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研究生:楊子頡
研究生(外文):Yang, Tzu-Chieh
論文名稱:三維可能性C樣本面分群法及其在三維物件分割的應用
論文名稱(外文):Three-Dimensional Possibilistic C-Template Shell Clustering and its Application in 3D Object Segmentation
指導教授:王才沛
指導教授(外文):Wang, Tsai-Pei
學位類別:碩士
校院名稱:國立交通大學
系所名稱:多媒體工程研究所
學門:電算機學門
學類:軟體發展學類
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:31
中文關鍵詞:點雲三維霍夫變換連通元件標記法可能性C-means分群法
外文關鍵詞:point cloud3D Hough transformconnect-componentpossibilistic c-means clustering
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本論文研究目的在於在三維空間內以一個模型匹配出空間內相似的物件。此研究步驟包括四個主要的部份:1.使用Kinect感應器對實景進行3D建模 2.對建模資料進行物件分割切出獨立的物件 3.建立一個模型對每個獨立的物件進行匹配並得到最終結果。第一部分使用Kinect建立出一個點雲(point cloud),第二部分使用 3D Hough Transform找出點雲中的平面切除後,在使用連通物件標記(connected-component)進行物件分割,第三部分則是本論文重點,透過以面為基準的模型的分群法(Template-Based Shell Clustering),將模型與分割的點雲進行迭代匹配來找出相似的成果。在實驗結果中,可以看到物體除了可以精准的匹配外,有別以往兩群點與點互相搜索的耗時,更能提高其效益。
The purpose of this thesis is to use a model to match a similar object in three-dimensional space.This research includes four main parts: First, using the Kinect sensor to take the real world; second, splitting the point cloud into separate items; third, creating a model to match each individual item; lastly, getting the final result. The thesis includes descriptions on using Kinect to establish a point cloud, using 3D Hough Transform to find and remove the cloud points of planes, and using connected-component to separate individual objects. The focus of this thesis is on matching with individual item and manually created models through the Template-Based Shell Clustering that is the process of detecting clusters of particular geometrical shapes through clustering algorithms. In experimental results, we can see accurate matching results.
摘要…………………………………………………………………………………………… i
ABSTRACT………………………………………………………………………………… ii
誌謝…………………………………………………………………………………………iii
目錄………………………………………………………………………………………… iv
圖目錄……………………………………………………………………………………… v
第一章 緒論………………………………………………………………………………… 1
1.1前言…………………………………………………………………………………1
1.2研究目的…………………………………………………………………………1
1.3研究方法……………………………………………………………………………1
1.4論文結構……………………………………………………………………………2
第二章 文獻探討 ……………………………………………………………………………3
第三章 研究方法 ……………………………………………………………………………5
3.1 Kinect Fusion 建立 3D 模型…………………..……………………….…………6
3.2平面偵測 3D Hough Transform……………………………………………...………7
3.3連通元件標記法 connected-component……………………………………..………9
3.4空間中點與三角形最近距離………………………………………………….……10
3.5模糊與可能性分群法(Fuzzy and Possibllistic Clustering) ………………...………11
3.6基於模型的分群法 (Template-Based Shell Clustering) ……………….….………12
第四章 實驗過程與結果……………………………………………………………………20
第五章 未來展望 …………………………………………………………………..………29
參考文獻…………………………………………………..…………………………………30

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[15] [online] https://msdn.microsoft.com/en-us/library/dn188670.aspx
[16] Tsaipei Wang,” Possibilistic Shell Clustering of Template-Based Shapes”, IEEE Trans. Fuzzy Systems, vol. 17, no. 4, 2009.

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