圖像分割外文翻譯---一種在線圖像編碼識(shí)別系統(tǒng)的設(shè)計(jì)

1、<p>　　一種在線圖像編碼識(shí)別系統(tǒng)的設(shè)計(jì)</p><p>　　摘要：本文介紹了在線圖像編碼字符識(shí)別系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)過(guò)程，對(duì)其中重點(diǎn)環(huán)節(jié)進(jìn)行了分析與研究，給出了主要環(huán)節(jié)問(wèn)題的解決方法，在識(shí)別算法上，結(jié)合模板匹配與特征識(shí)別，提出了基于特征加權(quán)的模板匹配算法，該算法對(duì)提高字符識(shí)別率提到了較好的作用。</p><p>　　關(guān)鍵詞：圖像處理;模式識(shí)別;特征加權(quán);軟件設(shè)計(jì)</p>

2、;<p><b>　　0引言</b></p><p>　　圖像編碼字符識(shí)別的研究目前仍是國(guó)內(nèi)外一個(gè)重點(diǎn)研究課題，它具有廣泛的應(yīng)用背景，比如車牌號(hào)碼自動(dòng)識(shí)別、郵政編碼的自動(dòng)識(shí)別、試卷自動(dòng)閱讀、報(bào)表自動(dòng)處理等，由于這種在線圖像編碼字符的識(shí)別都具有一些共性，本文結(jié)合在線輪胎編碼字符識(shí)別系統(tǒng)的設(shè)計(jì)，對(duì)一般圖像編碼字符識(shí)別系統(tǒng)進(jìn)行了闡述，對(duì)關(guān)鍵環(huán)節(jié)進(jìn)行了研究與分析，該方法對(duì)其它在線圖像編

3、碼字符系統(tǒng)的開(kāi)發(fā)具有一定指導(dǎo)意義。</p><p>　　1在線圖像編碼識(shí)別系統(tǒng)流程</p><p>　　在線圖像編碼字符識(shí)別系統(tǒng)主要包括數(shù)字圖像的采集、存儲(chǔ)、圖像預(yù)處理、編碼圖像提取、編碼特征提取、編碼識(shí)別和后續(xù)處理等一些環(huán)節(jié)，其流程圖如圖1所示。</p><p>　　圖1 在線圖像編碼字符識(shí)別系統(tǒng)流程圖</p><p>　　在線輪胎圖像編

4、碼字符識(shí)別系統(tǒng)要求對(duì)通過(guò)生產(chǎn)流水線上每一個(gè)輪胎采集含有輪胎編碼的圖像，然后通過(guò)對(duì)圖像的處理，提取出輪胎編碼特征，采用合適的識(shí)別算法將每一位編碼字符進(jìn)行識(shí)別。由于輪胎編碼字符在輪胎上有一定變形，且攝像角度不同，得到的編碼圖像差異也很大，規(guī)律性差，所以編碼圖像的預(yù)處理和識(shí)別算法的選取顯得尤為重要。</p><p><b>　　2圖像采集與存儲(chǔ)</b></p><p>　　

5、在線編碼圖像通常使用數(shù)碼攝像機(jī)、數(shù)碼照相機(jī)、數(shù)碼攝像頭等設(shè)備采集并輸入計(jì)算機(jī)進(jìn)行處理，本系統(tǒng)采用QuickCamPro4000數(shù)碼攝像頭采集輪胎編碼圖像,直接按JPG格式存儲(chǔ)。</p><p>　　編碼圖像一般都要先轉(zhuǎn)成BMP圖像格式，因?yàn)锽MP格式己經(jīng)成為PC領(lǐng)域事實(shí)上的標(biāo)準(zhǔn)——幾乎所有為Windows操作系統(tǒng)設(shè)計(jì)的圖像處理軟件都支持這種格式的圖像。BMP是Windows的原始位圖格式，它可以用于保存任意類型的

6、位圖數(shù)據(jù)，可以支持所有的屏幕分辨率和Windows所支持的顏色組合。一般情況下，為了保證顯示的高效率，它對(duì)圖像數(shù)據(jù)沒(méi)有任何的壓縮，所以一幅很小的位圖就可能占據(jù)相當(dāng)大的空間。</p><p>　　BMP位圖文件包括位圖文件頭、位圖信息頭、調(diào)色板、位圖數(shù)據(jù)區(qū)四個(gè)部分，位圖文件頭由14個(gè)字節(jié)構(gòu)成，位圖信息頭由40個(gè)字節(jié)構(gòu)成，調(diào)色板的大小取決于色彩數(shù)，單色圖像調(diào)色</p><p>　　板占8個(gè)字節(jié)

7、，16色圖像調(diào)色板占64個(gè)字節(jié)，256色圖像調(diào)色板占1024個(gè)字節(jié)，224色圖像沒(méi)有調(diào)色板，位圖數(shù)據(jù)區(qū)內(nèi)數(shù)據(jù)按行順序自下而上、自左而右排列。</p><p><b>　　3圖像預(yù)處理</b></p><p>　　圖像預(yù)處理主要包括有：圖像灰度化、圖像降噪與增強(qiáng)、編碼區(qū)邊緣檢測(cè)、圖像幾何校正、編碼區(qū)圖像提取、編碼圖像二值化、字符分割、字符歸一化等。下面介紹幾個(gè)關(guān)鍵環(huán)節(jié)的

8、處理過(guò)程。</p><p>　　3.1 圖像灰度化處理</p><p>　　編碼圖像通常是彩色的，實(shí)際識(shí)別用的圖像是灰度圖，所在需要先將彩色編碼圖像轉(zhuǎn)換為灰度圖像。在RGB顏色模型中，如果R=G=B，則顏色（R，G，B）表示一種黒白顏色，其中R=G=B的值叫灰度值，灰度化處理就是使彩色的R、G、B分量值相等的過(guò)程。常用灰度化處理方法是加權(quán)平均值法，即</p><p>

9、;　　R=G=B=(WRR+WGG+WBB)/3</p><p>　　其中，WR、WG、WB分別是R、G、B的權(quán)值，實(shí)驗(yàn)和理論證明，當(dāng)WR=0.3, WG=0.59, WB=0.11時(shí)，即當(dāng)R=G=B=0.30R+0.59G+0.11B時(shí)，能得到最合理的灰度圖像。</p><p>　　3.2 圖像增強(qiáng)處理</p><p>　　3.2.1 直接灰度變換</p&g

10、t;<p>　?、倬€性灰度變換：假設(shè)圖像灰度是線性變化的，如原圖像f(x,y)灰度范圍為[a,b]，要求變換后圖像灰度范圍達(dá)到[c,d]，根據(jù)線性規(guī)律，則變換后圖像g(x,y)為：</p><p><b>　?。?）</b></p><p>　　②非線性變換——對(duì)數(shù)變換和指數(shù)變換。當(dāng)需要擴(kuò)展低灰度區(qū)、壓縮高灰度區(qū)時(shí)使用對(duì)數(shù)變換，當(dāng)需要擴(kuò)展高灰度區(qū)時(shí)使用指

11、數(shù)變換。</p><p>　　3.2.2 平滑濾波—降噪</p><p>　　由于噪聲對(duì)應(yīng)圖像中的區(qū)域邊緣等灰度值具有較大較快變化的部分，屬高頻分量，所以使用低通濾波器（即平滑濾波器）降噪。同時(shí)平滑還可以使圖像模糊，有利于在提取較大的目標(biāo)前去除較小的細(xì)節(jié)或?qū)⒛繕?biāo)內(nèi)的小間斷連接起來(lái)。</p><p>　　平滑降噪的方法是使用模板對(duì)圖像進(jìn)行卷積運(yùn)算，線性平滑濾波器最常

12、用的模板是如圖2所示的3×3模板，將此模板與圖像中像素按如下方法進(jìn)行卷積運(yùn)算，可得到平滑降噪的圖像。</p><p>　?、賹⒛０逶趫D中漫游，并將模板中心與圖中每個(gè)像素位置重合；</p><p>　?、趯⒛０迳舷禂?shù)與模板下對(duì)應(yīng)像素相乘；</p><p><b>　?、蹖⑺谐朔e相加；</b></p><p>　

13、?、軐⒑唾x給圖中對(duì)應(yīng)模板中心位置的像素。</p><p>　　非線性平滑濾波器最常用的是中值濾波器，它將區(qū)域中所有的值按大小進(jìn)行排序，將排序后位于中間的像素值賦予中心像素。中值濾波可有效地去除隨機(jī)噪聲，能得到較好的視覺(jué)效果。</p><p>　　3.3 編碼區(qū)邊緣檢測(cè)</p><p>　　邊緣是灰度值不連續(xù)的結(jié)果，可利用求一階和二階導(dǎo)數(shù)的方法檢測(cè)到。因?yàn)樵谶吘壍貛?dǎo)

14、數(shù)值大，而非邊緣的地方導(dǎo)數(shù)值小。由于數(shù)字圖像是離散的，不能求導(dǎo)數(shù)，可以通過(guò)卷積的方法用差分近似代替微分。</p><p>　　效果較好的邊緣檢測(cè)算法是Sobel算子。Sobel算子是一種梯度幅值，分別利用垂直算子Sx、水平算子Sy來(lái)獲取編碼區(qū)垂直邊緣和水平邊緣，即在水平和垂直方向上使用如圖3所示的兩個(gè)不同的卷積模板，得到如圖4所示的邊緣檢測(cè)結(jié)果。</p><p>　　圖2 平滑濾波器模板

15、 圖3 Sobel邊緣檢測(cè)模板 圖4 Sobel算子邊緣檢測(cè)結(jié)果</p><p>　　3.4 圖像幾何校正</p><p>　　Hough變換可以檢測(cè)出編碼區(qū)圖像傾斜角度，根據(jù)此角度進(jìn)行旋轉(zhuǎn)變換可使編碼區(qū)圖像得到校正。</p><p>　　Hough變換可以將圖像空間XY中的直線（y=px+q）檢測(cè)問(wèn)題轉(zhuǎn)換到參數(shù)空間PQ中點(diǎn)的檢測(cè)問(wèn)題，在參數(shù)空間PQ里，建立一

16、個(gè)累加數(shù)組Sum(p,q)，對(duì)每一個(gè)圖像空間中給定邊緣點(diǎn)，讓p取遍所有可能值，根據(jù)直線方程q=-xp+y計(jì)算出對(duì)應(yīng)的q，對(duì)Sum(p,q)進(jìn)行累加，得到Sum(p,q)的值就是在(p,q)處共線的點(diǎn)的個(gè)數(shù)，(p,q)的值就是圖像空間中直線的斜率和截距，由斜率得到圖像編碼區(qū)水平邊緣角度。</p><p><b>　　3.5 字符切割</b></p><p>　　通過(guò)對(duì)編

17、碼字符區(qū)直接進(jìn)行水平掃描，由字符間距一般可以將字符區(qū)域分割出來(lái)。</p><p>　　也可以通過(guò)對(duì)編碼字符區(qū)做垂直方向投影運(yùn)算，根據(jù)字符大致寬度與字符總數(shù)，對(duì)字符進(jìn)行切割。如圖5所示是編碼字符區(qū)及對(duì)應(yīng)垂直投影圖。</p><p>　　圖5 編碼字符及對(duì)應(yīng)垂直投影 圖6 線性插值示意圖</p><p>　　3.6 字符歸一化處理</p>

18、<p>　　對(duì)分割出的字符從四個(gè)方向掃描，確定字符邊界，然后采用線性插值方法對(duì)每個(gè)字符作歸一化處理，使每個(gè)字符歸一為32×16點(diǎn)陣。圖6為線性插值示意圖，根據(jù)線性原理，f(x1)可由公式（2）計(jì)算：</p><p><b>　?。?）</b></p><p><b>　　4識(shí)別算法設(shè)計(jì)</b></p><p

19、>　　字符識(shí)別一般采取特征判別或模板匹配的方法，特征判別是根據(jù)特征抽取的程度分階段的、用結(jié)構(gòu)分析的辦法完成字符的識(shí)別。模板匹配即是根據(jù)字符的知識(shí)采取按形匹配的方法，模板匹配一般分為兩類：一類是直接利用輸入的二維平面圖像與字典中記憶的圖形進(jìn)行匹配；另一類是抽出部分特征與字典進(jìn)行匹配。</p><p>　　輪胎編碼圖像中字符僅涉及部分英文字符和10個(gè)阿拉伯?dāng)?shù)字，字符較少，結(jié)構(gòu)相對(duì)簡(jiǎn)單，因此具體識(shí)別時(shí)，既可以采用

20、圖形匹配的方法，也可以采用結(jié)構(gòu)分析的方法。但由于輪胎上編碼字符有一定變形，且有斷裂現(xiàn)象，所以直接模板匹配與直接特征抽取方法識(shí)別率都不理想，本系統(tǒng)使用了模板匹配與特征識(shí)別相結(jié)合的基于特征加權(quán)的模板匹配識(shí)別算法，其字符識(shí)別率比簡(jiǎn)單模板匹配算法和特征識(shí)別算法識(shí)別率都有不同程度的提高。</p><p>　　基于特征加權(quán)的模板匹配識(shí)別算法基本思路是：給模板中有字符筆畫的點(diǎn)分配不同的權(quán)重，位于筆畫中心的點(diǎn)權(quán)重最高，位于筆畫邊

21、緣的點(diǎn)權(quán)重最低，然后將樣本模板與標(biāo)準(zhǔn)模板逐點(diǎn)模糊匹配，按模糊識(shí)別規(guī)則識(shí)別。 </p><p><b>　　5結(jié)論</b></p><p>　　本本文結(jié)合輪胎編碼識(shí)別系統(tǒng)的實(shí)現(xiàn)對(duì)在線圖像字符編碼識(shí)別系統(tǒng)的設(shè)計(jì)進(jìn)行了闡述，提出了一種模板匹配與特征匹配相結(jié)合的識(shí)別算法，該方法對(duì)傳統(tǒng)的模板匹配算法進(jìn)行了改進(jìn)，提高了變形、斷裂等字符的識(shí)別率。這種方法在試驗(yàn)中得到了驗(yàn)證，取得了令

22、人滿意的效果。</p><p>　　文結(jié)合輪胎編碼識(shí)別系統(tǒng)的實(shí)現(xiàn)對(duì)在線圖像字符編碼識(shí)別系統(tǒng)的設(shè)計(jì)進(jìn)行了闡述，提出了一種模板匹配與特征匹配相結(jié)合的識(shí)別算法，該方法對(duì)傳統(tǒng)的模板匹配算法進(jìn)行了改進(jìn)，提高了變形、斷裂等字符的識(shí)別率。這種方法在試驗(yàn)中得到了驗(yàn)證，取得了令人滿意的效果。</p><p><b>　　外文原文（復(fù)印件）</b></p><p>

23、;　　The Development of A Kind of Online Image Code Recognition System</p><p>　　Abstract: This paper describes the design and the implement of online image coding char recognition system. It analyses and resea

24、rches the important contents about the system. Then it provides the solutions of main problems. In recognition algorithm, combining template matching with feature recognition, it put forword an improved template matching

25、 algorithm based on feature weights. The algorithm can obviously improve the char recognition ratio.</p><p>　　Keyword: image processing; pattern recognition; feature weights; software design</p><p

26、>　　0 Introductions</p><p>　　Character recognition of image coding is still the subject of intense study at home and abroad, it has broad applications, such as Automatic number plate recognition, postal co

27、de of the automatic identification, automatic reading papers, reports, automatic processing, because of this online image coded character recognition has some common, this paper online tire coding character recognition s

28、ystem for the general image coding character recognition system has been elaborated on the key link of t</p><p>　　An online image coding identification system processes</p><p>　　Online image cod

29、ing character recognition system includes digital image capture, storage, image preprocessing, encoding the image extraction, feature extraction coding, coding identification and follow-up treatment of some aspects of it

30、s flow chart shown in Figure 1.</p><p>　　Figure 1-line character recognition image coding system flowchart</p><p>　　Online tire image coding character recognition system requires the production

31、pipeline through the acquisition of each tire with tire encoded image, and then through image processing, coding to extract features of the tire, using the appropriate recognition algorithm to identify each coded charact

32、er. Tire coding characters as a certain deformation in the tires, and different camera angles, are also great differences in the coding images, regularity is poor, so coded image preprocessing and recog</p><p&

33、gt;　　Image Acquisition and Storage</p><p>　　Line coding commonly used digital camera images, digital cameras, digital video cameras capture and processed in computer, the system uses QuickCamPro4000 tire cod

34、ing digital camera image capture, directly from JPG format.</p><p>　　Coded images generally must first convert BMP image format, because the BMP format has become the de facto standard PC in the field - almo

35、st all of the Windows operating system designed for image processing software to support this format of the image. BMP is the original Windows bitmap format, which can be used to save any type of digital map data, can su

36、pport all Windows supported screen resolution and color combination. Under normal circumstances, in order to ensure the display of high effici</p><p>　　BMP bitmap file includes the bitmap file header, bitmap

37、 information header, palette, bitmap data area of four parts, bitmap file header from 14 bytes constitute the bitmap header from 40 bytes composition, tone color palette depends on the number of monochrome color images.&

38、lt;/p><p>　　Board accounted for 8 bytes, 16-color palette images accounted for 64 bytes, 256-color palette image 1024 bytes total, 224-color images without color palette, the bitmap data from the region under t

39、he order of the data by row and on the arrangement from left to right.</p><p>　　Preprocessing</p><p>　　Image preprocessing includes are: gray image, image noise reduction and enhancement, coding

40、, edge detection, image geometry correction, image coding region of extraction, encoding image binarization, character segmentation, character normalization and so on. Here are some key aspects of the process.</p>

41、<p>　　gray image processing</p><p>　　Images are usually color coded, the actual identification with the image is grayscale, where the need to convert first color-coded images to grayscale. In the RGB c

42、olor model, if R = G = B, then color (R, G, B) indicates a Black white color, in which R = G = B is called the value of gray value, gray level processing is to make the color of the R , G, B component value equal to the

43、process. Gray-scale processing methods are commonly used weighted average method, that is,</p><p>　　R = G = B = (WRR + WGG + WBB) / 3</p><p>　　Which, WR, WG, WB are the R, G, B the weight of exp

44、erimental and theoretical proof, when WR = 0.3, WG = 0.59, WB = 0.11, that is when R = G = B = 0.30R +0.59 G +0.11 B, can be the most reasonable grayscale.</p><p>　　3.2 image enhancement processing</p>

45、<p>　　3.2.1 Direct gray-scale transformation</p><p>　　① linear gray level transformation: if the image gray scale is linear, as in the original image f (x, y) gray-scale range of [a, b], asked the tra

46、nsformed image intensity range of up to [c, d], According to the linear law, the transformed image g (x, y) as:</p><p><b>　　(1)</b></p><p>　?、?nonlinear transformation -- log transfo

47、rmation and exponential transformation:When the need to expand low gray zone, gray zone of high compression used on the log transformation, when the need to expand the use of high gray area index transformation.</p>

48、;<p>　　3.2.2 smoothing filter - Noise Reduction</p><p>　　As the noise in the area corresponding to the edge of the image gray value of such rapid change with a larger part is a high frequency, so the

49、use of low-pass filter (ie, smoothing filter) noise. At the same time can make the image fuzzy smoothing is beneficial to the larger goal of the extraction prior to removal of the smaller details or to target the small i

50、nterruption link.</p><p>　　Smoothing noise reduction method is to use the template on the image convolution operation, linear smoothing filter is the most commonly used template is shown in Figure 2 of the 3

51、 × 3 template, this template and image in pixels by the following method of convolution , get smooth image noise reduction.</p><p>　　In the figure, roaming the template and the template center and map l

52、ocation of each pixel overlap;</p><p>　　the template on the coefficient multiplied with the template under the corresponding pixel;</p><p>　　add all the product;</p><p>　　It will as

53、sign the figure corresponds to the template and the center of the pixel.</p><p>　　The most commonly used non-linear smoothing filter is median filter, it will all of the values of the region are sorted accor

54、ding to size, will be sorted in the middle of the pixel values given to the center pixel. Median filter can effectively remove the random noise, can get a better visual effect.</p><p>　　Edge detection coding

55、</p><p>　　Edge is the result of discrete gray value can be used to request the first and second derivative method to detect. Because the derivative of the edge of a large area, rather than the local derivati

56、ve of the edge of the small. As the digital image is discrete, not the derivative, convolution method can replace the differential with the differential approximation.</p><p>　　Is better Sobel edge detection

57、 algorithm is operator. Sobel operator is a gradient amplitude, respectively, using vertical operator Sx, Sy operator to obtain the level of the coding region of the vertical edges and horizontal edges, that is, the hori

58、zontal and vertical directions as shown in Figure 3 using two different volumes product template, get the edge as shown in Figure 4 results.</p><p>　　Figure 2 smoothing filter template Figure 3 Sobel edge de

59、tection template</p><p>　　Figure 4Sobeledgedetection operator</p><p>　　Image Rectification</p><p>　　Hough transform can detect the coding region of the image angle, the angle of rot

60、ation according to the coding region of the image transformation can be corrected.</p><p>　　Hough transform to the image space XY of the line (y = px + q) parameter space detection problem is transformed int

61、o the mid-point of detection PQ, PQ in the parameter space, the establishment of a cumulative array Sum (p, q), for each given the edge in image space, let p taken over all possible values, according to linear equation q

62、 =- xp + y to calculate the corresponding q, on the Sum (p, q) to accumulate, by Sum (p, q) the value of the is the (p, q) point total of the number line, (p, q) is </p><p>　　Character Cutting</p><

63、;p>　　Coded character area on the level of scanning directly from the character spacing can generally be out of character segmentation. Can also be done by coded character area vertical projection operation, according

64、to the character width and character less the total number of characters to be cut. Figure 5 is a coded character areas and the corresponding vertical projection.</p><p>　　Figure 5 encoded characters and the

65、 corresponding Figure 6 Schematic diagram of </p><p>　　vertical projection linear interpolation</p><p>　　Character normalization</p><p>　　The char

66、acter of the segmented into four scans to determine the character boundaries, and then use linear interpolation for each character for normalized so that each character is normalized to 32 × 16 lattice. Figure 6 Sch

67、ematic diagram of linear interpolation, according to linear theory, f (x1) by the formula (2) Calculation:</p><p><b>　　（2）</b></p><p>　　Identification algorithm</p><p>　

68、　To determine the general characteristics of character recognition or template matching method, Feature identification is based on the degree of feature extraction stages, complete with a structural analysis approach to

69、character recognition. Template matching that is based on knowledge of the characters take shape matching method according to the template matching is generally divided into two categories: direct use of the imported two

70、-dimensional plane images and dictionary matching graphics me</p><p>　　Tire coding image only some of the characters and English characters and 10 Arabic numerals, characters less, the structure is relativel

71、y simple, so when the specific identification, either graphical matching method, you can also use structural analysis. However, the tires have a certain deformation of character encoding, and there is breakage, so a dire

72、ct template matching and feature extraction methods to identify directly rate is unsatisfactory, the system uses a template matching and feature</p><p>　　Feature-based weighted template matching recognition

73、algorithm basic idea is: to the template in character stroke of points assigned different weights, in the stroke center point of the highest weight, in the stroke edge point of the weight minimum, then the sample templat

74、es and Standard Template point by point fuzzy matching, recognition by fuzzy recognition rules.</p><p>　　Conclusion</p><p>　　In this paper, coded tire identification system character encoding to

75、 achieve on-line image recognition system design was described, a template matching and feature matching recognition algorithm combines the method of the traditional template matching algorithm is improved, improved defo