非共平面文件影像透視矯正

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：85

、訪客IP：18.191.132.211

姓名

吳宗憲(Tsung-Hsien Wu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

非共平面文件影像透視矯正
(The perspective rectification for non-planar documents)

相關論文

★ 使用視位與語音生物特徵作即時線上身分辨識	★ 以影像為基礎之SMD包裝料帶對位系統
★ 手持式行動裝置內容偽變造偵測暨刪除內容資料復原的研究	★ 基於SIFT演算法進行車牌認證
★ 基於動態線性決策函數之區域圖樣特徵於人臉辨識應用	★ 基於GPU的SAR資料庫模擬器：SAR回波訊號與影像資料庫平行化架構 (PASSED)
★ 利用掌紋作個人身份之確認	★ 利用色彩統計與鏡頭運鏡方式作視訊索引
★ 利用欄位群聚特徵和四個方向相鄰樹作表格文件分類	★ 筆劃特徵用於離線中文字的辨認
★ 利用可調式區塊比對並結合多圖像資訊之影像運動向量估測	★ 彩色影像分析及其應用於色彩量化影像搜尋及人臉偵測
★ 中英文名片商標的擷取及辨識	★ 利用虛筆資訊特徵作中文簽名確認
★ 基於三角幾何學及顏色特徵作人臉偵測、人臉角度分類與人臉辨識	★ 一個以膚色為基礎之互補人臉偵測策略

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

近年來數位相機普及，使得人們隨時可以藉相機取得影像；過去文字影像只能以紙本形式透過掃描機取得，現在則可利用相機隨時隨地在幾秒內得到各種實物上的文字影像。然而當透過相機取得越接近生活化的影像，因為取像環境的變異多元，形成了如影像光場不均問題、影像非平面化問題等新的研究課題，尤其是在光學文字辨識的應用上，這些新的問題產生了許多不良的影響。
目前文字辨識技術已能對單一文字非常正確的進行辨識，反而是如何取得單一文字的切割問題，因為以相機取像後所造成的視角差異、及文字影像不再是單純的分佈在同一平面等問題，成為有待努力的課題。本研究的研究目標即專注於文件因取像視角及文字分佈在不同平面等所造成的文件透視失真的文件變形矯正，針對日常生活中最常取得的圓柱上文件、立方體上非共平面文件、及非垂直取像光軸的透視失真文件進行分類與矯正研究。
本研究提出一個有效分割不同表面的文件影像矯正方法，利用影像前處理、連通標記擷取影像資料、文字行擷取流水演算法、及文件表面類別判斷，設計出有效的文件矯正方法。本研究所提出的矯正方法，不需使用文件邊界或排版格式的資訊，即可對常見文件影像因所在表面非單一平面的失真現象有不錯的矯正效果。

摘要(英)

Recently, digital cameras become a universal device due to its cost down. People can capture images at will in any time. In the past, text images can only be acquired by scanning documents using scanners. Currently, we can obtain the images of any kinds of objects by simply using cameras within second. Resulting from the influences of environments existing in our daily life while capturing the object images, some new research topics arise, such as the space with uneven light-illumination and scene with more than one plane. Those new problems will definitely affect the performance of OCR (optical character recognition) drastically.
Instead of focusing on OCR study which can already correctly recognize a single character with very high recognition rate currently, we devoting ourselves on slicing and obtaining a character from the images captured under poor conditions. For instance, the difference in view angles and texts do not distribute on the same plane. In this thesis, the research focuses on rectifying the documents with perspective distortions caused by different view angles while capturing images and various planes that texts locate on the image. In our work, we specially focus on classifying and rectifying images resided on cylinders, cubes of non-coplanar, and those captured through non-vertical light axis lens.
This study provides an effective way in splitting an image with different planes and rectifying the split regions. The effective method in rectifying documents is designed mainly by using image processing techniques, such as connected-component labeling for extracting image information, text line extraction water flow algorithm, and image plane analysis. The proposed method can rectify those common document images with perspective distortion caused by non-singular planes without needing the information of document border and typesetting. Experimental results verify the feasibility and validity of our proposed method.

關鍵字(中)

★ 文件矯正
★ 流水演算法
★ 相關係數

關鍵字(英)

★ document rectification
★ water flow
★ correlation coefficients

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 v
表目錄 vii
第一章緒論 1
1.1 研究動機與目的 1
1.2 相關研究 3
1.3 系統流程 9
1.4 論文架構 10
第二章前處理 11
2.1 彩色影像轉換灰階 11
2.2 去除不均勻光場 12
2.3 影像二值化 16
2.4 前處理實驗 18
第三章文字行處理與判定 20
3.1 流水法文字行擷取 22
3.2 文字行抽取 27
3.3 文字行形狀判定 29
3.4 平面偵測 34
第四章文字矯正 37
4.1 文字行逼近 38
4.2 轉換參數的估計與矯正 46
第五章實驗結果與討論 50
5.1 文字行形狀判定結果 53
5.2 平面矯正結果 59
5.3 非共平面矯正結果 63
5.4 曲面矯正結果 71
5.5 實驗影像資料分析與討論 76
第六章結論與未來工作 80
6.1 結論 80
6.2 未來工作 81
參考文獻 82

參考文獻

[1] G. Agam and C. Wu, “Structural Rectification of Non-planar Document Images: Application to Graphics Recognition,” Lecture Notes in Computer Science, pp. 2390, 2002.
[2] M. S. Brown , M. Sun , R. Yang , L. Yun and W. B. Seales, “Restoring 2D Content from Distorted Documents,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, n0.11, pp.1904-1916, 2007
[3] R. Hartley and A. Zisserman, “Multiple View Geometry in Computer Vision,” Cambridge university press, 2ed, 2003.
[4] X. Chen, J. Yang, J. Zhang and A. Waibel, “Automatic detection and recognition of signs from natural scenes,” IEEE Trans. Image Process., vol. 13, no. 1, pp.87 - 99, 2004.
[5] N. Stamatopoulos, B. Gatos, I. Pratikakis and S. J. Perantonis, “Goal-Oriented Rectification of Camera-Based Document Images,” IEEE Trans. Image Process., vol. 20, no. 4, pp.910 - 920, 2011.
[6] S. J. Lu, B. M. Chen and C. C. Ko, “A partition approach for the restoration of camera images of planar and curled document,” Image and Vision Computing, vol. 24, no. 8, pp. 837-848, 2006.
[7] L. Likforman-Sulem, A. Zahour and B. Taconet, “Text line segmentation of historical documents: a survey,” International Journal on Document Analysis and Recognition, vol. 9, no. 2, pp. 123–138, 2007.
[8] Y. H. Tseng, H. J. Lee, “Recognition-based handwritten Chinese character segmentation using a probabilistic Viterbi algorithm,” Pattern Recognit. Lett., vol. 20, no 8, pp. 791–806, 1999.
[9] M. W. Friedrich, Y. W. Kwan and G. C. Richard, “Block segmentation and text extraction in mixed text/image documents,” Computer Graphics and Image Processing, vol. 20, no 4, pp.375-390, 1982.
[10] A. Zramdini and R. Ingold, “Optical Font Recognition Using Typographical Features,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, 1998.
[11] S.-C. Pei and M. Tzeng “Uneven Illumination Removal and Image Enhancement Using Empirical Mode Decomposition, ” 20th Conf. in Computer Vision, Graphics and Image Processing(CVGIP), 2007.
[12] 陳建隆, “應用改良式經驗模態分解法於消除文件影像中的不良光照現象,” 國立中央大學資訊工程研究所碩士論文, 中華民國98年.
[13] N. Otsu, “A Threshold Selection Method from gray-level Histograms. ” IEEE Trans. Syst. Man Cybern., vol. 9, pp. 62–66, 1979.
[14] L. He, Y. Chao, K. Suzuki and K. Wu, “Fast connected-component labeling,” Pattern Recognition, vol. 42, no. 9, pp.1977-1987, 2009.
[15] S. Basu, C. Chaudhuri, M. Kundu, M. Nasipuri and D.K. Basu, “Text line extraction from multi-skewed handwritten documents,” Pattern Recognition, vol. 40, no. 6, pp.1825-1839, 2007.
[16] 廖紹鋼(編譯), G. Woods(原著), “數位影像處理,” 普林斯頓國際有限公司,第二版, 2003.
[17] 吳榮彬(編譯), J. S. Milton(原著), “工程統計學：原則與應用,” 麥格羅‧希爾,第四版, 2003.
[18] Wikipedia; Pearson product-moment correlation coefficient. http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient. Accessed 20 June 2011.
[19] http://perso.ens-lyon.fr/patrick.flandrin/emd.html. Accessed 20 July 2011.

指導教授

范國清(Kuo-Chin Fan)

審核日期

2011-8-16

推文