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Author: 申希明
Hsi-ming Shen
Thesis Title: 有效率的取樣策略和精煉策略用於隨機式測圓
Efficient Sampling Strategy and Refinement Strategy forRandomized Circle Detection
Advisor: 鍾國亮
Kuo-Liang Chung
Committee: 賴榮滄
Zone-Chang Lai
陳世旺
Sei-Wang Chen
曾定章
Din-Chang Tseng
楊傳凱
Chuan-Kai Yang
Degree: 碩士
Master
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2010
Graduation Academic Year: 98
Language: 英文
Pages: 33
Keywords (in Chinese): 取樣策略強健性精煉隨機式演算法查表法哈克轉換圓形偵測
Keywords (in other languages): Sampling Strategy, Refinement, Randomized algorithms, Robustness, Lookup table, Hough transform, Circle detection
Reference times: Clicks: 267Downloads: 1
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在影像處理和圖形識別領域,圓形偵測是很重要且基本的方法。本論文分為兩部份,第一部份提出了以法線為基礎的抽樣策略來決定候選圓,較傳統的隨機式測圓有更高的機率使得候選圓成為真圓,大大的降低了計算時間。為了提昇準確率,第二部份提出以重新投票為基礎的精煉策略。實驗結果顯示和先前有關的隨機式測圓相比,以我們提出的以法線為基礎的抽樣策略和以重新投票為基礎的精煉策略來測圓,大大的降低了計算時間且有更好的準確率。


Circle-detection is an important and fundamental operation in image
processing and pattern recognition. This thesis first presents a new
gradient line-based sampling strategy to determine a candidate
circle and the determined candidate circle has higher probability to
be promoted to a true circle when compared with the traditional
randomized strategy; it leads to significantly computational effect.
Further, for enhancing the detection accuracy, a new revoting-based
refinement strategy is presented. Experimental results demonstrated
that our proposed gradient line-based sampling strategy and
revoting-based refinement strategy can significantly improve
computing time performance and the detection accuracy for circle
detection when compared with previous randomized related algorithms.

1 Introduction 1 2 Problems in Past Sampling Strategy and Refinement Scheme 4 2.1 Sampling strategy in the RCD leads to bias and computational overhead problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Lee et al.’s RCD–based refinement strategy leads to the time consuming problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3 The Proposed Gradient Line-Based Sampling Strategy 10 4 The Proposed New Refinement Scheme 16 5 Experimental results 21 6 Conclusion 30

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