請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/43290完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅楸善 | |
| dc.contributor.author | Tz-Sheng Peng 彭志昇 | en |
| dc.contributor.author | 彭志昇 | zh_TW |
| dc.date.accessioned | 2021-06-15T01:47:30Z | - |
| dc.date.available | 2009-07-14 | |
| dc.date.copyright | 2009-07-14 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-07-07 | |
| dc.identifier.citation | [1] R. de Beer, “Histogram-Based Methods,” http://dutnsj2.tn.tudelft.nl:8080/main/node52.html, 2009.
[2] J. Garcia-Consuegra, G. Cisnero, and E. Navarro, “A Sequential ECHO Algorithm Based on the Integration of Clustering and Region Growing Techniques,” Proceedings of International Geoscience and Remote Sensing Symposium, Honolulu, Hawaii, Vol. 2, pp. 648-650, 2000. [3] J. George, “Circuit Construction Techniques,” http://wiredworld.tripod.com/tronics/pcb_techniques.html, 2001. [4] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd edition, Prentice Hall, Upper Saddle River, NJ, 2002. [5] R. M. Haralick and L. G. Sharpiro, Computer and Robot Vision, Vol. I, Addison Wesley, Reading, MA, 1992. [6] O. J. Morris, M. de J. Lee, and A. G. Constantinides, “Graph Theory for Image Analysis: an Approach Based on the Shortest Spanning Tree,” IEE Proceedings F: Communications, Radar and Signal Processing, Vol. 133, No. 2, pp. 146-152, 1986. [7] N. Ohta, T. Kanade, and T. Takai, “Color Information for Region Segmentation,” Computer Graphics and Image Processing, Vol. 13, No. 3, pp. 222-241, 1980. [8] N. Otsu, 'A Threshold Selection Method from Gray-Level Histograms,' IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62-66, 1979. [9] TRI Innovation, “TR7600,” http://www.tri.com.tw/en/products_a_txt.aspx?id=P_00000019&cid=C_00000002&pname=TR7600&cname=AOI%2fAXI, 2009. [10] Wikipedia, “Cluster Analysis,” http://en.wikipedia.org/wiki/Data_clustering, 2009. [11] Wikipedia, “Color Space,” http://en.wikipedia.org/wiki/Color_space, 2009. [12] Wikipedia, “Edge Detection,” http://en.wikipedia.org/wiki/Edge_detection, 2009. [13] Wikipedia, “Image Segmentation,” http://en.wikipedia.org/wiki/Image_segmentation, 2009. [14] Wikipedia, “K-means Clustering,” http://en.wikipedia.org/wiki/K_means, 2009. [15] Wikipedia, “Minimum Spanning Tree,” http://en.wikipedia.org/wiki/Minimum_spanning_tree, 2009. [16] Wikipedia, “Solder Mask,” http://en.wikipedia.org/wiki/Solder_mask, 2009. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/43290 | - |
| dc.description.abstract | 我們所提出來的方法可以簡單分成三個階段：訓練階段、臨界值階段與精練階段。
訓練階段的目的是計算錫膏色彩的平均值。我們利用一個自動找尋臨界值的演算法在臨界值階段中遞迴地去尋找臨界值。在臨界值階段後，我們想得到更加準確的結果，因為錫膏區域不會太大或太小，因此我們使用連接元件分析去消除那些有太多或太少元素數目的元件。我們並計算連接元件與訓練出來的數據的色彩距離，並將此作為是否保留元件的依據。 我們的方法在分割錫膏上是有效的，實驗數據讓我們知道這個方法是快速而且準確率高的。 | zh_TW |
| dc.description.abstract | Our method can be briefly divided into three phases: training phases, thresholding phase, and refining phase. We only have to train once. We do not have to train again until we have different color PCB (Printed Circuit Board) images. Thresholding and refining phases are used to segment every image.
The goal of training phase is to calculate the centroid of solder color. In thresholding phase, apply an automatic threshold finding algorithm to find the threshold recursively. After thresholding, we want to obtain better result. Consequently, we use connected component analysis to remove the component with too many or too few elements, because solder area is not too big or too small. Calculate the minimum color distance d from the centroid of solder color to connected component. Our method is effective for solder image segmentation. Experiment data show us this method has high-speed and high-precision. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T01:47:30Z (GMT). No. of bitstreams: 1 ntu-98-R96922118-1.pdf: 4159015 bytes, checksum: b3ad977cb665c79fa7aad1f6c98b66b0 (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | Chapter 1 Introduction 1
1.1 Motivation 1 1.2 AOI and AXI 2 1.3 Color Space 4 1.4 False Alarm and Misdetection 5 Chapter 2 Segmentation Methods [13] 6 2.1 Introduction 6 2.2 Clustering Methods 7 2.3 Histogram-Based Methods 8 2.4 Edge Detection Methods 10 2.5 Region Growing Methods 11 2.6 Graph Partition Methods 12 Chapter 3 Previous Work 14 Chapter 4 Our Method 16 4.1 Preview 16 4.2 Training 16 4.3 Thresholding 18 4.4 Refining 21 Chapter 5 Experiment Result 24 5.1 Experiment Environment 24 5.2 Experiment Result 25 Chapter 6 Conclusion and Future work 48 Reference 49 | |
| dc.language.iso | zh-TW | |
| dc.subject | 色彩分割 | zh_TW |
| dc.subject | color segmentation | en |
| dc.title | 以色彩為基礎的印刷電路板分割 | zh_TW |
| dc.title | Color-Based Printed Circuit Board Segmentation | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 方志恆,尤智仕,吳文明 | |
| dc.subject.keyword | 色彩分割, | zh_TW |
| dc.subject.keyword | color segmentation, | en |
| dc.relation.page | 50 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2009-07-08 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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