外文翻譯---MATLAB 在圖像邊緣檢測中的應(yīng)用

1、英文資料翻譯MATLAB application in image edge detectionMATLAB of the 1984 countries MathWorks company to market since, after 10 years of development, has become internationally recognized the best technology application software. MATLAB is not only a kind of direct, efficient computer langua

2、ge, and at the same time, a scientific computing platform, it for data analysis and data visualization, algorithm and application development to provide the most core of math and advanced graphics tools. According to provide it with the more than 500 math and engineering function, engineering and te

3、chnical personnel and scientific workers can integrated environment of developing or programming to complete their calculation.MATLAB software has very strong openness and adapt to sex. Keep the kernel in under the condition of invariable, MATLAB is in view of the different application subject of la

4、unch corresponding Toolbox (Toolbox), has now launched image processing Toolbox, signal processing Toolbox, wavelet Toolbox, neural network Toolbox and communication tools box, etc multiple disciplines special kit, which would place of different subjects research work.MATLAB image processing kit is

5、by a series of support image processing function from the composition, the support of the image processing operation: geometric operation area of operation and operation; Linear filter and filter design; Transform (DCT transform); Image analysis and strengthened; Binary image manipulation, etc. Imag

6、e processing tool kit function, the function can be divided into the following categories: image display; Image file input and output; Geometric operation; Pixels statistics; Image analysis and strengthened; Image filtering; Sex 2 d filter design; Image transformation; Fields and piece of operation;

7、 Binary image operation; Color mapping and color space transformation; Image types and type conversion; Kit acquiring parameters and Settings.1. Edge detection thisUse computer image processing has two purposes: produce more suitable for human observation and identification of the images; Hope can b

8、y the automatic computer image recognition and understanding.No matter what kind of purpose to, image processing the key step is to contain a variety of scenery of decomposition of image information. Decomposition of the end result is that break down into some has some kind of characteristics of the

9、 smallest components, known as the image of the yuan. Relative to the whole image of speaking, this the yuan more easily to be rapid processing.Image characteristics is to point to the image can be used as the sign of the field properties, it can be divided into the statistical features of the image

10、 and image visual, two types of levy. The statistical features of the image is to point to some people the characteristics of definition, through the transform to get, such as image histogram, moments, spectrum, etc.; Image visual characteristics is refers to person visual sense can be directly by t

11、he natural features, such as the brightness of the area, and texture or outline, etc. The two kinds of characteristics of the image into a series of meaningful goal or regional process called image segmentation.The image is the basic characteristics of edge, the edge is to show its pixel grayscale a

12、round a step change order or roof of the collection of those changes pixels. It exists in target and background, goals and objectives, regional and region, the yuan and the yuan between, therefore, it is the image segmentation dependent on the most important characteristic that the texture character

13、istics of important information sources and shape characteristics of the foundation, and the image of the texture characteristics and the extraction of shape often dependent on image segmentation. Image edge extraction is also the basis of image matching, because it is the sign of position, the chan

14、ge of the original is not sensitive, and can be used for matching the feature points.The edge of the image is reflected by gray not continuity. Classic edge extraction method is investigation of each pixel image in an area of the gray change, use edge first or second order nearby directional derivat

15、ive change rule, with simple method of edge detection, this method called edge detection method of local operators.The type of edge can be divided into two types: (1) step representation sexual edge, it on both sides of the pixel gray value varies significantly different; (2) the roof edges, it is l

16、ocated in gray value from the change of increased to reduce the turning point. For order jump sexual edge, second order directional derivative in edge is zero cross; For the roof edges, second order directional derivative in edge take extreme value.If a pixel fell in the image a certain object bound

17、ary, then its field will become a gray level with the change. The most useful to change two features is the rate of change and the gray direction, they are in the range of the gradient vector and the direction to said. Edge detection operator check every pixel grayscale rate fields and evaluation, a

18、nd also include to determine the directions of the most use based on directional derivative deconvolution method for masking.Digital image processing technique has been widely applied to the biomedical field, the use of computer image processing and analysis, and complete detection and recognition o

19、f cancer cells can help doctors make a diagnosis of tumor cancers. Need to be made in the identification of cancer cells, the quantitative results, the human eye is difficult to accurately complete such work, and the use of computer image processing to complete the analysis and identification of the

20、 microscopic images have made great progress. In recent years, domestic and foreign medical images of cancer cells testing to identify the researchers put forward a lot of theory and method for the diagnosis of cancer cells has very important meaning and practical value. Cell edge detection is the c

21、ell area of the number of roundness and color, shape and chromaticity calculation and the basis of the analysis their test results directly affect the analysis and diagnosis of the disease. Classical edge detection operators such as Sobel operator, Laplacian operator, each pixel neighborhood of the

22、image gray scale changes to detect the edge. Although these operators is simple, fast, but there are sensitive to noise, get isolated or in short sections of a continuous edge pixels, overlapping the adjacent cell edge defects, while the optimal threshold segmentation and contour extraction method o

23、f combining edge detection, obtained by the iterative algorithm for the optimal threshold for image segmentation, contour extraction algorithm, digging inside the cell pixels, the last remaining part of the image is the edge of the cell, change the processing order of the traditional edge detection

24、algorithm, by MATLAB programming, the experimental results that can effectively suppress the noise impact at the same time be able to objectively and correctly select the edge detection threshold, precision cell edge detection.2. Edge detection of MATLABMATLAB image processing toolkit defines the ed

25、ge () function is used to test the edge of gray image.(1) BW = edge (I, "method"), returns and I size binary image BW, including elements of 1 said is on the edge of the point, 0 means the edge points. Method for the following a string of:1) soble: the default value, with derivative Sobel

26、edge detection approximate measure, to return to a maximum gradient edge;2) prewitt: with the derivative prewitt approximate edge detection, a maximum gradient to return to edge;3) Roberts: with the derivative Roberts approximate edge detection margins, return to a maximum gradient edge;4) the log:

27、use the Laplace operation gaussian filter to I carry filtering, through the looking for 0 intersecting detection of edge;5) zerocross: use the filter to designated I filter, looking for 0 intersecting detection of edge.(2) BW = edge (I, "method", thresh) with thresh designated sensitivity

28、threshold value, rather than the edge of all not thresh are ignored.(3) BW = edge (I, "method" thresh, direction, for soble and prewitt method specified direction, direction for string, including horizontal level said direction; Vertical said to hang straight party; Both said the two direc

29、tions (the default).(4) BW = edge (I, 'log', thresh, log sigma), with sigma specified standard deviation.(5) BW, thresh = edge (.), the return value of a function in fact have multiple (" BW "and" thresh "), but because the brace up with u said as a matrix, and so can be

30、thought a return only parameters, which also shows the introduction of the concept of matrix MATLAB unity and superiority.3. Last wordMATLAB has strong image processing function, provide a simple function calls to realize many classic image processing method. Not only is the image edge detection, in

31、 transform domain processing, image enhancement, mathematics morphological processing, and other aspects of the study, MATLAB can greatly improve the efficiency rapidly in the study of new ideas.MATLAB 在 圖 像 邊 緣 檢 測 中 的 應(yīng) 用MATLAB自1984年由國MathWorks公司推向市場以來,歷經(jīng)十幾年的發(fā)展,現(xiàn)已成為國際公認(rèn)的最優(yōu)秀的科技應(yīng)用軟件。MATLAB既是一種直觀、高效的

32、計(jì)算機(jī)語言,同時(shí)又是一個(gè)科學(xué)計(jì)算平臺(tái),它為數(shù)據(jù)分析和數(shù)據(jù)可視化、算法和應(yīng)用程序開發(fā)提供了最核心的數(shù)學(xué)和高級圖形工具。根據(jù)它提供的500多個(gè)數(shù)學(xué)和工程函數(shù), 工程技術(shù)人員和科學(xué)工作者可以在它的集成環(huán)境中交互或編程以完成各自的計(jì)算。 MATLAB軟件具有很強(qiáng)的開放性和適應(yīng) 性。在保持內(nèi)核不變的情況下,MATLAB 可以針對不同的應(yīng)用學(xué)科推出相應(yīng)的工具箱(Toolbox),目前已經(jīng)推出了圖像處理工具箱、信號(hào)處理工具箱、小波工具箱、神經(jīng)網(wǎng)絡(luò)工具箱以及通信工具 箱等多個(gè)學(xué)科的專用工具箱,極大地方便了不同學(xué)科的研究工作。MATLAB的圖像處理工具包是由一系列支持圖像處理操作的函數(shù)組成的,所支持的圖像處理操

33、作有:幾何操作區(qū)域操作和塊操作;線性濾波和濾波器設(shè)計(jì);變換(DCT變換);圖像分析和增強(qiáng);二值圖像操作等。圖像處理工具包的函數(shù),按功能可以分為以下幾類:圖像顯示;圖像文件輸入與輸出;幾何操作;像素值統(tǒng)計(jì);圖像分析與增強(qiáng);圖像濾波; 性二維濾波器設(shè)計(jì);圖像變換;領(lǐng)域和塊操作;二值圖像操作;顏色映射和顏色空間轉(zhuǎn)換;圖像類型和類型轉(zhuǎn)換;工具包參數(shù)獲取和設(shè)置等。與其他工具包一樣,用戶還可以根據(jù)需要書寫自己的 函數(shù),以滿足特定的需要,也可以將這個(gè)工具包和信號(hào)處理工具包或小波工具包等其他工具包聯(lián)合起來使用。1. 邊緣檢測概述利用計(jì)算機(jī)進(jìn)行圖像處理有兩個(gè)目的:產(chǎn)生更適合人類觀察和識(shí)別的圖像;希望能由計(jì)算機(jī)自

34、動(dòng)識(shí)別和理解圖像。無論為了哪種目的,圖像處理中關(guān)鍵的一步就是對包含有大量各式各樣景物信息的圖像進(jìn)行分解。分解的最終結(jié)果是被分解成一些具有某種特征的最小成分,稱為圖像的基元。相對于整幅圖像來說,這種基元更容易被快速處理。圖像的特征是指圖像場中可用作標(biāo)志的屬性,它可以分為圖像的統(tǒng)計(jì)特征和圖像的視覺特 征兩類。圖像的統(tǒng)計(jì)特征是指一些人為定義的特征，通過變換才能得到,如圖像的直方圖、矩、頻譜等;圖像的視覺特征是指人的視覺可直接感 受到的自然特征,如區(qū)域的亮度、紋理或輪廓等。利用這兩類特征把圖像分解成一系列有意義的目標(biāo)或區(qū)域的過程稱為圖像的分割。圖像最基本的特征是邊緣,所謂邊緣是指其周圍像素灰度有階躍變

35、化或屋頂變化的那些像素的集合。它存在于目標(biāo)與背景、目標(biāo)與目標(biāo)、區(qū)域與區(qū)域、基元與基元之間,因此,它是圖像分割所依賴的最重要的特征,也是紋理特征的重要信息源和形狀特征的基礎(chǔ),而圖像的紋理形狀特征的提取又常常要依賴于圖像分割。圖像的邊緣提取也是圖像匹配的基礎(chǔ),因?yàn)樗俏恢玫臉?biāo)志,對灰度的變化不敏感,可作為匹配的特征點(diǎn)。圖像的邊緣是由灰度不連續(xù)性所反映的。經(jīng)典的邊緣提取方法是考察圖像的每個(gè)像素在某個(gè)區(qū)域內(nèi)灰度的變化,利用邊緣鄰近一階或二階方向?qū)?shù)變化規(guī)律,用簡單的方法檢測邊緣,這種方法稱為邊緣檢測局部算子法。邊緣的種類可以分為兩種：階躍性邊緣,它兩邊的像素的灰度值有顯著的不同；屋頂狀邊緣,它位于灰度

36、值從增加到減少的變化轉(zhuǎn)折點(diǎn)。對于階躍性邊緣，二階方向?qū)?shù)在邊緣處呈零交叉；對于屋頂狀邊緣，二階方向?qū)?shù)在邊緣處取極值。如果一個(gè)像素落在圖像中某一個(gè)物體的邊界上,那么它的領(lǐng)域?qū)⒊蔀橐粋€(gè)灰度級的變化帶。對這種變化最有用的兩個(gè)特征是灰度的變化率和方向,它們分別以梯度向量的幅度和方向來表示。邊緣檢測算子檢查每個(gè)像素的領(lǐng)域并對灰度變化率進(jìn)行量化,也包括方向的確定,大多數(shù)使用基于方向?qū)?shù)掩模求卷積的方法。數(shù)字圖像處理技術(shù)已被廣泛應(yīng)用到生物醫(yī)學(xué)領(lǐng)域，運(yùn)用計(jì)算機(jī)對圖像進(jìn)行處理和分析，并進(jìn)一步完成癌細(xì)胞的檢測與識(shí)別，能有效的協(xié)助醫(yī)生對腫瘤癌癥做出診斷。在識(shí)別癌細(xì)胞時(shí)，需要做出定量的結(jié)果，人眼很難準(zhǔn)確的完成這類工作，而利用