原文出处Digital Image Processing 2/E
图像分割
前一章的资料使我们所研究的图像处理方法开始发生了转变。从输人输出均为图像的处理方法转变为输人为图像而输出为从这些图像中提取出来的属性的处理方法〔这方面在1.1节中定义过)。图像分割是这一方向的另一主要步骤。
分割将图像细分为构成它的子区域或对象。分割的程度取决于要解决的问题。就是说当感兴趣的对象已经被分离出来时就停止分割。例如，在电子元件的自动检测方面，我们关注的是分析产品的图像，检测是否存在特定的异常状态，比如，缺失的元件或断裂的连接线路。超过识别这此元件所需的分割是没有意义的。
异常图像的分割是图像处理中最困难的任务之一。精确的分割决定着计算分析过程的成败。因此，应该特别的关注分割的稳定性。在某些情况下，比如工业检测应用，至少有可能对环境进行适度控制的检测。有经验的图像处理系统设计师总是将相当大的注意力放在这类可能性上。在其他应用方面，比如自动目标采集，系统设计者无法对环境进行控制。所以，通常的方法是将注意力集中于传感器类型的选择上，这样可以增强获取所关注对象的能力，从而减少图像无关细节的影响。一个很好的例子就是，军方利用红外线图像发现有很强热信号的目标，比如移动中的装备和部队。
图像分割算法一般是基于亮度值的不连续性和相似性两个基本特性之一。第一类性质的应用途径是基于亮度的不连续变化分割图像，比如图像的边缘。第二类的主要应用途径是依据事先制定的准则将图像分割为相似的区域，门限处理、区域生长、区域分离和聚合都是这类方法的实例。
本章中，我们将对刚刚提到的两类特性各讨论一些方法。我们先从适合于检测灰度级的不连续性的方法展开，如点、线和边缘。特别是边缘检测近年来已经成为分割算法的主题。除了边缘检测本身，我们还会讨论一些连接边缘线段和把边缘“组装”为边界的方法。关于边缘检测的讨论将在介绍了各种门限处理技术
之后进行。门限处理也是一种人们普遍关注的用于分割处理的基础性方法，特别是在速度因素占重要地位的应用中。关于门限处理的讨论将在几种面向区域的分割方法展开的讨论之后进行。之后，我们将讨论一种称为分水岭分割法的形态学图像分割方法。这种方法特别具有吸引力，因为它将本章第一部分提到的几种分割属性技术结合起来了。我们将以图像分割的应用方面进行讨论来结束本章。 10.1间断检测
在本节中，我们介绍几种用于检测数字图像中三种基本的灰度级间断技术:点、线和边缘。寻找间断最一般的方法是以3.5节中描述的方式对整幅图像使用一个模板进行检测。图10-1所示的3x3模板，这一过程包括计算模板所包围区域内灰度级与模板系数的乘积之和。就是说，关于式(3.5.3)，在图像中任意点的模板响应由下列公式给出：
∑==+++=919
9...2211i wizi
z w z w z w R （10.1.1）
图10-1 一个一般的3*3模板
这里Zi 是与模板系数Wi 相联系的像素的灰度级。照例，模板响应是它的中心位置。有关执行模板操作的细节在3.5节中讨论。
10.1.1点检测
在一幅图像中，孤立点的检测在理论上是简单的。使用如图10-2(a)所示的模板，如果
|R| ≥ T (10.1.2)
我们说在模板中心的位置上已经检测到一个点。这里T 是一个非负门限，R 由式(10.1.1)给出。基本上，这个公式是测量中心点和它的相邻点之间加权的差值。
基本思想就是:如果一个孤立的点(此点的灰度级与其背景的差异相当大并且它
所在的位置是一个均匀的或近似均匀的区域)与它周围的点很不相同，则很容易
被这类模板检测到。注意，图10-2(a)中的模板同图3.39(d)中给出的模板在拉
普拉斯操作方而是相同的。严格地讲，这里强调的是点的检测。即我们着重考虑
的差别是那些足以识别为孤立点的差异(由T决定)。注意，模板系数之和为零表
示在灰度级为常数的区域，模板响应为零。
（a）
（b）（c）（d）图10-2 （a）点检测模板，（b）带有通孔的涡轮叶片的X射线，（c）点检测的结果，（d）使用式（10.1.2）得到的结果（原图由X-TEK系统公司提供）
例10.1图像中孤立点的检浏
我们以图10-2(b)功为辅助说明如何从一幅图中将孤立点分割出来.这幅X
射线图显示了一个带有通孔的喷气发动抓涡枪叶片，通孔位于圈像的右上象限。
在孔中只嵌有一个黑色像素。图10-2(c)是将点检测模板应用于X射线图像后得
到的结果.图10-2(d)显示了当T取图10-2(c)中像素最高绝衬值的90%时，应用
式(10.1.2)所得的结果(门限选择将在10.3节中详细讨论)。图中的这个单一的
像素清晰可见(这个像素被人为放大以便印刷后可以看到)。由于这类检测是基于
单像素间断，并且检测器模板的区域有一个均匀的背景，所以这个检测过程是相
当有专用性的当这一条件不能满足时，本章中计论的其他方法会更适合检测灰度
级间断
10.1.2线检测
复杂程度更高一级的检测是线检测，考虑图10-3中显示的模板。如果第l 个模板在图像中移动，这个模板将对水平方向的线条(一个像素宽度)有更强的响应。在一个不变的背景上，当线条经过模板的中间一行时会产生响应的最大值。画一个元素为1的简单阵列，并且使具有不同灰度级(如5)的一行水平穿过阵列，可以很容易验证这一点。同样的实验可以显示出图10-3中的第2个模板对于45°方向线有最佳响应;第3个模板对于垂直线有最佳响应;第4个模板对于
-45°方向线有最佳响应;这些方向也可以通过注释每个模板的优选方向来设置，即在这些方向上用比别的方向更大的系数(为2)设置权值。注意每个模板系数相加的总和为零，表示在灰度级恒定的区域来自模板的响应为零。
图10-3 线模板
令R1，R2，R3和R4。从左到右代表图10-3中模板的响应，这里R的值由式(10.1.1)给出。
假设4个模板分别应用于一幅图像，在图像中心的点，如果|Ri|>|Rj| ,
j≠i，则此点被认为与在模板i方向上的线更相关。例如，如果在图中的一点有|Ri|>|Rj| ,j=2,3,4，我们说此特定点与水平线有更大的联系。
换句话说，我们可能对检测特定方向上的线感兴趣。在这种情况下，我们应使用与这一方向有关的模板，并设置该模板的输出门限，如式(10.1.2)所示。换句话说，如果我们对检测图像中由给定模板定义的方向上的所有线感兴趣.只需要简单地通过整幅图像运行模板，并对得到的结果的绝对值设置门限即可。留下的点是有最强响应的点。对于一个像素宽度的线，这些响应最靠近模板定义的对应方向。下列例子说明了这一过程。
例 10.2特定方向上的线检测
图10-4(a)显示了一幅电路接线模板的数字化(二值的)图像。假设我们要找到一个像素宽度的并且方向为-45°的线条。基于这个假设，使用图10-3中最后
一个模板。图10-4(b)显示了得到的结果的绝对值。注意，图像中所有水平和垂直的部分都被除去了。并且在图10-4(b)中所有原图中接近-45°方向的部分产生了最强响应。
（a）
（b）（c）
图10-4 线检测的说明。（a）二进制电路接线模板，（b）使用-45°线检测器
处理后得到的绝对值，（c）对图像（b）设置门限得到的结果为了决定哪一条线拟合模板最好，只需要简单地对图像设置门限。图10-4(c)显示了使门限等于图像中最大值后得到的结果。对于与这个例子类似的应用，让门限等于最大值是一个好的选择，因为输入图像是二值的，并且我们要寻找的是最强响应。图10-4(c)显示了在白色区所有通过门限检测的点。此时，这一过程只提取了一个像素宽且方向为-45°的线段(图像中在左上象限中也有此方向上的图像部分，但宽度不是一个像素)。图10-4(c)中显示的孤立点是对于模板也有相同强度响应的点。在原图中，这些点和与它们紧接着的相邻点，是用模板在这些孤立位置上生成最大响应的方法来定向的。这些孤立点也可以使用图10-2(a)中的模板进行检测，然后删除，或者使用下一章中讨论的形态学腐蚀法
删除。
10.1.3边缘检侧
尽管在任何关于分割的讨论中，点和线检测都是很重要的，但是边缘检测对于灰度级间断的检测是最为普遍的检测方法。本节中，我们讨论实现一阶和二阶数字导数检测一幅图像中边缘的方法。在3.7节介绍图像增强的内容中介绍过这些导数。本节的重点将放在边缘检测的特性上。某些前面介绍的概念在这里为了叙述的连续性将进行简要的重述。
基本说明
在3.7.1节中我们非正式地介绍过边缘。本节中我们更进一步地了解数字化边缘的概念。直观上，一条边缘是一组相连的像素集合。这些像素位于两个区域的边界上。然而，我们已经在2.5.2节中用一定的篇幅解释了一条边缘和一条边界的区别。从根本上讲，如我们将要看到的，一条边缘是一个“局部”概念，而由于其定义的方式，一个区域的边界是一个更具有整体性的概念。给边缘下一个更合理的定义需要具有以某种有意义的方式测量灰度级跃变的能力。
我们先从直观上对边缘建模开始。这样做可以将我们引领至一个能测量灰度级有意义的跃变的形式体系中。从感觉上说，一条理想的边缘具有如图10-5(a)所示模型的特性。依据这个模型生成的完美边缘是一组相连的像素的集合(此处为在垂直方向上)，每个像素都处在灰度级跃变的一个垂直的台阶上(如图形中所示的水平剖面图)。
实际上，光学系统、取样和其他图像采集的不完善性使得到的边缘是模糊的，模糊的程度取决于诸如图像采集系统的性能、取样率和获得图像的照明条件等因素。结果，边缘被更精确地模拟成具有“类斜面”的剖面，如图10-5(b)所示。斜坡部分与边缘的模糊程度成比例。在这个模型中，不再有细线(一个像素宽的线条)。相反，现在边缘的点是包含于斜坡中的任意点，并且边缘成为一组彼此相连接的点集。边缘的“宽度”取决于从初始灰度级跃变到最终灰度级的斜坡的长度。这个长度又取决于斜度，斜度又取决于模糊程度。这使我们明白:模糊的边缘使其变粗而清晰的边缘使其变得较细。
图10-6(a)显示的图像是从图10-5(b)的放大特写中提取出来的。图10-6(b)显示了两个区域之间边缘的一条水平的灰度级剖面线。这个图形也显示出灰度级剖面线的一阶和二阶导数。当我们沿着剖面线从左到右经过时，在进人和离开斜
面的变化点，一阶导数为正。在灰度级不变的区域一阶导数为零。在边缘与黑色一边相关的跃变点二阶导数为正，在边缘与亮色一边相关的跃变点二阶导数为负，沿着斜坡和灰度为常数的区域为零。在图10-6(b)中导数的符号在从亮到暗的跃变边缘处取反。
（a）（b）
图10-5 （a）理想的数字边缘模型，（b）斜坡数字边缘模型。
斜坡部分与边缘的模糊程度成正比
图10-6 (a)由一条垂直边缘分开的两个不同区域,(b)边界附近的细
节显示了一个灰度级剖面图和一阶与二阶导数的剖面图由这些现象我们可以得到的结论是:一阶导数可以用于检测图像中的一个点
是否是边缘的点(也就是判断一个点是否在斜坡上)。同样，二阶导数的符号可以用于判断一个边缘像素是在边缘亮的一边还是暗的一边。我们注意到围绕一条边缘，二阶导数的两条附加性质(1)对图像中的每条边缘二阶导数生成两个值(一个不希望得到的特点);(2)一条连接二阶导数正极值和负极值的虚构直线将在边缘中点附近穿过零点。将在本节后面说明，二阶导数的这个过零点的性质对于确定粗边线的中心非常有用。
最后，注意到某些边缘模型利用了在进人和离开斜坡地方的平滑过渡(习题10.5)。然而，我们在接下来的讨论中将得出同样的结论。而且，这一点从我们使用局部检测进行处理就可以很明显地看出(因此，2.5.2节中对于边缘的局部性质进行了说明)。
尽管到此为止我们的注意力被限制在一维水平剖面线范围内，但同样的结论可以应用于图像中的任何方向上。我们仅仅定义了一条与任何需要考察的点所在的边缘方向相垂直的剖面线，并如前面讨论的那样，对结果进行了解释。
注：出自
Digital Image Processing 2nd Edition . Prentice Hall
Image Segmentation
The material in the previous chapter began a transition from image processing methods whose input and output are images, to methods in which the inputs are images, but the outputs are attributes extracted from those images (in the sense defined is Section 1.1). Segmentation is another major step in that direction.
Segmentation subdivides an image into its constituent regions or objects. The level to which the subdivision is carried depends on the problem being solved. That is, segmentation should stop when the objects of interest in an application have been isolated. For example, in the automated inspection of electronic assemblies, interest lies in analyzing images of the products with the objective of determining the presence or absence of specific anomalies, such as missing components or broken connection paths. There is no point in carrying segmentation past the level of detail required to identify those elements.
Segmentation of nontrivial images is one of the most difficult tasks in image processing. Segmentation accuracy determines the eventual success or failure of computerized analysis procedures. For this reason, considerable care should be taken to improve the probability of rugged segmentation. In some situations , such as industrial inspection applications, at least some measure of control over the environment is possible at times. The experienced image processing system designer invariably pays considerable attention to such opportunities. In other applications, such as autonomous target acquisition, the system designer has no control of the environment. Then the usual approach is to focus on selecting the types of sensors most likely to enhance the objects of interest while diminishing the contribution of irrelevant image detail. A good example is the use of infrared imaging by the military to detect objects with strong heat signatures , such as equipment and troops in motion.
Image segmentation algorithms generally are based on one of two basic properties of intensity values: discontinuity and similarity. In the first category, the approach is to partition an image based on abrupt changes in intensity, such as edges in an image. The principal approaches in the second category are based on
partitioning an image into regions that are similar according to a set of predefined criteria. Thresholding, region growing, and region splitting and merging are examples of methods in this category.
In this chapter we discuss a number of approaches in the two categories just mentioned. We begin the development with methods suitable for detecting gray level discontinuities such as points, lines, and edges. Edge detection in particular has been a staple of segmentation algorithms for many years. In addition to edge detection per se, we also discuss methods for connecting edge segments and for "assembling" edges into region boundaries. The discussion on edge detection is followed by the introduction of various thresholding techniques . Thresholding also is a fundamental approach to segmentation that enjoys a significant degree of popularity, especially in applications where speed is an important factor. The discussion on thresholding is followed by the development of several region-oriented segmentation approaches. We then discuss a morphological approach to segmentation called watershed segmentation. This approach is particularly attractive because it combines several of the positive attributes of segmentation based on the techniques presented in the first part of the chapter. We conclude the chapter with a discussion on the use of motion cues for image segmentation.
10.1Detection of Discontinuities
In this section we present several techniques for detecting the three basic types of gray-level discontinuities in a digital image: points, lines, and edges. The most common way to look for discontinuities is to run a mask through the image in the manner described in Section 3.5. For the 3 x 3 mask shown in Fig. 10.1, this procedure involves computing the sum of products of the coefficients with the gray levels contained in the region encompassed by the mask. That is. with reference to Eq.
(3.5-3). the response of the mask at anv point in the image is given by
∑==+++=919
9...2211i wizi
z w z w z w R （10.1.1）
FIGURE 10.1 A general 3 x 3 mask.
where z; is the gray level of the pixel associated with mask coefficient Wi. As usual, the response of the mask is defined with respect to its center location. The details for implementing mask operations are discussed in Section 3.5.
10.1.1 Point Detection
The detection of isolated points in an image. is straightforward in principle. Using the mask shown in Fig. 10.2(a), we say that a point has been detected at the location on which the mask is centered if
|R| ≥ T (10.1.2)
where T is a nonnegative threshold and R is given by Eq. (10.1-1). Basically,this formulation measures the weighted differences between the center point and its neighbors. The idea is that an isolated point (a point whose gray level is significantly different from its background and which is located in a homogeneous or nearly homogeneous area) will be quite different from its surroundings, and thus be easily detectable by this type of mask. Note that the mask in Fig. 10.2(a) is the same as the mask shown in Fig. 3.39(d) in connection with Laplacian operations. However, the emphasis here is strictly on the detection of points. That is, the only differences that are considered of interest are those large enough (as determined by T, to be considered isolated points. Note that the mask coefficients sum to zero, indicating that the mask response will be zero in areas of constant gray level.
（a）
（b）（c）（d）FIGURE 10.2(a) Pointdetection mask. (b) X-ray image of a turbine blade with a porosity. (c) Result of point detection. (d) Result of using Eq. (10.1-2).(Original image courtesy of X-TEK Systems Ltd.)
EXAMPLE 10.1:Detection of isolated points in an image.
We illustrate segmentation of isolated points from an image with the aid of Fig.
10.2(6), which shows an X-ray image of a jet-engine turbine blade with a porosity in the upper, right quadrant of the image. There is a single black pixel embedded within the porosity. Figure 10.2(c) is the result of applying the point detector mask to the X-ray image, and Fig. 10.2(d) shows the result of using Eq. (10.1.2) with T equal to 90% of the highest absolute pixel value of the image in Fig. 10.2(c). (Threshold selection is discussed in detail in Section 10.3) The single pixel is clearly visible in this image (the pixel was enlarged manually so that it would be visible after printing). This -type of detection process is rather specialized because it is based on single-pixel discontinuities that have a homogeneous background in the area of the detector mask. When this condition is not satisfied, other methods discussed in this chapter are more suitable for detecting gray-level discontinuities.
10.1.2 Line Detection
The next level of complexity is line detection. Consider the masks shown in Fig.
10.3. If the first mask were moved around an image, it would respond more strongly to lines (one pixel thick) oriented horizontally. With a constant background, the maximum response would result when the line passed through the middle row of the mask. This is easily verified by sketching a simple array of 1's with a line of a different gray level (say, 5's) running horizontally through the array. A similar experiment would reveal that the second mask in Fig. 10.3 responds best to lines oriented at +450; the third mask to vertical lines; and the fourth mask to lines in the -450 direction . These directions can be established also by noting that the preferred direction of each mask is weighted with a larger coefficient (i.e., 2) than other possible directions. Note that the coefficients in each mask sum to zero, indicating a zero response from the masks in areas of constant gray level.
Horizontal +45° Vertical -45°
FIGURE 10.3 Line masks.
Let R1, R2, R3, and R4 denote the responses of the masks in Fig. 10.3, from left to right, where the R's are given by Eq. (10.1-1). Suppose that the four masks are run individually through an image. If, at a certain point in the image, |Ri| > |Rj|, for all j ≠i, that point is said to be more likely associated with a line in the direction of mask i. For example, if at a point in the image, |Ri|>|Rj|, for j = 2, 3. 4, that particular point is said to be more likely associated with a horizontal line. Alternatively, we may be interested in detecting lines in a specified direction. In this case, we would use the mask associated with that direction and threshold its output, as in Eq . (10.1.2). In other words, if we are interested in detecting all the lines in an image in the direction defined by a given mask, we simply run the mask through the image and threshold the absolute value of the result. The points that are left are the strongest responses, which,
for lines one pixel thick, correspond closest to the direction defined by the mask. The following example illustrates this procedure.
EXAMPLE 10.2:Detection of lines in a specified direction
Figure 10.4(a) shows a digitized (binary) portion of a wire-bond mask for an electronic circuit. Suppose that we are interested in finding all the lines that are one pixel thick and are oriented at-45". For this purpose, we use the last mask shown in Fig. 10.3.The absolute value of the result is shown in Fig. 10.4(b). Note that all vertical and horizontal components of the image were eliminated, and that the components of the original image that tend toward a -45° direction
（a）
（b）（c）
FIGURE 10.4 Illustration of line detection (a) Binary wirebond mask.
(b) Absolute value of result after processing with -45° line detector. (c) Result of thresholding image. (b) produced the strongest responses in Fig. 10.4(b).
In order to determine which lines best fit the mask, we simply threshold this image. The result of using a threshold equal to the maximum value in the image is
shown in Fig. 10.4(c).The maximum value is a good choice for a threshold in applications such as this because the input image is binary and we are looking for the strongest responses. Figure 10.4(c) shows in white all points that passed the threshold test. In this case, the procedure extracted the only line segment that was one pixel thick and oriented at -450 (the other component of the image oriented in this direction in the top, left quadrant is not one pixel thick). The isolated points shown in Fig.
10.4(c) are points that also had similarly strong responses to the mask. In the original image, these points and their immediate neighbors are oriented in such as way that the mask produced a maximum response at those isolated locations. These isolated points can be detected using the mask in Fig. 10.2(a) and then deleted, or they could be deleted using morphological erosion, as discussed in the last chapter.
10.1.3 Edge Detection
Although point and line detection certainly are important in any discussion on segmentation, edge detection is by far the most common approach for detecting meaningful discontinuities in gray level. In this section we discuss approaches for implementing first- and second-order digital derivatives for the detection of edges in an image. We introduced these derivatives in Section 3.7 in the context of image enhancement. The focus in this section is on their properties for edge detection. Some of the concepts previously introduced are restated briefly here for the sake continuity in the discussion.
Basic formulation
Edges were introduced informally in Section 3.7.1. In this section we look at the concept of a digital edge a little closer. Intuitively, an edge is a set of connected pixels that lie on the boundary between two regions. However, we already went through some length in Section 2.5.2 to explain the difference between an edge and a boundary. Fundamentally, as we shall see shortly, an edge is a "local" concept whereas a region boundary, owing to the way it is defined, is a more global idea. A reasonable definition of "edge" requires the ability to measure gray-level transitions in a meaningful way.
We start by modeling an edge intuitively. This will lead us to a formalism to
which "meaningful" transitions in gray levels can be measured. Intuitively, an ideal edge has the properties of the model shown in Figure 10.5(a). An ideal edge according to this model is a set of connected pixels (in the vertical direction here), each of which is located at an orthogonal step transition in gray level (as shown by the horizontal profile in the figure).
In practice, optics, sampling, and other image acquisition imperfections yield edges that are blurred, with the degree of blurring being determined by factors. such as the quality of the image acquisition system, the sampling rate, and illumination conditions under which the image is acquired. As a result, edges are more closely modeled as having a "ramplike" profile, such as the one shown in Figure 10.5(b). The slope of the ramp is inversely proportional to the degree of blurring in the edge. In this model. we no longer have a thin (one pixel thick) path. Instead, an edge point now is any point contained in the ramp, and an edge would then be a set of such points that arc connected. The "thickness" of the edge is determined by the length of the ramp. as it transitions from an initial to a final gray level. This length is determined by the slope, which. in turn is determined by the degree of blurring. This makes sense: Blurred edges tend to be thick and sharp edges tend to be thin.
Figure 10.6(a) shows the image from which the close-up in Fig. 10.5(b) was extracted. Figure 10.6(b) shows a horizontal gray-level profile of the edge between the two regions. This figure also shows the first and second derivatives of the gray-level profile. The first derivative is positive at the points of transition into and out of the ramp as we move from left to right along the profile: it is constant for points in the ramp: and is zero in areas of constant gray level.丁he second derivative is positive at the transition associated with the dark side of the edge, negative at the transition associated with the light side of the edge, and zero along the ramp and in areas of constant gray level. The signs of the derivatives in Fig. 10.6(b) would be reversed for an edge that transitions from light to dark.
a b
FIGURE 10.5 (a) Model of an ideal digital edge. (b) Model of a ramp edge. The
slope of the ramp is proportional to the degree of blurring in the edge.
FIGURE 10.6 (a) Two regions separated by a vertical edge. (b) Detail near the edge, showing a gray-level profile, and the first and second derivatives of the
profile
We conclude from these observations that the magnitude of the first derivative can be used to detect the presence of an edge at a point in an image (i.e.,to determine if a point is on a ramp). Similarly, the sign of the second derivative can be used to determine whether an edge pixel lies on the dark or light side of an edge. We note two additional properties of the second derivative around an edge: (7 ) II produces two values for every edge in an image (an undesirable feature); and (2) an imaginary straight line joining the extreme positive and negative values of the second derivative would cross zero near the midpoint of the edge. This zero-crossing property of the second derivative is quite useful for locating the centers of thick edges, as we show later in this section. Finally, we note that some edge models make use of a smooth transition into and out of the ramp (Problem 10.5). However, the conclusions at which we arrive in the following discussion are the same. Also, it is evident from this discussion that we are dealing here with local measures (thus the comment made in Section 2.5.2 about the local nature of edges).
Although attention thus far has been limited to a I -D horizontal profile. A similar argument applies to an edge of any orientation in an image. We simply define a profile perpendicular to the edge direction at any desired point and interpret the results as in the preceding discussion.
From:
Introduction to Algorithms Digital Image Processing 2nd Edition . Prentice Hall
中英文资料对照外文翻译 一、英文原文 A NEW CONTENT BASED MEDIAN FILTER ABSTRACT In this paper the hardware implementation of a contentbased median filter suitabl e for real-time impulse noise suppression is presented. The function of the proposed ci rcuitry is adaptive; it detects the existence of impulse noise in an image neighborhood and applies the median filter operator only when necessary. In this way, the blurring o f the imagein process is avoided and the integrity of edge and detail information is pre served. The proposed digital hardware structure is capable of processing gray-scale im ages of 8-bit resolution and is fully pipelined, whereas parallel processing is used to m inimize computational time. The architecturepresented was implemented in FPGA an d it can be used in industrial imaging applications, where fast processing is of the utm ost importance. The typical system clock frequency is 55 MHz. 1. INTRODUCTION Two applications of great importance in the area of image processing are noise filtering and image enhancement [1].These tasks are an essential part of any image pro cessor,whether the final image is utilized for visual interpretation or for automatic an alysis. The aim of noise filtering is to eliminate noise and its effects on the original im age, while corrupting the image as little as possible. To this end, nonlinear techniques (like the median and, in general, order statistics filters) have been found to provide mo re satisfactory results in comparison to linear methods. Impulse noise exists in many p ractical applications and can be generated by various sources, including a number of man made phenomena, such as unprotected switches, industrial machines and car ign ition systems. Images are often corrupted by impulse noise due to a noisy sensor or ch annel transmission errors. The most common method used for impulse noise suppressi on n forgray-scale and color images is the median filter (MF) [2].The basic drawback o f the application of the MF is the blurringof the image in process. In the general case,t he filter is applied uniformly across an image, modifying pixels that arenot contamina ted by noise. In this way, the effective elimination of impulse noise is often at the exp ense of an overalldegradation of the image and blurred or distorted features[3].In this paper an intelligent hardware structure of a content based median filter (CBMF) suita ble for impulse noise suppression is presented. The function of the proposed circuit is to detect the existence of noise in the image window and apply the corresponding MF
中等分辨率制备分离的 快速色谱技术 W. Clark Still,* Michael K a h n , and Abhijit Mitra Departm(7nt o/ Chemistry, Columbia Uniuersity,1Veu York, Neu; York 10027 ReceiLied January 26, 1978 我们希望找到一种简单的吸附色谱技术用于有机化合物的常规净化。这种技术是适于传统的有机物大规模制备分离，该技术需使用长柱色谱法。尽管这种技术得到的效果非常好，但是其需要消耗大量的时间，并且由于频带拖尾经常出现低复原率。当分离的样本剂量大于1或者2g时，这些问题显得更加突出。近年来，几种制备系统已经进行了改进，能将分离时间减少到1-3h，并允许各成分的分辨率ΔR f≥（使用薄层色谱分析进行分析）。在这些方法中，在我们的实验室中，媒介压力色谱法1和短柱色谱法2是最成功的。最近，我们发现一种可以将分离速度大幅度提升的技术，可用于反应产物的常规提纯，我们将这种技术称为急骤色谱法。虽然这种技术的分辨率只是中等（ΔR f≥），而且构建这个系统花费非常低，并且能在10-15min内分离重量在的样本。4 急骤色谱法是以空气压力驱动的混合介质压力以及短柱色谱法为基础，专门针对快速分离，介质压力以及短柱色谱已经进行了优化。优化实验是在一组标准条件5下进行的，优化实验使用苯甲醇作为样本，放在一个20mm*5in.的硅胶柱60内，使用Tracor 970紫外检测器监测圆柱的输出。分辨率通过持续时间（r）和峰宽（w,w/2）的比率进行测定的（Figure 1），结果如图2-4所示，图2-4分别放映分辨率随着硅胶颗粒大小、洗脱液流速和样本大小的变化。
The smart grid Smart grid is the grid intelligent (electric power), also known as the "grid" 2.0, it is based on the integration, high-speed bidirectional communication network, on the basis of through the use of advanced sensor and measuring technology, advanced equipme nt technology, the advanced control method, and the application of advanced technology of decision support system, realize the power grid reliability, security, economic, efficient, environmental friendly and use the security target, its main features include self-healing, incentives and include user, against attacks, provide meet user requirements of power quality in the 21st century, allow all sorts of different power generation in the form of access, start the electric power market and asset optimizatio n run efficiently. The U.S. department of energy (doe) "the Grid of 2030" : a fully automated power transmission network, able to monitor and control each user and power Grid nodes, guarantee from power plants to end users among all the nodes in the whole process of transmission and distribution of information and energy bi-directional flow. China iot alliance between colleges: smart grid is made up of many parts, can be divided into:intelligent substation, intelligent power distribution network, intelli gent watt-hourmeter,intelligent interactive terminals, intelligent scheduling, smart appliances, intelligent building electricity, smart city power grid, smart power generation system, the new type of energy storage system.Now a part of it to do a simple i ntroduction. European technology BBS: an integration of all users connected to the power grid all the behavior of the power transmission network, to provide sustained and effective economic and security of power. Chinese academy of sciences, institute of electrical: smart grid is including all kinds of power generation equipment, power transmission and distribution network, power equipment and storage equipment, on the basis of the physical power grid will be modern advanced sensor measurement technology, network technology, communication
图像分割算法开题报告 摘要：图像分割是图像处理中的一项关键技术，自20世纪70年代起一直受到人们的高度重视，并在医学、工业、军事等领域得到了广泛应用。近年来具有代表性的图像分割方法有：基于区域的分割、基于边缘的分割和基于特定理论的分割方法等。本文主要对基于自动阈值选择思想的迭代法、Otsu法、一维最大熵法、二维最大熵法、简单统计法进行研究，选取一系列运算出的阈值数据和对应的图像效果做一个分析性实验。 关键字：图像分割，阈值法，迭代法，Otsu法，最大熵值法 1 研究背景 1．1图像分割技术的机理 图像分割是将图像划分为若干互不相交的小区域的过程。小区域是某种意义下具有共同属性的像素连通集合，如物体所占的图像区域、天空区域、草地等。连通是指集合中任意两个点之间都存在着完全属于该集合的连通路径。对于离散图像而言，连通有4连通和8连通之分。图像分割有3种不同的方法，其一是将各像素划归到相应物体或区域的像素聚类方法，即区域法，其二是通过直接确定区域间的边界来实现分割的边界方法，其三是首先检测边缘像素，然后再将边缘像素连接起来构成边界的方法。 图像分割是图像理解的基础，而在理论上图像分割又依赖图像理解，两者是紧密关联的。图像分割在一般意义下十分困难的，目前的图像分割处于图像的前期处理阶段，主要针对分割对象的技术，是与问题相关的，如最常用到的利用阈值化处理进行的图像分割。 1．2数字图像分割技术存在的问题
虽然近年来对数字图像处理的研究成果越来越多,但由于图像分割本身所具有的难度,使研究没有大突破性的进展,仍然存在以下几个方面的问题。 现有的许多种算法都是针对不同的数字图像,没有一种普遍适用的分割算法。 缺乏通用的分割评价标准。对分割效果进行评判的标准尚不统一,如何对分割结果做出量化的评价是一个值得研究的问题,该量化测度应有助于视觉系统中的自动决策及评价算法的优劣,同时应考虑到均质性、对比度、紧致性、连续性、心理视觉感知等因素。 与人类视觉机理相脱节。随着对人类视觉机理的研究,人们逐渐认识到,已有方法大都与人类视觉机理相脱节,难以进行更精确的分割。寻找到具有较强的鲁棒性、实时性以及可并行性的分割方法必须充分利用人类视觉特性。 知识的利用问题。仅利用图像中表现出来的灰度和空间信息来对图像进行分割,往往会产生和人类的视觉分割不一致的情况。人类视觉分割中应用了许多图像以外的知识,在很多视觉任务中,人们往往对获得的图像已具有某种先验知识,这对于改善图像分割性能是非常重要的。试图寻找可以分割任何图像的算法目前是不现实,也是不可能的。人们的工作应放在那些实用的、特定图像分割算法的研究上,并且应充分利用某些特定图像的先验知识,力图在实际应用中达到和人类视觉分割更接近的水平。 1．3数字图像分割技术的发展趋势 从图像分割研究的历史来看,可以看到对图像分割的研究有以下几个明显的趋势。 对原有算法的不断改进。人们在大量的实验下，发现一些算法的效
附录2：外文翻译 Robust Analysis of Feature Spaces: Color Image Segmentation Abstract A general technique for the recovery of significant image features is presented. The technique is based on the mean shift algorithm, a simple nonparametric procedure for estimating density gradients. Drawbacks of the current methods (including robust clustering) are avoided. Feature space of any nature can be processed, and as an example, color image segmentation is discussed. The segmentation is completely autonomous, only its class is chosen by the user. Thus, the same program can produce a high quality edge image, or provide, by extracting all the significant colors, a preprocessor for content-based query systems. A 512 512 color image is analyzed in less than 10 seconds on a standard workstation. Gray level images are handled as color images having only the lightness coordinate. Keywords: robust pattern analysis, low-level vision, content-based indexing
New technique of the computer network Abstract The 21 century is an ages of the information economy, being the computer network technique of representative techniques this ages, will be at very fast speed develop soon in continuously creatively, and will go deep into the people's work, life and study. Therefore, control this technique and then seem to be more to deliver the importance. Now I mainly introduce the new technique of a few networks in actuality live of application. keywords Internet Network System Digital Certificates Grid Storage 1. Foreword Internet turns 36, still a work in progress Thirty-six years after computer scientists at UCLA linked two bulky computers using a 15-foot gray cable, testing a new way for exchanging data over networks, what would ultimately become the Internet remains a work in progress. University researchers are experimenting with ways to increase its capacity and speed. Programmers are trying to imbue Web pages with intelligence. And work is underway to re-engineer the network to reduce Spam (junk mail) and security troubles. All the while threats loom: Critics warn that commercial, legal and political pressures could hinder the types of innovations that made the Internet what it is today. Stephen Crocker and Vinton Cerf were among the graduate students who joined UCLA professor Len Klein rock in an engineering lab on Sept. 2, 1969, as bits of meaningless test data flowed silently between the two computers. By January, three other "nodes" joined the fledgling network.
Computer Program 1 Introduction Computer Program, set of instructions that directs a computer to perform someprocessing function or combination of functions. For the instructions to be carried out, a computer must execute a program, that is, the computer reads the program, and then follow the steps encoded in the program in a precise order until completion. A program can be executed many different times, with each execution yielding a potentially different result depending upon the options and data that the user gives the computer. Programs fall into two major classes: application programs and operating systems. An application program is one that carries out somefunction directly for a user, such as word processing or game-playing. An operating system is a program that manages the computer and the various resources and devices connected to it, such as RAM,hard drives, monitors, keyboards, printers, and modems,so that they maybe used by other programs. Examples of operating systems are DOS, Windows 95, OS\2, and UNIX. 2 Program Development Software designers create new programs by using special applications programs, often called utility programs or development programs. A programmer uses another type of program called a text editor to write the new program in a special notation called a programming language. With the text editor, the programmer creates a text file, which is an ordered list of instructions, also called the program source file. The individual instructions that make up the program source file are called source code. At this point, a special applications program translates the source code into machine language, or object code— a format that the operating system
关于三维目标识别的文献综述 前言： 随着计算机技术和现代信息处理技术的快速发展，目标识别已经迅速发展成为一种重要的工具与手段，目标识别是指一个特殊目标（或一种类型的目标）从其它目标（或其它类型的目标）中被区分出来的过程。它既包括两个非常相似目标的识别，也包括一种类型的目标同其他类型目标的识别。目标识别的基本原理是利用雷达回波中的幅度、相位、频谱和极化等目标特征信息，通过数学上的各种多维空间变换来估算目标的大小、形状、重量和表面层的物理特性参数，最后根据大量训练样本所确定的鉴别函数，在分类器中进行识别判决。它属于模式识别的范畴，也可以狭义的理解为图像识别。三维目标识别是以物体表面朝向的三维信息来识别完整的三维物体模型目标识别需要综合运用计算机科学、模式识别、机器视觉以及图像理解等学科知识。目标识别技术已广泛应用于国民经济、空间技术和国防等领域。 正文： 图像识别总的来说主要包括目标图像特征提取和分类两个方面。但是一般情况下，图像受各种因素影响，与真实物体有较大的差别，这样，就需要经过预处理、图像分割、特征提取、分析、匹配识别等一系列过程才能完成整个识别过程。 目前，最主流的三种三维物体识别研究思路是： 1)基于模型或几何的方法；
2)基于外观或视图的方法； 3)基于局部特征匹配的方法； 一、基于模型或几何的方法： 这种方法所识别的目标是已知的，原理就是利用传感器获得真实目标的三维信息并对信息进行分析处理，得到一种表面、边界及连接关系的描述，这里，三维物体识别中有两类最经常使用的传感器：灰度传感器和深度传感器，前者获取图像的每个像素点对应于一个亮度测量，而后者对应于从传感器到可视物体表面的距离；另一方面，利用CAD建立目标的几何模型，对模型的表面、边界及连接关系进行完整的描述。然后把这两种描述加以匹配就可以来识别三维物体。其流程如下图所示： 传感器数据获取过程，就是从现实生活中的真实物体中产生待识别的模型。分析/建模过程，是对传感器数据进行处理，从中提取与目标有关的独立应用特征。模型库的建立一般式在识别过程之前，即首先根据物体的某些特定特征建立一些关系以及将这些信息汇总成一个库。在模型匹配过程，系统通过从图像中抽取出的物体关系属性图，把物体描述与模型描述通过某种匹配算法进行比较、分析，最终得到与物体最相似的一种描述，从而确定物体的类型和空间位置。 基于模型的三维物体识别，需要着重解决以下4个问题：
The research of digital image processing technique 1 Introduction Interest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for autonomous machine perception. This chapter has several objectives: (1)to define the scope of the field that we call image processing; (2)to give a historical perspective of the origins of this field; (3)to give an idea of the state of the art in image processing by examining some of the principal area in which it is applied; (4)to discuss briefly the principal approaches used in digital image processing; (5)to give an overview of the components contained in a typical, general-purpose image processing system; and (6) to provide direction to the books and other literature where image processing work normally is reporter. 1.1What Is Digital Image Processing? An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pels, and pixels. Pixel is the term most widely used to denote the elements of a digital image. We consider these definitions in more formal terms in Chapter2. Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike human who are limited to the visual band of the electromagnetic (EM) spectrum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that human are not accustomed to associating with image. These include ultrasound, electron microscopy, and computer-generated images. Thus, digital image processing encompasses a wide and varied field of application. There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vision, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields a single number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computer to
英文翻译 A comprehensive overview of substations Along with the economic development and the modern industry developments of quick rising, the design of the power supply system become more and more completely and system. Because the quickly increase electricity of factories, it also increases seriously to the dependable index of the economic condition, power supply in quantity. Therefore they need the higher and more perfect request to the power supply. Whether Design reasonable, not only affect directly the base investment and circulate the expenses with have the metal depletion in colour metal, but also will reflect the dependable in power supply and the safe in many facts. In a word, it is close with the economic performance and the safety of the people. The substation is an importance part of the electric power system, it is consisted of the electric appliances equipments and the Transmission and the Distribution. It obtains the electric power from the electric power system, through its function of transformation and assign, transport and safety. Then transport the power to every place with safe, dependable, and economical. As an important part of power’s transport and control, the transformer substation must change the mode of the traditional design and control, then can adapt to the modern electric power system, the development of modern industry and the of trend of the society life. Electric power industry is one of the foundations of national industry and national economic development to industry, it is a coal, oil, natural gas, hydropower, nuclear power, wind power and other energy conversion into electrical energy of the secondary energy industry, it for the other departments of the national economy fast and stable development of the provision of adequate power, and its level of development is a reflection of the country's economic development an important indicator of the level. As the power in the industry and the importance of the national economy, electricity transmission and distribution of electric energy used in these areas is an indispensable component.。Therefore, power transmission and distribution is critical. Substation is to enable superior power plant power plants or power after adjustments to the lower load of books is an important part of power transmission. Operation of its functions, the capacity of a direct impact on the size of the lower load power, thereby affecting the industrial production and power consumption.Substation system if a link failure, the system will protect the part of action. May result in power outages and so on, to the production and living a great disadvantage. Therefore, the substation in the electric power system for the protection of electricity reliability,
实验报告 课程名称医学图像处理 实验名称图像分割 专业班级 姓名 学号 实验日期 实验地点 2015—2016学年度第 2 学期
050100150200250 图1 原图 3 阈值分割后的二值图像分析：手动阈值分割的阈值是取直方图中双峰的谷底的灰度值作为阈值，若有多个双峰谷底，则取第一个作为阈值。本题的阈值取
%例2 迭代阈值分割 f=imread('cameraman.tif'); %读入图像 subplot(1,2,1);imshow(f); %创建一个一行二列的窗口，在第一个窗口显示图像title('原始图像'); %标注标题 f=double(f); %转换位双精度 T=(min(f(:))+max(f(:)))/2; %设定初始阈值 done=false; %定义开关变量，用于控制循环次数 i=0; %迭代，初始值i=0 while~done %while ~done 是循环条件，~ 是“非”的意思，此 处done = 0; 说明是无限循环，循环体里面应该还 有循环退出条件，否则就循环到死了; r1=find(f<=T); %按前次结果对t进行二次分 r2=find(f>T); %按前次结果重新对t进行二次分 Tnew=(mean(f(r1))+mean(f(r2)))/2; %新阈值两个范围内像素平均值和的一半done=abs(Tnew-T)<1; %设定两次阈值的比较，当满足小于1时，停止循环， 1是自己指定的参数 T=Tnew; %把Tnw的值赋给T i=i+1; %执行循坏，每次都加1 end f(r1)=0; %把小于初始阈值的变成黑的 f(r2)=1; %把大于初始阈值的变成白的 subplot(1,2,2); %创建一个一行二列的窗口，在第二个窗口显示图像imshow(f); %显示图像 title('迭代阈值二值化图像'); %标注标题 图4原始图像图5迭代阈值二值化图像 分析：本题是迭代阈值二值化分割，步骤是：1.选定初始阈值，即原图大小取平均；2.用初阈值进行二值分割；3.目标灰度值平均背景都取平均；4.迭代生成阈值，直到两次阈值的灰 度变化不超过1，则稳定；5.输出迭代结果。