跳到主要內容

臺灣博碩士論文加值系統

(54.91.62.236) 您好!臺灣時間:2022/01/18 00:58
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:張綜麟
研究生(外文):Chung-Lin Chang
論文名稱:積層陶瓷電容影像自動辨識之研究
論文名稱(外文):A Study on Automatic Recognition of MLCC Images
指導教授:陳進興陳進興引用關係
指導教授(外文):Chin-Hsing Chen
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:64
中文關鍵詞:傅立葉分析積層陶瓷電容影像物件切割
外文關鍵詞:object segmentationfourier analysisMLCC images
相關次數:
  • 被引用被引用:11
  • 點閱點閱:2537
  • 評分評分:
  • 下載下載:204
  • 收藏至我的研究室書目清單書目收藏:0
  傳統電容辨識方法是依靠人工來檢查一張影像中有幾層電容層。但利用人眼來檢測缺乏效率且不可靠,因此自動化辨識在現代的工業檢測中已是一種趨勢且被廣泛的利用。本篇論文實現一個積層陶瓷電容影像的辨識系統。電容影像分析的困難主要包含沒有參考樣版、電容層附近雜訊的影響等。

  在論文中,我們結合基本影像處理和影像轉換技術來找出影像中電容層的層數。我們的檢測方法包含三部分:(1)傾斜偵測以及影像校正,(2)區塊分割及(3)電容層數計算。檢測過程描述如下:
  在第一部份利用中心點計算、方向偵測所得到的資訊隨後結合區塊搜尋法來找出電容層傾斜角度,然後將輸入影像經由旋轉校正。在第二部份中,先從校正後影像得到投影量,然後用小波轉換和分類演算法來切割電容層邊界範圍。第三部份我們再利用快速傅立葉轉換和間距偵測來找出電容層的間距,最後結果則是利用間距和電容層範圍來進行電容層數計算。

  實驗裡,我們對8種產品共256個影像進行辯識,若辨識結果和真正的層數之間誤差的絕對值小於0.5就可視為正確,實驗結果顯示我們的方法可以讓256個影像計算出來的誤差均小於0.5。平均檢測一張電容影像所需的時間為443.4 ms,平均影像大小為300×300。
  The traditional capacitor recognition is manual, unreliable, and inefficiency. Automatic recognition would therefore have great benefits and have been widely adopted for various automatic visual inspections in today’s industry. This thesis implements a recognition system to calculate how many layers in a MLCC image. The main difficulties of the problem includes: without template image, noise near each layer…, etc.

  The proposed algorithms combined basic image processing and image transform techniques in our recognition system to find the number of layers. Our algorithm consists of three parts: (1) skew detection and alignment, (2) object segmentation and (3) layer number calculation. In the first part, by using the information from centroid and direction, block matching algorithm is performed to find the skew angle and then the image is aligned via a rotation matrix. In the second part, projection is performed first. After that we use wavelet frames and fuzzy c-means to locate the layer boundary in the image. At last part, fast Fourier transform and pitch detection is performed to find pitch between layers. The number of layers is computed by combing the pitch and the layer boundary.

  In the experiments, we used eight products with total 256 images as test samples. The error is defined as the difference between the actual layer number and the number computed from the computer system. According to our experimental results, the errors are less than 0.5 for all 256 images. As for the processing time, 443.4 ms in average is required by one image and the average of the image size is 300×300.
Chapter 1 Introduction  1
  1.1 Motivation  1
  1.2 System Configuration   1
  1.3 Types of MLCC Images   4
  1.4 Organization of the Thesis   6

Chapter 2 Skew Detection and Alignment   7
  2.1 Introduction   7
  2.2 Image Thresholding and Centroid Calculation   9
    2.2.1 Thresholding Using the Otsu Method   9
    2.2.2 Centroid Calculation   11
  2.3 Edge Detection and Direction Calculation  12
    2.3.1 Edge Detection   12
    2.3.2 Direction Calculation  14
  2.4 Skew Detection and Alignment   15
    2.4.1 Image Smoothing Using Median Filters   15
    2.4.2 Skew Detection   17
      2.4.2.1 Block Matching Criterion   18
      2.4.2.2 Block Matching Algorithm   19
    2.4.3 Image Alignment   21

Chapter 3 Object Segmentation   26
  3.1 Introduction   26
  3.2 Projection   28
  3.3 Feature Extraction   31
    3.3.1 The Pyramid-Structured Wavelet Transform   31
    3.3.2 The Wavelet Frames   36
    3.3.3 Feature Extraction Using Wavelet Frames  40
      3.3.3.1 Signal Decomposition   40
      3.3.3.2 Local Energy Measure   41
  3.4 Clustering   42
    3.4.1 Clustering Using Fuzzy C-Means   42
    3.4.2 Region Separation Using Thresholding   45

Chapter 4 Calculation of Layer Numbers   47
  4.1 Introduction   47
  4.2 Fourier Analysis   48
    4.2.1 Fourier Transform and Fast Fourier Transform   48
    4.2.2 Spectrum Analysis Using FFT   51
  4.3 Pitch Detection   54
  4.4 Computation of Layer Numbers   55

Chapter 5 Experiments and Discussions   57
  5.1 Experimental Results   57
  5.2 Processing Time   57
  5.3 Discussion   60
  5.4 Conclusion   61
  5.5 Future Directions   61

Appendix The details of the experimental result for each product   62
[1]N. Otsu, “A threshold selection method from gray-level histogram,” IEEE Transactions on Systems, Man, and Cybernetics, SMC-9, pp. 62-66, 1979.

[2]R. C. Gonzale and R. E. Woods, Digital Image Processing, United States of America, 1993.

[3]J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-697, November 1986.

[4]V. Fotopoulos and A. N. Skodras, “Smae: an improved block matching criterion,” IEEE International Conference on Electronics, Circuits and Systems, vol. 3, pp. 519-522, 1998.

[5]J.-N. Kim and T.-S. Choi, “A fast three-step search algorithm with minimum checking point using unimodal error surface assumption,” IEEE Transactions on Computer Electronics, vol. 44, no. 3, pp. 638-648, August 1998.

[6]J. Lu and M. L. Lious, “A simple and efficient search algorithm for block-matching motion estimation,” IEEE Transactions Circuits System Video Technology, vol. 7, pp. 429-433, April 1997.

[7]R. C. Gonzales and R. Safabakhsh, “Computer vision techniques for industrial inspection and robot control: a tutorial overview,” IEEE Computer, pp. 400-420, May 1986.

[8]A. W. Paeth, “A fast algorithm for general raster rotation,” in Proc. Graphics Interface Vision Interface, pp. 77-81, May 1986.

[9]M. Unser, P. Thévenaz and L. Yaroslavsky, “Convolution-based interpolation for fast, high-quality rotation of images,” IEEE Transactions on Image Processing, vol. 4, no. 10, pp. 1549-1560, October 1995.

[10]S. Chien and Y. M. Baek, “Hierarchical block matching method for fast rotation of binary images,” IEEE Transactions on Image Processing, vol. 10, no. 3, pp. 483-489, March 2001.

[11]M. Unser, “Texture classification and segmentation using wavelet frames,” IEEE Trans. Image Process, vol. 4, no. 11, pp. 1549-1560 November 1995.

[12]T. Cheang and C.-C. J. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Process, vol. 2, no. 4, pp. 429-441, October 1993.

[13]Martin Vetterli, Jelena Kovačević, Wavelets and Subband Coding, Prentice-Hall, NJ, 1995.

[14]S. Mallat, “A theory of multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Patt. Anal. Machine Intell., vol. 11, no. 7, pp. 674-693, 1989.

[15]S. Mallat and S. Zhong, “Characterization of signals from multiscale edges,” IEEE Trans. Patt. Anal. Machine Intell., vol. 14, no. 7, pp. 710-732, July 1992.

[16]A. Laine and J. Fan, “Frame representations for texture segmentation,” IEEE Trans. Image Process., vol. 5, no. 5, pp. 771-780, May 1996.

[17]Q. Pan, L. Zhang, G. Dai and H. Zhang, “Two denoising method by wavelet transform,” IEEE Trans. Image Process., vol. 47, no. 12, pp. 3401-3406, December 1999.

[18]I. Daubechies, Ten Lectures on Wavelet. Philadelphia: Soc. Ind. Applied Math., 1992

[19]K. L. Laws, “Rapid texture identification,” Proc. SPIE, vol. 238, pp. 376-380, 1980.

[20]J. S. Jang, C. T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice-Hall International, NJ, 1997.

[21]J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithm, Plenum Press, New York, 1981.

[22]吳盈璋, “用快速定位演算法做晶片印碼品質檢測,” 碩士論文, 國立成功大學電機工程研究所, 1999.

[23]陳鵬仁, “基因式特徵粹取應用於使用小波理論之紋路影像切割,” 碩士論文, 國立成功大學電機工程研究所, 1998.

[24]Milan Sonka, Vaclav Hlavac, Roger Boyle, Image Processing, Analysis, and Machine Vision, United States of America, 1998.

[25]Ioannis Pitas, Digital Image Processing Algorithms, Prentice-Hall International, NJ, 1993.

[26]Alan V. Oppenheim, Ronald W. Schafer, Discrete-Time Signal Processing, Prentice-Hall International, NJ, 1999.

[27]G. T. Uber, “Illumination methods for machine vision,” Proceedings of
SPIE, 1005, pp. 93-102, 1986.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
1. 林寶貴、李真賢(民71)。聽覺障礙學生國語文能力之研究。教育學院學報第十二期, 1-27頁。
2. 林寶貴、張小芬(民77)。聽覺障礙學生瑞文民非文學推理測驗常模之建立及其相關之研究。特殊教育學報。3期,29-67頁。
3. 林寶貴、李真賢(民76)。聽覺障礙學生國語文能力之研究。教育學院學報,12期,1-27頁。
4. 林寶貴(民71)。國小聽覺障礙兒童語文學習之調查研究。師大學報,第27期。國立臺灣師範大學,79-101頁。
5. 林淑玟(民78)。身體殘體對個人心理與社會適應的影響。特教園丁,5卷2期,15-19頁。
6. 林家宇(民85)。聽障兒童數字加法困擾問題之探討。特教園丁。11卷4期。
7. 林宏熾(民88)。身心障礙者自我決策與自我擁護:障礙者社會福利與特殊教育新議題與趨勢。社會福利,140期,25-41頁。
8. 林宏熾(民85)。殘障者的生涯教育職業訓練與就業輔導。特殊教育季刊,61期,1-12頁。
9. 邱上真(民80)。學習策略教學的理論與實際。特殊教育與復健學報, 1-49頁。
10. 吳武典(民87)。教育改革與特殊教育。教育資料集刊,23輯,197-220頁。台北:國立編譯館。
11. 丁秀雄(民81)。聽覺障礙兒童人格特質之探討。特教園丁,7卷3期,26-27頁。
12. 康自立(民68),簡介心理動作領域之教育目標。中學工藝教育月刊,12卷3期,頁2-5。
13. 張蓓莉(民70)。台北市聽覺障礙兒童之智力結構。特殊教育季刊,2期,24-31頁。
14. 張蓓莉(民74)。聽覺障礙學生的情緒問題。特殊教育季刊。15期。6-9頁。
15. 張蓓莉(民74)。聽覺障礙教育回歸主流的探討。特殊教育季刊。17期,12-15頁。