN 2153-1064