【正文】 轉(zhuǎn)變 ） 去確定目標(biāo)和背景的連貫性仍然沒變 .報(bào)告討論了一些性質(zhì)是為了證明平行細(xì)算法的正確性 . 所用符號如下 [ 17 ] 。舉例來說，經(jīng)常提到的算法 hilditch 是一個(gè)迭代的過程中的測試和刪去的輪廓像素 ， 按順序在標(biāo)準(zhǔn)光柵掃描秩序。 P2的 。 一個(gè)字集 Q_I的對象點(diǎn)是減少了設(shè)置 ， 在一迭代和 Q0的結(jié)果 = Q N D成為 Q報(bào)表下次迭代。許多算法在圖像分析是在此基礎(chǔ)上的一般概念的細(xì)化。 第二類 算子 產(chǎn)生的中位數(shù)或中心線數(shù)字對象在一個(gè)非迭代的方式。 不過，有趣的是，請注意， 有幾個(gè)等價(jià)之間出版的方法和觀 念，和表征這種等價(jià)應(yīng)該是有用的分類的廣泛和多樣性， 討論等價(jià)是 這份報(bào)告 一個(gè)主要的意圖。m+ 1) = 0 ， mi(l) = int((oi(l) ? ei(l)=2)+ oi(l) ， The result of scanning row i is a set of coordinates (i。 j ? 1) = 0 in row i, counting from the left, with I(i。 j) = T_(i。 T___(i + 1。 j。 T__(i ? 1。 j。 j) = g2(i。 j) = g1(i。 j)) has a value in T equal to T(i。 j)] : 1 _ i _ n ^ 1 _ j _ mg, and let T_ _ T such that [(i。 T(i。 j)) = minfI_(i。 j ? 1) + 1g if I(i。 j)) = 8: 0 if I(i。 I_(i。 I(i。 pn = q with pi is a 4neighbor of pi?1, 1 _ i _ n. If p = q the distance between them is de_ned to be zero. The distance d4(p。 pn = q such that pi is an _neighbor of pi?1, for 1 _ i _ n, and all points on this sequence are either in M or all in the plement of M. A subset M _ C of an image carrier is called _connected i_ M is not empty and all points in M are pairwise _connected with respect to set M. An _ponent of a subset S of C is a maximal _connected subset of S. The study of connectivity in digital images has been introduced in [15]. It follows that any set hIi consists of a number of _ponents. In case of the grid cell model, a ponent is the union of closed squares (2D case) or closed cubes (3D case). The boundary of a 2cell is the union of its four edges and the boundary of a 3cell is the union of its six faces. For practical purposes it is easy to use neighborhood operations (called local operations) on a digital image I which de_ne a value at p 2 C in the transformed image based on pixel 4 values in I at p 2 C and its immediate neighbors in N_(p). 2 Noniterative Algorithms Noniterative algorithms deliver subsets of ponents in specied scan orders without testing connectivity preservation in a number of iterations. In this section we only use the grid point model. \Distance Skeleton Algorithms Blum [3] suggested a skeleton representation by a set of symmetric a closed subset of the Euclidean plane a point p is called symmetric i_ at least 2 points exist on the boundary with equal distances to p. For every symmetric point, the associated maximal disc is the largest disc in this set. The set of symmetric points, each labeled with the radius of the associated maximal disc, constitutes the skeleton of the set. This idea of presenting a ponent of a digital image as a \distance skeleton is based on the calculation of a speci_ed distance from each point in a connected subset M _ C to the plement of the subset. The local maxima of the subset represent a \distance skeleton. In [15] the d4distance is specied as follows. De_nition 1 The distance d4(p。 q 2 C are _connected with respect to M _ C and neighborhood relation N_ i_ there is a sequence of points p = p0。 j are integers and n。 18。 1。 pn = q such that pi is a neighbor of pi?1, 1 _ i _ n, and p = q. A digital curve is called simple if each point pi has exactly two neighbors in this curve. A digital arc is a subset of a digital curve such that p 6= q. A point of a digital arc which has exactly one neighbor is called an end point of this arc. Within this third class of operators (thinning algorithms) we may classify with respect to algorithmic strategies: individual pixels are either removed in a sequential order or in parallel. For example, the often cited algorithm by Hilditch [5] is an iterative process of testing and deleting contour