翻訳と辞書 Words near each other ・ Scale height ・ Scale insect ・ Scale invariance ・ Scale length ・ Scale length (string instruments) ・ Scale model ・ Scale of chords ・ Scale of harmonics ・ Scale of one to ten ・ Scale of temperature ・ Scale of vowels ・ Scale parameter ・ Scale relativity ・ Scale shell ・ Scale space ・ Scale space implementation ・ Scale test car ・ Scale the Summit ・ Scale the Summit discography ・ Scale up ・ Scale Venture Partners ・ Scale-A-Ton ・ Scale-and-platt ・ Scale-backed antbird ・ Scale-crested pygmy tyrant ・ Scale-eye plaice ・ Scale-feathered malkoha ・ Scale-free ideal gas ・ Scale-free network ・ Scale-invariant feature transform Dictionary Lists mini英和辞書 mini和英辞書 Webster 1913 Latin-English FOLDOC Wikipedia English ウィキペディア

翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Scale space implementation ： ウィキペディア英語版 Scale space implementation The linear scale-space representation of an ''N''-dimensional continuous signal, :$f\_C\; \backslash left\; (x\_1,\; \backslash cdots,\; x\_N,\; t\; \backslash right),$ is obtained by convolving ''fC'' with an ''N''-dimensional Gaussian kernel: :$g\_N\; \backslash left\; (x\_1,\; \backslash cdots,\; x\_N,\; t\; \backslash right).$ In other words: :$L\; \backslash left\; (x\_1,\; \backslash cdots,\; x\_N,\; t\; \backslash right\; )\; =\backslash int\_^\; \backslash cdots\; \backslash int\_^\; f\_C\; \backslash left\; (x\_1-u\_1,\; \backslash cdots,\; x\_N-u\_N,\; t\; \backslash right\; )\; \backslash cdot\; g\_N\; \backslash left(u\_1,\; \backslash cdots,\; u\_N,\; t\; \backslash right)\; \backslash ,\; du\_1\; \backslash cdots\; du\_N.$ However, for implementation, this definition is impractical, since it is continuous. When applying the scale space concept to a discrete signal ''fD'', different approaches can be taken. This article is a brief summary of some of the most frequently used methods. ==Separability== Using the ''separability property'' of the Gaussian kernel :$g\_N\; \backslash left\; (x\_1,\backslash dots,\; x\_N,\; t\; \backslash right)\; =\; G\backslash left(x\_1,\; t\; \backslash right)\; \backslash cdots\; G\backslash left(x\_N,\; t\backslash right)$ the ''N''-dimensional convolution operation can be decomposed into a set of separable smoothing steps with a one-dimensional Gaussian kernel ''G'' along each dimension :$L(x\_1,\; \backslash cdots,\; x\_N,\; t)\; =\; \backslash int\_^\; \backslash cdots\; \backslash int\_^\; f\_C(x\_1-u\_1,\; \backslash cdots,\; x\_N-u\_N,\; t)\; G(u\_1,\; t)\; \backslash ,\; du\_1\; \backslash cdots\; G(u\_N,\; t)\; \backslash ,\; du\_N,$ where :$G(x,\; t)\; =\; \backslash frac\; \}$ and the standard deviation of the Gaussian σ is related to the scale parameter ''t'' according to ''t'' = σ2. Separability will be assumed in all that follows, even when the kernel is not exactly Gaussian, since separation of the dimensions is the most practical way to implement multidimensional smoothing, especially at larger scales. Therefore, the rest of the article focuses on the one-dimensional case. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Scale space implementation」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.