Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/2920
DC FieldValueLanguage
dc.contributor李吉群zh_TW
dc.contributor.author陳瑩聰zh_TW
dc.contributor.authorChen, Ying-Tsungen_US
dc.contributor.other機械工程學系所zh_TW
dc.date2012en_US
dc.date.accessioned2014-06-05T11:44:19Z-
dc.date.available2014-06-05T11:44:19Z-
dc.identifierU0005-0708201214451300en_US
dc.identifier.citation[1] O. D. Faugeras , M. Herbert , E. Pauchon , “Segmentation of range image into planar and quadratic patches”, Proc. IEEE Conf. Computer vision and pattern Recognition, pp. 8-13, June 1983. [2] M. Oshima , Y. Shirai ,“Object recognition using three-dimensional information”, IEEE Trans. Pattern Anal. Machine Intell. , vol. PAMI-5 , pp.353-361, July 1983. [3] P. J. Besl , R. C. Jain, “Invariant surface characteristics for 3D object recognition in range image ” , Computer Vision Graphic and image processing , 33, pp. 33-80 , 1986. [4] P. J. Besl , R. C. Jain, “Segmentation through variable-order surface fitting” , Proc. IEEE Conf. Computer vision and Pattern Recognition ,vol. 10 ,No.2 , March 1988. [5] N. Yakoya , M. D. Levine, “Range Image Segmentation Based on Differential Geometry: A Hybrid Approach”, IEEE Trans. On Pattern Analysis And Machine Intelligence, vol. 11,No.6,June 1989. [6] C. Zhao , D. Zhao , Y. Chen , “Simplified Gaussian and Mean curvatures to range image segmentation ” , IEEE Pro. Of ICPR , 1996. [7] X. Jiang , H. Burke , “Edge detection in range image based on scan line approximation ” , Computer vision and image understanding , vol.73,No.2, pp. 183-199, February 1999. [8] A. D. Sappa , M. Devy , “Fast range image segmentation by an edge detection strategy” , Proc. IEEE Conf. 3-D Digital Imaging and Modeling, pp. 292-299 , 2001. [9] A.K. Jain , M.N. Murty , P.J. Flynn, “Data Clustering: A Review” , ACM Computing Surveys, Vol. 31, No. 3, September 1999. [10] E. Cohen , R. F. Riesenfeld , G . Elber , Geometric Modeling with Splines , A K Peter , pp. 341-432 , 2001. [11] P. J. Besl , R. C. Jain , “Segmentation Through Variable-Order Surface Fitting”, IEEE Trans. On Pattern Analysis And Machine Intelligence, vol. 10,No.2, pp. 167-192 , June 1988en_US
dc.identifier.urihttp://hdl.handle.net/11455/2920-
dc.description.abstract在影像視覺中,從一張含有三維資訊的影像中直接獲得有用的資訊有所困難,因此形貌影像分割(Range Image segmentation)為一個很重要的初步處裡,將原始影像中的物件表面分割為基本的幾何元件,幾何元件包含平面、球面、圓柱面以及圓錐面。一個機械元件往往含有許多的不同的幾何元件且若直接一次分析整張影像全部的像素點容易造成運算時間過長,因此在本文使用分層聚類的觀念,先將影像分割為小的區塊,再由這些區塊去找尋出各元件。 本文先使用高斯曲率與平均曲率進行表面型態標記,將影像初步分割為八種曲面型態,再從這些曲面型態中利用RANSAC演算法找出所有的表面元件,在比較所有的像素點與各元件的誤差進行像素點分類以達到影像分割的效果,文中將會對於使用高斯曲率與平均曲率進行表面型態標記之方法敘述外,也會對於如何使用RANSAC演算法自動找表面元件數量有所論述。zh_TW
dc.description.abstractIn computer vision, get a useful information directly from the one containing the image of the three-dimensional information has been difficult, the range image segmentation is a very important initial at the object surface in the original image is dividedbasic geometric elements and geometric elements with plane, spherical, cylindrical and conical surface. A mechanical components often contain many different geometric components and if the direct analysis to the image all the pixels likely to cause long computation time, so in this paper,we use of the hierarchical clustering , the first image is divided into small blocksby block to find the components. In this paper,the first step use sign the the Gaussian curvature and mean curvature of the surface morphology,than we use RANSAC algorithm to identify all the surface elements from the sign image.en_US
dc.description.tableofcontents摘要 i ABSTRAC ii 目錄 iii 圖目錄 vi 表目錄 vii 符號表 viii 第一章 緒論 1 1.1 前言 1 1.2 研究動機 2 1.3 研究方法 3 1.4 文獻回顧 4 第二章 形貌影像分割原理 5 2.1.1 形貌影像定義 5 2.1.2 曲率定義 6 2.1.3 表面曲率形態 7 2.1.4表面元件定義 9 2.3 分層聚類(HIERARCHICAL CLUSTERING) 13 2.4 演算法流程 14 第三章 演算法過程詳述 16 3.1 初步分割 18 3.1.1影像雜訊評估 18 3.1.2 影像平滑化 19 3.1.3 高斯曲率與平均曲率計算 21 3.2.1 表面元件描述 27 3.2.2 表面元件擬合 31 3.2.3 擬合結果測試 34 第四章 實驗結果與討論 37 第五章 結論與未來展望 47 參考文獻 49zh_TW
dc.language.isozh_TWen_US
dc.publisher機械工程學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0708201214451300en_US
dc.subject形貌影像zh_TW
dc.subjectrange imageen_US
dc.subject影像分割zh_TW
dc.subject表面型態標記zh_TW
dc.subjectRANSACzh_TW
dc.subject幾何元件擬合zh_TW
dc.subject分層聚類zh_TW
dc.subjectimage segmentationen_US
dc.subjectsurface curvatureen_US
dc.subjectRANSACen_US
dc.subject3-D object descriptionen_US
dc.subjecthierarchical clusteringen_US
dc.title運用聚類技術進行形貌影像分割zh_TW
dc.titleRange Image Segmentation Using Clustering Techniquesen_US
dc.typeThesis and Dissertationzh_TW
item.languageiso639-1zh_TW-
item.openairetypeThesis and Dissertation-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.fulltextno fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:機械工程學系所
Show simple item record
 
TAIR Related Article

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.