【正文】 （上）（ 2020 年版） . （論文）集。要求語言簡(jiǎn)練，步驟清晰。學(xué)生根據(jù)畢業(yè)設(shè)計(jì)指導(dǎo)書的選題和指導(dǎo)教師的安排，應(yīng)該做好如下的準(zhǔn)備，包括：認(rèn)真閱讀畢業(yè)設(shè)計(jì)任務(wù)書的內(nèi)容，熟悉施工圖紙，調(diào)查了解與設(shè)計(jì)內(nèi)容相關(guān)的資料；收集相關(guān)的工具書。 資金籌措條件 （ 1）工程合同價(jià) C=工程報(bào)價(jià)； （ 2）開工前業(yè)主撥付工程備料款 A=20% C。 FIGURE Line masks. Let R1, R2, R3, and R4 denote the responses of the masks in Fig. , from left to right, where the R39。我們僅僅定義了一條與任何需要考察的點(diǎn)所在的邊緣方向相垂直的剖面線，并如前面討論的那樣，對(duì)結(jié)果進(jìn)行了解釋。同樣，二階導(dǎo)數(shù)的符號(hào)可以用于判斷一個(gè)邊緣像素是在邊緣亮的一邊還是暗的一邊。圖 106(b)顯示了兩個(gè)區(qū)域之間邊緣的一條水平的灰度級(jí)剖面線。結(jié)果，邊緣被更精確地模擬成具有“類斜面”的剖面，如圖 105(b)所示。然而，我們已經(jīng)在 節(jié)中用一定的篇幅解釋了一條邊緣和一條邊界的區(qū)別。本節(jié)中，我們討論實(shí)現(xiàn)一階和二階數(shù)字導(dǎo)數(shù)檢測(cè)一幅圖像中邊緣的方法。對(duì)于與這個(gè)例子類似的應(yīng)用，讓門限等于最大值是一個(gè)好的選擇，因?yàn)檩斎雸D像是二值的，并且我們要尋找的是最強(qiáng)響應(yīng)。圖 104(b)顯示了得到的結(jié)果的絕對(duì)值。換句話說，如果我們對(duì)檢測(cè)圖像中由給定模板定義的方向上的所有線感興趣 .只需要簡(jiǎn)單地通過整幅圖像運(yùn)行模板，并對(duì) 得到的結(jié)果的絕對(duì)值設(shè)置門限即可。 Horizontal +45176。畫一個(gè)元素為 1的簡(jiǎn)單陣列，并且使具有不同灰度級(jí) (如 5)的一行水平穿過陣列，可以很容易驗(yàn)證這一點(diǎn)。注意， 模板系數(shù)之和為零表示在灰度級(jí)為常數(shù)的區(qū)域，模板響應(yīng)為零。 點(diǎn)檢測(cè) 在一幅圖像中，孤立點(diǎn)的檢測(cè)在理論上是簡(jiǎn)單的。這種方法特別具有吸引力，因?yàn)樗鼘⒈菊碌谝徊糠痔岬降膸追N分割屬性技術(shù)結(jié)合起來了。 本章中，我們將對(duì)剛剛提到的兩類特性各討論一些方法。在某些情況下，比如工業(yè)檢測(cè)應(yīng)用，至少有可能對(duì)環(huán)境進(jìn)行適度控制的檢測(cè)。 分割將圖像細(xì)分為構(gòu)成它的子區(qū)域或?qū)ο?。分割的程度取決于要解決的問題。有經(jīng)驗(yàn)的圖像處理系統(tǒng)設(shè)計(jì)師總是將相當(dāng)大的注意力放在這類可能性上。我們先從適合于檢測(cè)灰度級(jí)的不連續(xù)性的方法展開，如點(diǎn)、線和邊緣。我們將以圖像分割的應(yīng)用方面進(jìn)行討論來結(jié)束本章。使用如圖 102(a)所示的模板，如果 |R| ≥ T () 我們說在模板中心的位置上已經(jīng)檢測(cè)到一個(gè)點(diǎn)。 1 1 1 1 8 1 1 1 1 （ a） （ b） （ c） （ d） 圖 102 （ a）點(diǎn)檢測(cè)模板，（ b）帶有通孔的渦輪葉片的 X 射線，（ c）點(diǎn)檢測(cè)的 結(jié)果，（ d）使用式（ ）得到的結(jié)果（原圖由 XTEK 系統(tǒng)公司提供） 例 圖像中孤立點(diǎn)的檢瀏 我們以圖 102(b)功為輔助說 明如何從一幅圖中將孤立點(diǎn)分割出來 .這幅 X射線圖顯示了一個(gè)帶有通孔的噴氣發(fā)動(dòng)抓渦槍葉片，通孔位于圈像的右上象限。同樣的實(shí)驗(yàn)可以顯示出圖 103 中的第 2個(gè)模板對(duì)于 45176。 Vertical 45176。留下的點(diǎn)是有最強(qiáng)響應(yīng)的點(diǎn)。注意，圖像中所有水平和垂直的部分都被除去了。圖 104(c)顯示了在白色區(qū)所有通 過門限檢測(cè)的點(diǎn)。在 節(jié)介紹圖像增強(qiáng)的內(nèi)容中介紹過這些導(dǎo)數(shù)。從根本上講，如我們將要看到的，一條邊緣是一個(gè)“ 局部”概念，而由于其定義的方式，一個(gè)區(qū)域的邊界是一個(gè)更具有整體性的概念。斜坡部分與邊緣的模糊程度成比例。這個(gè)圖形也顯示出灰度級(jí)剖面線的一階和二階導(dǎo)數(shù)。我們注意到圍繞一條邊緣，二階導(dǎo)數(shù)的兩條附加性質(zhì) (1)對(duì)圖像中的每 條邊緣二階導(dǎo)數(shù)生成兩個(gè)值 (一個(gè) 5 不希望得到的特點(diǎn) )。 注：出自 Digital Image Processing 2nd Edition . Prentice Hall 1 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 ). 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 ponents 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 puterized 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 2 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