数字图像处理 外文翻译 外文文献 英文文献 数字图像处理.docx

1、数字图像处理 外文翻译 外文文献 英文文献 数字图像处理数字图像处理 外文翻译 外文文献 英文文献 数字图像处理Digital Image Processing 1 Introduction Many operators have been proposed for presenting a connected component n a digital image by a reduced amount of data or simplied shape. In general we have to state that the development, choice and modi_ca

2、tion of such algorithms in practical applications are domain and task dependent, and there is no best method. However, it is interesting to note that there are several equivalences between published methods and notions, and characterizing such equivalences or di_erences should be useful to categoriz

3、e the broad diversity of published methods for skeletonization. Discussing equivalences is a main intention of this report. 1.1 Categories of Methods One class of shape reduction operators is based on distance transforms. A distance skeleton is a subset of points of a given component such that every

4、 point of this subset represents the center of a maximal disc (labeled with the radius of this disc) contained in the given component. As an example in this _rst class of operators, this report discusses one method for calculating a distance skeleton using the d4 distance function which is appropria

5、te to digitized pictures. A second class of operators produces median or center lines of the digital object in a non-iterative way. Normally such operators locate critical points _rst, and calculate a speci_ed path through the object by connecting these points. The third class of operators is charac

6、terized by iterative thinning. Historically, Listing 10 used already in 1862 the term linear skeleton for the result of a continuous deformation of the frontier of a connected subset of a Euclidean space without changing the connectivity of the original set, until only a set of lines and points rema

7、ins. Many algorithms in image analysis are based on this general concept of thinning. The goal is a calculation of characteristic properties of digital objects which are not related to size or quantity. Methods should be independent from the position of a set in the plane or space, grid resolution (

8、for digitizing this set) or the shape complexity of the given set. In the literature the term thinning is not used - 1 - in a unique interpretation besides that it always denotes a connectivity preserving reduction operation applied to digital images, involving iterations of transformations of speci

9、_ed contour points into background points. A subset Q _ I of object points is reduced by a de_ned set D in one iteration, and the result Q0 = Q n D becomes Q for the next iteration. Topology-preserving skeletonization is a special case of thinning resulting in a connected set of digital arcs or curv

10、es. A digital curve is a path p =p0; p1; p2; :; 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 ha

11、s 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

12、5 is an iterative process of testing and deleting contour pixels sequentially in standard raster scan order. Another sequential algorithm by Pavlidis 12 uses the de_nition of multiple points and proceeds by contour following. Examples of parallel algorithms in this third class are reduction operator

13、s which transform contour points into background points. Di_erences between these parallel algorithms are typically de_ned by tests implemented to ensure connectedness in a local neighborhood. The notion of a simple point is of basic importance for thinning and it will be shown in this report that d

14、i_erent de_nitions of simple points are actually equivalent. Several publications characterize properties of a set D of points (to be turned from object points to background points) to ensure that connectivity of object and background remain unchanged. The report discusses some of these properties i

15、n order to justify parallel thinning algorithms. 1.2 Basics The used notation follows 17. A digital image I is a function de_ned on a discrete set C , which is called the carrier of the image. The elements of C are grid points or grid cells, and the elements (p; I(p) of an image are pixels (2D case)

16、 or voxels (3D case). The range of a (scalar) image is f0; :Gmaxg with Gmax _ 1. The range of a binary image is f0; 1g. We only use binary images I in this report. Let hIi be the set of all pixel locations with value 1, i.e. hIi = I?1(1). The image carrier is de_ned on an orthogonal grid in 2D or 3D

17、 - 2 - space. There are two options: using the grid cell model a 2D pixel location p is a closed square (2-cell) in the Euclidean plane and a 3D pixel location is a closed cube (3-cell) in the Euclidean space, where edges are of length 1 and parallel to the coordinate axes, and centers have integer

18、coordinates. As a second option, using the grid point model a 2D or 3D pixel location is a grid point. Two pixel locations p and q in the grid cell model are called 0-adjacent i_ p 6= q and they share at least one vertex (which is a 0-cell). Note that this speci_es 8-adjacency in 2D or 26-adjacency

19、in 3D if the grid point model is used. Two pixel locations p and q in the grid cell model are called 1- adjacent i_ p 6= q and they share at least one edge (which is a 1-cell). Note that this speci_es 4-adjacency in 2D or 18-adjacency in 3D if the grid point model is used. Finally, two 3D pixel loca

20、tions p and q in the grid cell model are called 2-adjacent i_ p 6= q and they share at least one face (which is a 2-cell). Note that this speci_es 6-adjacency if the grid point model is used. Any of these adjacency relations A_, _ 2 f0; 1; 2; 4; 6; 18; 26g, is irreexive and symmetric on an image car

21、rier C. The _-neighborhood N_(p) of a pixel location p includes p and its _-adjacent pixel locations. Coordinates of 2D grid points are denoted by (i; j), with 1 _ i _ n and 1 _ j _ m; i; j are integers and n;m are the numbers of rows and columns of C. In 3Dwe use integer coordinates (i; j; k). Base

22、d on neighborhood relations we de_ne connectedness as usual: two points p; q 2 C are _-connected with respect to M _ C and neighborhood relation N_ i_ there is a sequence of points p = p0; p1; p2; :; pn = q such that pi is an _-neighbor of pi?1, for 1 _ i _ n, and all points on this sequence are eit

23、her in M or all in the complement 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 _-component of a subset S of C is a maximal _-connected subset of S. The study of connectivity in digital images h

24、as been introduced in 15. It follows that any set hIi consists of a number of _-components. In case of the grid cell model, a component is the union of closed squares (2D case) or closed cubes (3D case). The boundary of a 2-cell is the union of its four edges and the boundary of a 3-cell is the unio

25、n 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 - 3 - values in I at p 2 C and its immediate neighbors in N_(p). 2 Non-iterative Algorithms Non-ite

26、rative algorithms deliver subsets of components in specied scan orders without testing connectivity preservation in a number of iterations. In this section we only use the grid point model. 2.1 Distance Skeleton Algorithms Blum 3 suggested a skeleton representation by a set of symmetric points.In a

27、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

28、 maximal disc, constitutes the skeleton of the set. This idea of presenting a component 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 complement of the subset. The local maxima of the subset represent a

29、 distance skeleton. In 15 the d4-distance is specied as follows. De_nition 1 The distance d4(p; q) from point p to point q, p 6= q, is the smallest positive integer n such that there exists a sequence of distinct grid points p = p0,p1; p2; :; pn = q with pi is a 4-neighbor of pi?1, 1 _ i _ n. If p =

30、 q the distance between them is de_ned to be zero. The distance d4(p; q) has all properties of a metric. Given a binary digital image. We transform this image into a new one which represents at each point p 2 hIi the d4-distance to pixels having value zero. The transformation includes two steps. We

31、apply functions f1 to the image I in standard scan order, producing I_(i; j) = f1(i; j; I(i; j), and f2 in reverse standard scan order, producing T(i; j) = f2(i; j; I_(i; j), as follows: f1(i; j; I(i; j) = 8: 0 if I(i; j) = 0 minfI_(i ? 1; j)+ 1; I_(i; j ? 1) + 1g if I(i; j) = 1 and i 6= 1 or j 6= 1

32、 - 4 - m+ n otherwise f2(i; j; I_(i; j) = minfI_(i; j); T(i+ 1; j)+ 1; T(i; j + 1) + 1g The resulting image T is the distance transform image of I. Note that T is a set f(i; j); T(i; j) : 1 _ i _ n 1 _ j _ mg, and let T_ _ T such that (i; j); T(i; j) 2 T_ i_ none of the four points in A4(i; j) has a value in T equal to T(i; j)+1. For all remaining points (i; j) let T_(i; j) = 0. This image T_ is called distance skeleton. Now we apply functions g1 to the distance skeleton T_ in standard scan order, producing T_(i; j) = g1(i; j; T_(i; j), and g2 to the result of g1 in reverse standard