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研究生:董思廷
研究生(外文):Dong, Siting
論文名稱:機器視覺問卷輸入影像系統開發
論文名稱(外文):Development of the Automatic Questionnaire Input System by Machine Vision
指導教授:蒲永仁蒲永仁引用關係
指導教授(外文):Pu, Yongren
口試委員:蒲永仁郭昭霖李素幸
口試委員(外文):Pu, YongrenKuo, ChaolinLee, Suhsing
口試日期:2012-06-28
學位類別:碩士
校院名稱:長榮大學
系所名稱:職業安全與衛生學系碩士班
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:58
中文關鍵詞:問卷辨識機器視覺影像分析安全衛生
外文關鍵詞:QuestionnaireMachine VisionImage ProcessSafety and Health
相關次數:
  • 被引用被引用:2
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
對許多研究安全衛生的學者來說,透過問卷調查來做定量訊息的收集是相當重要的。研究者通常都希望問卷分析的資料能有較高的準確性和代表性,因此在大型的問卷調查裡數據的收集與管理會相當龐大,也使得問卷輸入成為分析過程裡的重要關卡。問卷輸入可採用人工輸入或利用讀卡機讀取後傳輸至電腦來進行分析,但人工作業需考量問卷版面設計及人力成本,而讀卡機輸入則需使用專用卡紙,事後必須承租讀卡機來進行讀卡,成本相對地也會增加。因此本研究開發一套利用機器視覺進行問卷自動輸入的系統,使得問卷版面設計及紙張材質都無須受限,研究者能自行大量地自動化輸入問卷。
本論文問卷辨識系統開發多項軟硬體模組,當系統運作時,問卷經由送紙裝置送出,透過攝像鏡頭擷取回收問卷的影像,利用圖控式程式語言,手動初定位填答空格,隨後進行即時影像分析處理,並將分析後數據儲存成檔案。本論文進行若干實驗以評估此系統之準確性及效能。在性能實驗中針對填答用筆的顏色及劃記形狀進行準確率測試,經統計分析顯示黑、紅、藍、綠、紫及鉛筆等多種大眾常用顏色之辨識率並無顯著差異;而打勾、劃斜線、劃叉之辨識率顯著高於劃圈。整體而言,各顏色之辨識率均高達98.77%~99.17%,而各形狀之辨識率亦達95.32%~99.21%。此外分別對空白及已填答問卷進行辨識速度測試,其辨識平均時間分別為3119.78ms和3154.85ms,即在3.2秒內軟體可以完成辨識一張填畢問卷。因此本研究的自動化問卷輸入系統確可提高問卷調查辨識的準確性及效率。

For many scholars in the areas of safety and health, it is very important to collect the quantitative information through questionnaire surveys. In a large-scale survey, hoping that the data of the questionnaire analysis can be more accurate and representative, researchers collect and manage a huge amount of questionnaires, which makes the data input become one of the most important steps in the analytical process. Data input of questionnaires can be done manually or through the card reading devices for further analysis in computers. For manual input people need to consider the questionnaire layouts and the labor cost, while using the card reading devices requires much higher cost since those devices are rental and the questionnaires are made of some specified materials. This research are, therefore, to develop by machine vision an automatic input system, which let researchers be able to conduct a large amount of questionnaire input without the limitations of the layout designs and paper materials.
The questionnaire input system developed in this thesis integrates a number of hardware and software modules. When the system is in operation, each questionnaire sent by a paper feeder is captured by a camera in real time, and analyzed in the graphical user interface. The data acquired after image processing are then saved as a text file. Several experiments were conducted to assess the accuracy and the efficiency of the system. In the performance experiment, we processed a plenty of questionnaires answered using various pens with the specified colors, as well as some specified marks. It was found by statistical analyses that there was no significant difference in the accuracies of all colors; and accuracies answered with checks, ticks and crosses were significantly better than those answered with circles. Of all the accuracies of the software to recognize the checkboxes filled with checks in various colors ranged from 98.77%~99.17%; and to recognize other checkboxes answered with different marks were around 95.32%~99.21%. In the last experiment, we identified that the average processing time for a blank questionnaire was 3119.78ms, and for an answered one was 3154.85ms, which means that the developed software is capable of processing a questionnaire during 3.2 seconds. It is concluded that this automatic questionnaire input system is indeed able to increase the accuracy and efficiency in questionnaire surveys.

摘要 I
Abstract II
目錄 IV
表目錄 VII
圖目錄 VIII
第一章 緒論 1
1.1 研究背景 1
1.2研究目的 2
1.3文獻探討 2
第二章 影像處理 5
2.1影像分析流程 5
2.2影像校正方法 6
2.2.1透視投影模型 6
2.2.2定位標搜尋 9
2.2.3針孔鏡頭校正 10
2.2.4座標轉換 11
2.3選取ROI 12
2.4二值化 12
2.5框線訂定和框內區域萃取 14
2.6型態處理 16
2.7攝距長度訂定 17
第三章 系統架構與設備 19
3.1系統架構 19
3.2軟體開發 20
3.3硬體系統設備 21
3.3.1 系統單晶片 22
3.3.2送紙裝置架構 23
3.3.3 送紙裝置控制電路板 25
3.3.4步進馬達控制脈波信號擷取 27
3.4辨識流程 28
3.5系統流程 29
第四章 實驗架構 32
4.1顏色對辨識率的測試 32
4.1.1實驗方法 32
4.1.2實驗流程 32
4.2各種形狀的填答符號對辨識率的測試 33
4.2.1實驗方法 33
4.2.2實驗流程 34
4.3問卷辨識速度的測試 34
4.3.1實驗方法 34
4.3.2實驗流程 35
第五章 結果與討論 36
5.1實驗結果 36
5.1.1 顏色對辨識率實驗結果 36
5.1.2各種形狀的填答符號對辨識率實驗結果 37
5.1.3 問卷辨識速度的測試實驗結果 39
5.2辨識錯誤之情形 39
5.3硬體失誤之情形 41
第六章 結論與未來方向 42
6.1結論 42
6.2未來方向 42
參考文獻 44
附錄 47
系統單晶片嵌入程式 47

[1]A. N. Oppenheim, Questionnaire Design, Interviewing and Attitude Measurement: Continuum, 1992.
[2]David Poor, D. S. Image capture and storage techniques in association with optical mark reading. United States Patent 1995; 5,452,379.
[3]Rafael-C Gonzalez, Richard E Woods. Digital image processing. 2nd ed. New Jersey: Prentice Hall; 2002.
[4]Herbert Schantz, The History of OCR. Manchester Center, VT: Recognition Technologies Users Association, 1982
[5]Wei-Ran Xu, Hong-Gang Zhang, Jun Guo and Guang Chen, “Discrimination between Printed and Handwritten Characters for Cheque OCR System,” Proceedings of International Conference on Machine Learning and Cybernetics, vol. 2, pp. 1048-1053, Nov. 4-5, 2002.
[6]Hanchuan Peng, Fuhui Long and Zheru Chi, “Document Image Recognition Based on Template Matching of Component Block Projections,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, pp. 1188-1192, Sep. 2003
[7]Weihua Huang, Chew Lim Tan, Sam Yuan Sung and Yi Xu, “Vertical Bar Detection for Gauging Text Similarity of Document Images,” Proceedings of Sixth International Conference on Document Analysis and Recognition, pp. 640-644, Sep. 10-13, 2001.
[8]Weihua Huang, Chew Lim Tan, Sam Yuan Sung and Yi Xu, “Word Shape Recognition for Image-Based Document Retrieval,” Image Processings of International Conference, vol. 1, pp. 1114-1117 , Oct. 7-10, 2001
[9]William Patrick Tunney, “Method and System for Identifying Multiple Questionnaire Pages”, United States Patent 7,031,520 B2, Apr. 18, 2006.
[10]Carlos A. Luna, Manuel Mazo, José Luis Lázaro, and Juan F. Vázquez, “Calibration of Line-Scan Cameras”, IEEE Transactions on Instrumentation And Measurement, vol. 59, n. 8, pp. 2185-2190, Aug. 2010.
[11]Juho Kannala, and Sami S. Brandt, “A Generic Camera Model and Calibration Method for Conventional, Wide-Angle, and Fish-Eye Lenses”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, n. 8, Aug 2006.
[12]David Forsyth, and Jean Ponce, Computer Vision: A Modern Approach, 2003.
[13]Yong-Ren Pu, Su-Hsing Lee, and Chao-Lin, Kuo. “development of a questionnaire input software by machine vision, ” 2nd International Symposium on Knowledge Acquisition and Modeling, 2003.
[14]National Instruments, IMAQ Vision Concepts Manual, 2003.
[15]In-Jung Kim, “Multi-Window Binarization of Camera Image for Document Recognition,” Ninth International Workshop on Frontier in Handwriting Recognition, pp. 323-327, Oct. 26-29, 2004.
[16]H. Al-Yousefi and S. S. Udpa, “Recognition of Arabic Characters,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, pp. 853-857, Aug. 1992.
[17]張仁豪,“正交軸投影法與樹狀決策在汽車牌照辨識的研究”,國立清華大學原子科學系碩士論文,中華民國九十年六月,2001。
[18]曾健維,“晶片印字瑕疵檢測之研究”,中原大學工業工程系研究所碩士論文,中華民國八十九年七月,2000。
[19]曾文憲,“光學標誌辨識技術為基礎之整合簡易式紙本資料自動收集輔助系統”,國立陽明大學衛生資訊與決策研究所碩士論文,中華民國九十四年七月,2005。
[20]張玉其,“機器視覺問卷輸入影像辨識研究”,長榮大學職業安全與衛生學系碩士論文,中華民國九十九年一月,2010。

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