翻訳と辞書 Words near each other ・ Sobekemsaf (13th Dynasty) ・ Sobekemsaf (queen) ・ Sobekemsaf I ・ Sobekemsaf II ・ Sobekhotep ・ Sobekhotep III ・ Sobekhotep IV ・ Sobekhotep VI ・ Sobekhotep VIII ・ Sobeknakht ・ Sobeknakht II ・ Sobekneferou and Her Legacy ・ Sobekneferu ・ Sobel ・ Sobel (Sierra Leone) ・ Sobel operator ・ Sobel test ・ Sobelair ・ Sobell ・ Sobell House Hospice ・ Sobemovirus ・ Soben Huon ・ Sobenja Vas ・ Sobennikoffia ・ SOBEPS ・ SOBER ・ Sober ・ Sober & Lonely Institute for Contemporary Art ・ Sober (Big Bang song) ・ Sober (Childish Gambino song) 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 Sobel operator ： ウィキペディア英語版 Sobel operator The Sobel operator, sometimes called the Sobel-Feldman operator or Sobel filter, is used in image processing and computer vision, particularly within edge detection algorithms where it creates an image emphasising edges. It is named after Irwin Sobel and Gary Feldman, colleagues at the Stanford Artificial Intelligence Laboratory (SAIL). It was co-developed with Gary Feldman at SAIL. Sobel and Feldman presented the idea of an "Isotropic 3x3 Image Gradient Operator" at a talk at SAIL in 1968. 〔Irwin Sobel, 2014, (''History and Definition of the Sobel Operator'' )〕 Technically, it is a discrete differentiation operator, computing an approximation of the gradient of the image intensity function. At each point in the image, the result of the Sobel-Feldman operator is either the corresponding gradient vector or the norm of this vector. The Sobel-Feldman operator is based on convolving the image with a small, separable, and integer valued filter in the horizontal and vertical directions and is therefore relatively inexpensive in terms of computations. On the other hand, the gradient approximation that it produces is relatively crude, in particular for high frequency variations in the image. ==Formulation== The operator uses two 3×3 kernels which are convolved with the original image to calculate approximations of the derivatives - one for horizontal changes, and one for vertical. If we define A as the source image, and G''x'' and G''y'' are two images which at each point contain the horizontal and vertical derivative approximations, the computations are as follows:〔(Feature Detectors - Sobel Edge Detector )〕 :$$ \mathbf_x = \begin -1 & 0 & +1 \\ -2 & 0 & +2 \\ -1 & 0 & +1 \end * \mathbf \quad \mbox \quad \mathbf_y = \begin -1 & -2 & -1 \\ 0 & 0 & 0 \\ +1 & +2 & +1 \end * \mathbf where $$ * here denotes the 2-dimensional signal processing convolution operation. Since the Sobel kernels can be decomposed as the products of an averaging and a differentiation kernel, they compute the gradient with smoothing. For example, $\backslash mathbf$ can be written as :$$ \begin -1 & 0 & +1 \\ -2 & 0 & +2 \\ -1 & 0 & +1 \end = \begin 1\\ 2\\ 1 \end \begin -1 & 0 & +1 \end The ''x''-coordinate is defined here as increasing in the "right"-direction, and the ''y''-coordinate is defined as increasing in the "down"-direction. At each point in the image, the resulting gradient approximations can be combined to give the gradient magnitude, using: :$\backslash mathbf\; =\; \backslash sqrt^2\; +\; ^2\; \}$ Using this information, we can also calculate the gradient's direction: :$\backslash mathbf\; =\; \backslash operatorname\backslash left(\_x\; \}\backslash right)$ where, for example, Θ is 0 for a vertical edge which is lighter on the right side. 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「Sobel operator」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.