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研究生(外文):Chung-Lin Chang
論文名稱(外文):A Study on Automatic Recognition of MLCC Images
指導教授(外文):Chin-Hsing Chen
外文關鍵詞:object segmentationfourier analysisMLCC images
  • 被引用被引用:11
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  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 Block Matching Criterion   18 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 Signal Decomposition   40 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
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