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研究生:張家齊
研究生(外文):Chia-Chi Chang
論文名稱:分區加性原理之快速霍夫轉換
論文名稱(外文):A Fast Hough Transform Using Additive Region Segmentation
指導教授:吳崇賓
口試委員:賴永康陳春僥
口試日期:2016-07-27
學位類別:碩士
校院名稱:國立中興大學
系所名稱:電機工程學系所
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:2016
畢業學年度:104
語文別:中文
論文頁數:37
中文關鍵詞:霍夫轉換直線
外文關鍵詞:Hough Transformline
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為改善傳統霍夫轉換效率,本論文提出基於分區加性原理之快速霍夫轉換。首先,為降低因分散的雜點而產生誤判,將影像切為若干個區域。接著對每個區域內的cell計算其特徵點位置及權重,並依角度資訊於特定角度區間轉換,以近似原本霍夫轉換結果。而轉換的同時進行排序,每個區域皆限制其排序數量,並用峰值展開window的方式剔除相近的直線。最後將各區域霍夫轉換的結果對應回全域霍夫轉換結果,對應的同時將排序的資料對應回原影像直線即完成偵測。提出之演算法平均處理速率為5486(edge/ms)。相較傳統霍夫轉換,可節省95%以上運算時間。

In this thesis, based on Additive Region Segmentation a fast Hough Transform is proposed. To decrease the undesired detected line due to noise, the image is partitioned into several regions. Moreover, the feature point and the weighting of cell within each region are provided to transform into specific section by its angle information. The transform and sorting are performed simultaneously, and every region is limited with its sorting number, and the similar lines are removed with the method window extended by peak. Further, the result of region HT is mapping to Global HT to find the line. The experiment results show that the performance of the proposed algorithm is 5486 edges per millisecond. The computing time is saving about 95% comparing with Standard Hough Transform.

中文摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vii
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 1
1.3 論文架構 1
第二章 文獻探討 2
2.1 Hough Transform簡介 2
2.2 HT類型及應用 4
第三章 研究方法 7
3.1 演算法架構 7
3.2 梯度及角度分類 9
3.3 區域分割 13
3.4 特徵點判定 14
3.5 特定區間內霍夫轉換 18
3.6 區域至全域霍夫轉換對應 21
3.7 直線偵測 23
第四章 實驗結果與討論 25
4.1 複雜區域之影響 25
4.2 轉換區間之影響 26
4.3 Window大小之影響 27
4.4 排序數量應用 28
4.5 運算量分析 30
4.6 準確率分析 34
第五章 結論與未來工作 35
5.1 結論 35
5.2 未來工作 35
參考文獻 36



[1] Duda, R. O. and P. E. Hart, “Use of the Hough Transformation to Detect Lines and Curves in Pictures,” Comm. ACM, vol. 15, pp. 11–15, Jan 1972
[2] Hough, P.V.C. Method and means for recognizing complex patterns, U.S. Patent 3,069,654, Dec. 18, 1962
[3] Dana H. Ballard, “Generalizing the Hough transform to detect arbitrary shapes,” Pattern Recoqnition vol. 13, no. 2, pp. 111-122 ,1981
[4] Leandro A.F. Fernandes, Manuel M. Oliveira “Real-time line detection through an improved Hough transform voting scheme,” Pattern Recogn., 41 (1) 2008, pp. 299–314
[5] R. K. Satzoda, S. Suchitra, and T. Srikanthan “Parallelizing the Hough Transform Computation,” IEEE Signal Processing Letters, vol. 15, 2008
[6] Ravi Kumar Satzoda, Suchitra Sathyanarayana, Thambipillai Srikanthan “Hierarchical Additive Hough Transform for Lane Detection,” IEEE Embedded Systems Letters, vol. 2, no. 2, Jun 2010
[7] Shengzhi Du, Barend Jacobus van Wyk, Chunling Tu, and Xinghui Zhang “An Improved Hough Transform Neighborhood Mapfor Straight Line Segments,” IEEE Transactions On Image Processing, vol. 19, no. 3, Mar 2010
[8] Zezhong Xu, Bok-Suk Shin, and Reinhard Klette “ Accurate and Robust Line Segment Extraction Using Minimum Entropy With Hough Transform,” IEEE Transactions On Image Processing, vol. 24, no. 3, Mar 2015
[9] Shengzhi Du, Chunling Tu, Barend Jacobus van Wyk, and Zengqiang Chen “Collinear Segment Detection Using HT Neighborhoods,” IEEE Transactions On Image Processing, vol. 20, no. 12, Dec 2011
[10] Hart, P. E., “How the Hough Transform was Invented,” IEEE Signal Processing Magazine, vol.26, Issue 6, pp 18 – 22 Nov 2009
[11] [Online]. Available: http://www.cpubenchmark.net/cpu_list.php


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