| dc.contributor.advisor | 何瑁鎧 | zh_TW |
| dc.contributor.advisor | Hor, Maw Kae | en_US |
| dc.contributor.author (Authors) | 劉恭良 | zh_TW |
| dc.contributor.author (Authors) | Liu, Kung Liang | en_US |
| dc.creator (作者) | 劉恭良 | zh_TW |
| dc.creator (作者) | Liu, Kung Liang | en_US |
| dc.date (日期) | 2010 | en_US |
| dc.date.accessioned | 17-Apr-2012 09:16:52 (UTC+8) | - |
| dc.date.available | 17-Apr-2012 09:16:52 (UTC+8) | - |
| dc.date.issued (上傳時間) | 17-Apr-2012 09:16:52 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0097753020 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/52775 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊科學學系 | zh_TW |
| dc.description (描述) | 97753020 | zh_TW |
| dc.description (描述) | 99 | zh_TW |
| dc.description.abstract (摘要) | 基礎矩陣在影像處理是非常重要的參數，舉凡不同影像間對應點之計算、座標系統轉換、乃至重建物體三維模型等問題，都有賴於基礎矩陣之精確與否。本論文中，我們提出一個機制，透過粒子群最佳化的觀念來求取基礎矩陣，我們的方法不但能提高基礎矩陣的精確度，同時能降低計算成本。我們從多視角影像出發，以SIFT取得大量對應點資料後，從中選取8點進行粒子群最佳化。取樣時，我們透過分群與隨機挑選以避免選取共平面之點。然後利用最小平方中值表來估算初始評估值，並遵循粒子群最佳化演算法，以最小疊代次數為收斂準則，計算出最佳之基礎矩陣。實作中我們以不同的物體模型為標的，以粒子群最佳化與最小平方中值法兩者結果比較。實驗結果顯示，疊代次數相同的實驗，粒子群最佳化演算法估測基礎矩陣所需的時間，約為最小平方中值法來估測所需時間的八分之一，同時粒子群最佳化演算法估測出來的基礎矩陣之平均誤差值也優於最小平方中值法所估測出來的結果。 | zh_TW |
| dc.description.abstract (摘要) | Fundamental matrix is a very important parameter in image processing. In corresponding point determination, coordinate system conversion, as well as three-dimensional model reconstruction, etc., fundamental matrix always plays an important role. Hence, obtaining an accurate fundamental matrix becomes one of the most important issues in image processing.In this paper, we present a mechanism that uses the concept of Particle Swarm Optimization (PSO) to find fundamental matrix. Our approach not only can improve the accuracy of the fundamental matrix but also can reduce computation costs.After using Scale-Invariant Feature Transform (SIFT) to get a large number of corresponding points from the multi-view images, we choose a set of eight corresponding points, based on the image resolutions, grouping principles, together with random sampling, as our initial starting points for PSO. Least Median of Squares (LMedS) is used in estimating the initial fitness value as well as the minimal number of iterations in PSO. The fundamental matrix can then be computed using the PSO algorithm.We use different objects to illustrate our mechanism and compare the results obtained by using PSO and using LMedS. The experimental results show that, if we use the same number of iterations in the experiments, the fundamental matrix computed by the PSO method have better estimated average error than that computed by the LMedS method. Also, the PSO method takes about one-eighth of the time required for the LMedS method in these computations. | en_US |
| dc.description.tableofcontents | 第一章 緒論 11.1前言 11.2研究背景與動機 11.3問題描述 31.4論文貢獻 41.5論文章節架構 4第二章 文獻回顧 52.1粒子群最佳化演算法 52.2估測基礎矩陣 7第三章 背景技術 103.1粒子群最佳化演算法流程 103.2隨機取樣 143.3最小平方法 153.4最小平方中值法 173.5估測基礎矩陣流程 18第四章 以粒子群最佳化演算法估測基礎矩陣 204.1模擬基礎實驗 204.1.1平面中粒子群最佳化 204.1.2圓錐近似中粒子群最佳化 244.2研究方法與流程 284.2.1系統架構 284.2.2評估基礎矩陣 31第五章 實驗結果 335.1平面中粒子群最佳化實驗結果 335.1.1速度未經過正規化 345.1.2速度經過正規化 465.1.3討論與分析 555.2圓錐近似中粒子群最佳化實驗結果 555.2.1限制固定疊代次數 555.2.2對Gbest收斂 575.2.3限制fitness值和對Gbest收斂 645.2.4討論與分析 645.3以粒子群最佳化估測基礎矩陣實驗結果 655.3.1第一組實驗(3DMAX)資料 655.3.2第二組實驗(LIDAR)資料 69 5.3.3討論與分析 73第六章 結論 746.1結論 746.2未來工作 75參考文獻 76 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0097753020 | en_US |
| dc.subject (關鍵詞) | 影像處理 | zh_TW |
| dc.subject (關鍵詞) | 基礎矩陣 | zh_TW |
| dc.subject (關鍵詞) | 粒子群最佳化 | zh_TW |
| dc.subject (關鍵詞) | 最小平方中值法 | zh_TW |
| dc.subject (關鍵詞) | Image processing | en_US |
| dc.subject (關鍵詞) | fundamental matrix | en_US |
| dc.subject (關鍵詞) | PSO | en_US |
| dc.subject (關鍵詞) | Least Median of Squares | en_US |
| dc.title (題名) | 粒子群最佳化演算法於估測基礎矩陣之應用 | zh_TW |
| dc.title (題名) | Particle swarm optimization algorithms for fundamental matrix estimation | en_US |
| dc.type (資料類型) | thesis | en |
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