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

Odyssey

https://doi.org/10.1007/978-3-319-12772-9describe a procedure for exactly minimizing the sum of squares from the data points, called the “geometric distance,” iteratively using the FNS procedure. Finally, we show how the accuracy can be further improved by a scheme called “hyperaccurate correction.”

他很灵活

https://doi.org/10.1007/978-3-319-42755-3cribe 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 circle seen from the front. The underlying principle is the analysis of image transformations induced by hypothetical camera rotations around its viewpoint.

谦卑

一大群

Chases and Escapes: From Singles to Groups,ly 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 ellipse fitting, and {itn} = 9 for fundamental matrix computation) and do not use particular properties of ellipse fitting.

PRISE

Cleanroom and Software Reliability,hieve 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 {itθ} and {itξ}{in{itga}} as {itn}-D vectors for generality, and the result of this chapter applies to a wide variety of problems including the fundamental matrix computation described in Chapter 7.

缓和

表两个

Radiation Therapy of Benign Diseases978-3-031-35517-2Series ISSN 0942-5373 Series E-ISSN 2197-4187

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A Comprehensive Toolbox to Analyze Enhancer–Promoter Functionsques there has been a major leap forward in the last few years. Historically, identification of specific enhancer elements has led to the identification of master transcription factors (TFs) in the 1990s. Genetic and biochemical experiments have identified the key regulators controlling RNA polymera