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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク maximally stable extremal regions ： ウィキペディア英語版 maximally stable extremal regions In computer vision, maximally stable extremal regions (MSER) are used as a method of blob detection in images. This technique was proposed by Matas et al.〔J. Matas, O. Chum, M. Urban, and T. Pajdla. ("Robust wide baseline stereo from maximally stable extremal regions." ) Proc. of British Machine Vision Conference, pages 384-396, 2002.〕 to find correspondences between image elements from two images with different viewpoints. This method of extracting a comprehensive number of corresponding image elements contributes to the wide-baseline matching, and it has led to better stereo matching and object recognition algorithms. == Terms and Definitions == Image $I$ is a mapping $I\; :\; D\; \backslash subset\; \backslash mathbb^2\; \backslash to\; S$. Extremal regions are well defined on images if: # $S$ is totally ordered (total, antisymmetric and transitive binary relations $\backslash le$ exist). # An adjacency relation $A\; \backslash subset\; D\; \backslash times\; D$ is defined. Region $Q$ is a contiguous subset of $D$. (For each $p,q\; \backslash in\; Q$ there is a sequence $p,\; a\_1,\; a\_2,\; ..,\; a\_n,\; q$ and $pAa\_1,\; a\_iAa\_,\; a\_nAq$.) (Outer) Region Boundary $\backslash partial\; Q\; =\; \backslash$, which means the boundary $\backslash partial\; Q$ of $Q$ is the set of pixels adjacent to at least one pixel of $Q$ but not belonging to $Q$. Extremal Region $Q\; \backslash subset\; D$ is a region such that either for all $p\; \backslash in\; Q,\; q\; \backslash in\; \backslash partial\; Q\; :\; I(p)\; >\; I(q)$ (maximum intensity region) or for all (minimum intensity region). Maximally Stable Extremal Region Let $Q\_1,..,\; Q\_,\; Q\_i,...$ be a sequence of nested extremal regions ($Q\_i\; \backslash subset\; Q\_$). Extremal region $Q\_$ is maximally stable if and only if $q(i)\; =\; |\; Q\_\; \backslash setminus\; Q\_\; |\; /\; |Q\_i|$ has a local minimum at $i$ *. (Here $|\; \backslash cdot\; |$ denotes cardinality.)$\backslash Delta\; \backslash in\; S$ is a parameter of the method. The equation checks for regions that remain stable over a certain number of thresholds. If a region $Q\_$ is not significantly larger than a region $Q\_$, region $Q\_i$ is taken as a maximally stable region. The concept more simply can be explained by thresholding. All the pixels below a given threshold are 'black' and all those above or equal are 'white'. Given a source image, if we generate a sequence of thresholded result images $I\_t$ where each image $t$ corresponds to a increasing threshold t, we would see first a white image, then 'black' spots corresponding to local intensity minima will appear then grow larger. These 'black' spots will eventually merge, until the whole image is black. The set of all connected components in the sequence is the set of all extremal regions. In that sense, the concept of MSER is linked to the one of component tree of the image.〔L. Najman and M. Couprie: ("Building the component tree in quasi-linear time" ); IEEE Transaction on Image Processing, 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「maximally stable extremal regions」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.